Consumer Acceptance of Brown and White Rice Varieties

Consumer Acceptance of Brown and White Rice Varieties | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Original article Consumer Acceptance of Brown and White Rice Varieties Tanweer Aslam Gondal, Russell SJ Keast, Robert A Shellie, Snehal R Jadhav, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-526268/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2021 Read the published version in Foods → Version 1 posted You are reading this latest preprint version Abstract Rice is consumed as a staple food by more than half of the world’s population. Due to a higher fibre and micronutrient content, brown rice is more nutritious than white rice, but the consumption of brown rice is significantly lower than that of white rice, primarily due to sensory attributes. Therefore, the present research aimed to identify the sensory attributes which drive liking of Australian-grown brown and white rice varieties. Participants (n = 139) tasted and scored (9-point hedonic scale) their liking (i.e., overall liking, aroma, colour and texture) of brown and white rice types of Jasmine (Kyeema), Low GI (Doongara) and Medium grain rice (Amaroo). In addition, participants scored, aroma, colour, hardness, fluffiness, stickiness and chewiness, on Just About Right Scales. A within-subjects crossover design with randomised order (William’s Latin Square design) was used with six repeated samples for liking and Just About Right scales. Penalty analyses were applied to determine the relative influence of perception of sensory attributes on consumer liking of the rice varieties. Across all varieties, white rice was liked more than brown rice due to texture and colour, and Jasmine rice was preferred over Low GI and Medium Grain. Rice texture (hardness and chewiness) was the most important sensory attribute among all rice varieties and aroma was important for driving of liking between white rice varieties. Agronomy Brown rice white rice sensory consumer acceptance Just About Right scale JAR penalty analysis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Rice is consumed as a staple food by more than 4 billion people around the globe (Lee, Sreenivasulu, Hamilton, & Kohli, 2019; Sattari, Mahdinezhad, Fakheri, Noroozi, & Beheshtizadeh, 2015; Younas et al., 2020). Rice is a significant source of dietary nutrients such as carbohydrates, vitamins and minerals (Civáň, Craig, Cox, & Brown, 2015; Huang et al., 2012). For populations that rely on rice as a staple food, it delivers approximately 21% of the consumed energy and 15% of the consumed protein (Maclean, Dawe, Hardy, & Hettel, 2002). Australia produces high quality rice from different varieties, which are categorised as aromatic-Thai jasmine origin and non-aromatic rice (Chaudhary, Tran, & Duffy, 2001). Aromatic rice varieties have distinctive popcorn like flavour notes due to the presence of 2-acetyl-1-pyrroline (Bryant & Mcclung, 2011; R. G. Buttery, Ling, & Mon, 1986; Chaudhary et al., 2001; Daygon et al., 2017). Furthermore, rice can be classified based on the milling process. The milling of the whole grain results in brown rice, a further removal of bran and germ, results in white rice. (IRRI, 2013). Although white rice is more commonly consumed, brown rice is considered healthier due to nutritional components such as lipids, proteins, dietary fibre and polyphenols (Mir, Bosco, Shah, & Mir, 2016; Tikapunya, Henry, & Smyth, 2018). The sensory profile of rice is an important driver of consumer acceptance. Sensory attributes have a strong influence on product selection, consumption and purchase decisions (Combris, Bazoche, Giraud-Héraud, & Issanchou, 2009; Januszewska, Pieniak, & Verbeke, 2011). Sensory attributes such as physical appearance (i.e., uniformity, cleanliness, brightness, glossiness and translucency of the rice grain) (Tomlins, Manful, Larwer, & Hammond, 2005), taste (e.g., sweetness, bitterness) and aroma (e.g., floral notes) are drivers of liking (Li, Jervis, & Drake, 2015) that affect consumer acceptance of rice. Furthermore, rice texture (i.e., cohesiveness, softness) has been suggested to be of high importance for consumer acceptance of rice. A previous study reported that brown rice texture was less liked compared to white rice and there was variation in liking of the various textures of different brown rice varieties (Kwon & Ju, 2018). Along the same lines, Suwansri et al suggested that an increase in hardness of rice is associated with a lower consumer acceptability (Suwansri, Meullenet, Hankins, and Griffin, 2002). The importance of texture has also been emphasised by Maleki et al, who suggests that consumers can be segmented based on their preference for different rice textures (Maleki, Oliver, Lewin, Liem, & Keast, 2020). Within each rice variety, the milling process (e.g., white vs brown rice) alters the nutrient composition and sensory attributes (Shobana et al., 2011) For example, brown rice has a higher lipid content compared with white rice. The lipid context affects the sensory profile due to lipid oxidation in the bran layer of brown rice (Park, Kim, & Baek, 2010). Lipid oxidation leads to the development of off-flavours (Pingret, Fabiano-Tixier, & Chemat, 2013), which potentially impact consumer perception and acceptance. In short, differences in the acceptance of white and brown rice is likely caused by differences in sensory profiles, which are related to differences in nutrient composition (Sudha et al., 2013). In Australia 90% of rice is consumed as white rice, whereas only 10% is consumed as brown rice (Pollard et al., 2017), which is similar to global rice consumption patterns (Pollard et al., 2017; Selvam, Masilamani, Umashankar, & Albert, 2017).. To understand what drives the difference in consumption of brown and white rice it is important to investigate the sensorial differences of brown and white rice. The objective of this study is to identify the drivers of liking of Australian-grown brown and white rice varieties. It will provide important information for rice industry and breeding programmes for the development of new rice varieties to meet consumer needs. 2. Participants, Materials And Methods 2.1 Study design: A within-subjects crossover design with randomised order (William’s Latin Square design) for liking and Just About Right scales with six repeated samples was used in the present study. To determine the required participant sample size, G*power [Version 3.1.9.2, (Franz Faul) Germany 2014] was used. Based on six measurements (six rice samples) comparisons within subjects with alpha level 0.05, power of 0.8, and a small effect size (f=.10), the minimum sample size was 109. To account for potential dropouts, 140 participants from CASS Food Research Centre database were recruited. Participants were excluded if they had food allergies, dietary restrictions, and/or were pregnant or lactating. Participants were asked to refrain from eating, drinking or brushing their teeth one hour prior to testing. The rice consumer study was approved by the research ethics committee Deakin University (HEAG-H 29_2018). 2.2 Measurements: Participants were asked to complete two questionnaires concerning 1) demographics (age, gender, education, and marital status), and 2) rice consumption (type of rice (brown or white), number of times they eat rice daily, weekly or fortnightly, and awareness of brown rice health benefits). To assess the liking and sensory perception of the rice samples before and after tasting the rice samples, participants filled out 9-point hedonic scales (9= extremely like and 1= extremely dislike) ( Peryam & Pilgrim, 1957 ) for overall liking, aroma, colour and texture. In addition, participants completed Just About Right scales for aroma intensity, colour, hardness, fluffiness, stickiness and chewiness. A Just About Right scale, is a bipolar labelled attribute scale ( Pagès, Berthelo, Brossier, & Gourret, 2014 ), which has an anchored mid-point that corresponded to Just About Right for each attribute ( Cadot, Caillé, Samson, Barbeau, & Cheynier, 2010 ). The Just About Right scales provided the participants with 3 answer options per sensory attribute (1=not enough, 2= Just about Right, 3= too much) ( J.-E. Yang & Lee, 2020 ). 2.3 Materials: Three Australian rice varieties [Jasmine rice (Kyeema), Low GI (Doongara) and Medium grain (Amaroo) (Table 1)] with both brown and white rice types were sourced from Sunrice (Ricegrowers Ltd, Leeton, Australia) Australia. Rice samples were washed 2 to 3 times in cold running water until the water ran clear. Rice samples were cooked in dedicated rice cookers (“Grain Master” HD4514/72_ UM_ US_v1.0, Philips), to avoid cross flavour contamination, according to manufacturer’s instructions with specific water to rice ratios (Table 1). Rice samples and water quantities were measured by a measuring cup. Rice was cooked at quick rice cooking mode and kept warm in the rice cooker for no longer than the duration of the sensory test (approximately 45 minutes). 2.4 Testing procedure: Sensory testing took place in a sensory laboratory, which consisted of partitioned booths and a high-capacity air filtration system, of the CASS Food Research Centre, Deakin University Melbourne, Australia. On arrival participants were instructed to carefully read the Plain Language Statement and sign the consent form. Ten participants participated in each one-hour session. Rice samples were served to the participants in 30 mL serving cups that were labelled with three-digit unique codes. The rice samples were randomly presented one at a time directly from the rice cooker at a temperature of 55 ±3 o C. The participants were instructed to rinse their mouth with filtered water for five seconds and use crackers between tasting the different rice samples. The test consisted of two parts (i.e., before tasting, after tasting). In the first part, the participants received the following instruction: “do not eat the rice samples, only look, feel (e.g., hold the rice between your fingers) and smell the rice”. Next, participants were asked to rate overall liking and their liking for aroma and colour on a 9-point hedonic scale, and fluffiness, stickiness, hardness and aroma intensity on Just About Right Scales. In the second part, the participants were instructed to taste the rice samples (one by one) and rate on 9-point hedonic scales, their overall liking, and texture for each rice sample. In addition, participants rated their perceived intensity of flavour, fluffiness, hardness and chewiness on Just About Right scales. There was a one-minute break after the tasting of each sample to avoid tasting fatigue of the participants. The data was collected on computers using Compusense Software Academic Consortium (Compusense, Inc., Guelph, Ontario, Canada). Gift vouchers were served to each participant on completion of the rice consumer test. 2.5 Statistical Analysis: All rice consumer study data was exported from Compusense Cloud into Microsoft Excel version 1708 (Microsoft Corporation) for data cleaning. For the statistical analysis of liking, the program Stata/IC 15.0 (StataCorp LLC,4905 Lakeway Drive, College Station, Texas 77845, USA) was used. Descriptive statistics (mean, standard deviation and correlation coefficient) were calculated for overall liking scores and all sensory attributes. Box plots and scatter plots were extracted for overall liking and for other sensory attributes. Linear mixed model approach was used to analyse repeated measure Analysis of Variance (ANOVA) data to determine the effect of rice varieties (Jasmine, Low GI and medium grain rice samples) and rice types (brown, white) on overall liking, aroma, colour and texture linking. This approach accounts for within subjects autocorrelation via a random intercept in the model. The combined effect of rice varieties and types of rice was tested through a model that contained main effects of rice type (brown and white) and varieties (Jasmine, Low GI and Medium Grain) as well as the two-way interaction between varieties, and types of rice. The post-hoc pairwise comparison (Bonferroni adjusted) was conducted to identify the significant difference in sensory attributes among rice varieties and rice types. The descriptive statistics for Just About Right attributes, overall liking and penalty analysis (p<0.05) of brown and white rice from the three varieties was conducted in XLSTAT-Sensory version 2020.3 (Addinsoft, New York, NY, USA). The penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together ( J.-E. Yang & Lee, 2020 ). Spearman’s correlation coefficients were calculated. Mean drop plots were extracted to identify the effect of JAR attributes on overall liking of rice. The mean drops were plotted against the percentage of consumers. For penalty analysis and mean drop plots, 20% consumers were considered as threshold level for each JAR attribute ( Pagès et al., 2014 ). 3. Results 3.1 Participants The participants (n=140, female 52%, male 48%) from different age groups participated in the consumer study, one participant was excluded during data cleaning because of incomplete rice tasting session. The participants were rice consumers and mostly thought they were aware of the health benefits of brown rice. The demographics are shown in table 2. 3.2 Liking (9-point hedonic scale) of brown and white rice varieties before tasting: In the result section rice variety refers to the different varieties which were tested (i.e., Jasmine white, Jasmine brown, Low GI white, Low GI brown, Medium grain white and Medium grain brown) and rice type refers to brown and white rice. The results (Table 3) indicate that there was a main effect of rice varieties and their types (i.e., brown vs white) on overall liking before tasting the rice samples (p<0.05). However, there was no statistically significant interaction between rice variety (i.e., Jasmine, Low GI, Medium Grain) and rice type. That is, white rice was preferred over brown rice, regardless of the rice variety (p<0.05) (see Figure 1). Pairwise comparisons show that Jasmine white rice was more liked than any of the other rice varieties (p<0.05). While, liking of Low GI white and Med. grain was not statistically significantly different (represented with shared letter “C”). Likewise, no difference was observed between the overall liking of brown rice varieties. For aroma liking, there was a significant difference (Table 3) between rice varieties and their types (i.e., brown vs white) before tasting the rice samples. The differences in mean values of Low GI and Medium Grain were -0.6, 95% CI (-0.8, -0.5) and -0.7, 95% CI (-0.9, -0.5) respectively when compared with Jasmine rice (a reference sample). The interaction between rice variety (i.e., Jasmine, Low GI, Medium Grain) and rice type was also statistically significant. That is, the aroma of white rice was preferred over brown rice, regardless of the rice variety (p<0.05) (see Figure 1). Pairwise comparisons show that the aroma of Jasmine white rice was more liked than any of the other rice varieties (i.e., Jasmine white rice has the highest mean 7.0, 95% CI (6.9, 7.3) and Medium Grain brown has lowest mean 5.8, 95% CI (5.5, 6.1)). While, liking of Low GI white and Med. grain was not statistically significantly different (represented with shared letter “AB”). Similarly, no difference was observed between the aroma liking of Low GI and Med. Grain brown rice varieties. The rice varieties and their types (i.e., brown vs white) were significantly associated with colour liking before tasting the rice samples. The differences in mean values of Low GI and Medium Grain for colour liking were -0.4, 95% CI (-0.5, -0.2) and -0.1, 95% CI (-0.3, -0.04) respectively when compared with Jasmine rice (a reference sample). However, the interaction between rice variety (i.e., Jasmine, Low GI, Medium Grain) and rice type was not statistically significant. That is, the colour of white rice was liked more than the colour of brown rice, regardless of the rice variety (see Figure 1). Pairwise comparisons show that there was no difference in colour liking of Jasmine white and Med. Grain white rice (represented with shared letter “C”). Likewise, no difference was observed in colour liking of Jasmine brown, Low GI brown and Med. Grain brown (represented with shared letter “A”). 3.2.1. Liking (9-point hedonic scale) of brown and white rice varieties after tasting: The results show rice variety and rice type (i.e., brown and white) after tasting significantly affect liking (see Table 4). Jasmine rice was liked more than Low GI and Medium Grain rice. For all rice varieties white rice was preferred over brown rice (mean difference= 0.8, 95% CI (0.6, 1.1). The significant interaction between rice varieties and rice types (i.e., brown and white) shows that Jasmine white rice was liked more than any of the other brown and white rice varieties (see Figure 1). The pairwise comparisons show that no difference was observed between Low GI white rice, Med. Grain white rice and Jasmine brown rice in overall liking after tasting. There was significant correlation between rice variety and rice type on texture liking ( p <0.05) after tasting rice samples (Table 4). That is, the texture of Jasmine rice was liked more than the texture of Low GI and Medium Grain. The mean liking of Low GI and Medium Grain rice were reduced by -0.6, 95% CI (-0.8, -0.4) and -0.4, 95% CI (-0.7, -0.2) respectively when compared with Jasmine rice (a reference sample). Likewise, the texture of white rice was preferred over brown rice, regardless of rice varieties (mean difference=0.91, 95% CI (0.6, 1.2). The significant interaction between rice varieties and rice types also indicates that the texture of Jasmine white rice was liked more than the texture of any of the other brown and white rice varieties (see Figure 1). The pairwise comparisons show that no difference was observed between Low GI white rice, Med. Grain white rice and Jasmine brown rice in texture liking after tasting. However, the texture liking of brown rice varieties was not statistically different. 3.3. Just About Right attributes and Penalty Analysis 3.3.1. Penalty analysis of Jasmine brown and white rice before tasting: Penalty analysis shown in Table 5, indicates that the overall penalty is significant ( p <0.05) for all attributes of Jasmine brown rice. This means that the rice was not perceived at optimum level for all attributes tested. The Jasmine brown rice was rated as being too low in aroma, too dark in colour, too hard in texture, too low in fluffiness and/or too low in stickiness. Similarly, for Jasmine white rice, the overall penalty (Table 5) is not significant for any of the attributes. This means that across all tested attributes, a deviation from JAR did not have a significant influence on overall liking. The mean drop plot against consumers for each attribute of Jasmine brown and white rice shown in Figure 2 (A & B), which visually represents the results of the penalty analysis. 3.3.2. Penalty analysis of Low GI brown and white rice before tasting: The overall penalty analysis for Low GI brown rice is significant (p<0.05) for all attributes except “hardness” (p=0.18) (see table 6). This means that the hardness of Low GI brown rice was the only attribute which was rated as being optimal. The penalty analysis (Table 6) shown that the overall penalty for Low GI white rice is significant for fluffiness (p<0.05). This means that the rating of liking was significantly negatively influenced when participant rated Low GI white as low in fluffiness. Specific changes in liking due to suboptimal attributes are shown in Figure 3 (A & B) which visually represents the penalty analysis of Low GI rice. 3.3.3. Penalty analysis of Med. Grain brown and white rice before tasting: The results of the penalty analysis (Table 7) for Med. Grain brown rice show that the overall liking was significantly (p<0.05) influenced when majority of the participants considered aroma, colour and hardness are not at optimum level, the attributes were too high in aroma, too dark in colour and too hard in texture. Similarly, the overall penalty (Table 7) for Med. Grain white rice is significant for fluffiness ( p =0.02). That means, for fluffiness, the deviations from the Just about right level have significant impact on overall liking. The impact on liking of each attribute shown in Figure 4(A & B). 3.3.4 Penalty analysis of Jasmine brown and Jasmine white rice after tasting: Penalty analysis (Table 8) indicate that the overall liking was significantly (p<0.05) influenced when participants rated the Jasmine brown rice as not being ideal for flavour, fluffiness, hardness or chewiness. For Jasmine white rice, the overall penalty (Table 8) is only significant for hardness and not significant for all other attributes after rice tasting. This means that most of the participants considered Jasmine white rice “not hard enough” in texture. The mean drop plot against participants for each attribute by tasting of Jasmine brown and Jasmine white rice shown in Figure 5(A & B). 3.3.5 Penalty analysis of Low GI brown and Low GI white rice after tasting: The penalty analysis (Table 9) of Low GI brown rice by tasting shows that the overall liking was significantly (p<0.05) influenced when most of the participants judged that flavour, hardness and fluffiness were not optimal in Low GI brown rice. For Low GI white rice, the overall penalty (Table 9) is significant (p<0.05) for all attributes tested. This means, the overall liking was significantly influenced, when majority of the participants rated Low GI white rice as being not ideal for flavour, fluffiness, hardness or chewiness. The influence on liking of sensory attributes shown in Figure 6(A & B). 3.3.6 Penalty analysis of Med. Grain brown and white rice after tasting: For Med. Grain brown rice, the penalty analysis (Table 10) shows that the overall liking of Med. Grain brown rice was significantly ( p <0.05) influenced when participant rated flavour, fluffiness and hardness were not at optimum level. Similarly, the overall penalty of Med. Grain white rice is significant for flavour intensity, fluffiness and chewiness. This means that significant participants perceived Med. Grain white as too low in flavour and fluffiness, and too high in chewiness. The mean drop plots against participants for each attribute of Med. Grain brown rice shown in Figure 7(A & B). 4. Discussion This study aimed to identify the consumer liking, sensory attributes, and drivers of liking of brown and white rice varieties. The results suggest that, overall, participants liked Jasmine rice varieties more than Low GI and Medium grain rice varieties. This was also reflected in a higher liking of the aroma, colour and texture of Jasmine rice, compared to Low GI and Medium grain rice varieties. However, white rice is preferred over brown rice regardless of rice varieties. The present study suggests, in line with previous studies (Bunyasiri & Sirisupluxana, 2017; Sudha et al., 2013; Suwansri et al., 2002; Wangcharoen et al., 2016) that texture, colour and aroma are important drivers of consumer liking for rice. However, these drivers of liking do not seem to equally explain the differences in liking of white and brown rice. That is, differences in aroma mainly explain the difference in liking for white rice varieties and the aroma of Jasmine white rice was liked more than any of the other rice varieties. The most liked white rice (Jasmine rice), contains more of the compound 2-acetyle-1-pyrroline (Daygon et al., 2017) which is known to elicit a distinctive popcorn/pandan aroma (Bergman et al., 2000; Ron G Buttery, Ling, Juliano, & Turnbaugh, 1983; Jezussek, Juliano, & Schieberle, 2002; Lee et al., 2019) that has strong impact on consumer acceptance of rice (Tananuwong & Lertsiri, 2010). Whereas the other white rice (non-fragrant) varieties contain less 2AP (Mahattanatawee & Rouseff, 2014; D. S. Yang, Shewfelt, Lee, & Kays, 2008; Zeng, Zhang, & Zhang, 2009) that may have an impact on liking of non-fragrant white rice varieties. This is also reflected in the sensory data of the present study that aroma of Jasmine white rice is an important sensory attribute in predicting consumer liking and acceptance of white rice varieties. Therefore, the aroma of Jasmine white rice was preferred over all other white and brown rice varieties. In contrast to aroma being able to explain liking differences for white rice varieties, aroma does not fully explain differences in liking for brown rice. Differences between brown rice varieties can be explained by texture (hardness and chewiness). That is, brown rice is considered as too hard and chewy in texture which is driving the difference between brown rice varieties. Whereas Jasmine brown rice was preferred over Low GI and Med. grain brown rice. However, the results are in line with previous study conducted on ready to eat rice in Korea that the brown rice was scored less in overall acceptability due to being high in hardness, chewiness and yellowness (Kwon & Ju, 2018). Brown rice hardness in texture is associated with dietary fibre that is present in bran layer (Wang et al., 2011) whereas, in white rice, polishing removes bran and germ during rice processing (Liu, Zheng, & Chen, 2017). This significantly improves texture liking and consumer acceptance of white rice. However, the current study investigated consumer acceptance of brown and white rice with Western consumers using 9-Point hedonic scales, JAR scales and penalty analysis that was not reported by previous studies. This study also compared a range of brown and white rice varieties which enabled to compare brown and white rice, but also identify the drivers of liking between brown rice varieties as well as the drivers of liking within white. In addition, Therefore, it is interesting to note that rice texture (hardness) is more important for the consumer acceptance and overall liking of Australian brown rice varieties. This study suggests that the decrease in hardness and chewiness will increase the overall liking of Australian brown rice varieties, which can eventually increase brown rice acceptance and consumption. Brown rice texture (hardness and chewiness) and colour are the sensory attributes that are driving the difference between white and brown rice varieties. That is, the texture of brown rice is less liked as compare to white rice regardless of rice varieties, because the majority of participants rated brown rice varieties as too hard and too chewy. However, differences in texture seem to be more important when comparing liking between white and brown rice. This is in line with study conducted on consumer acceptance of parboiled brown and white rice reported that white rice was preferred to brown rice because of texture and colour (Sudha et al., 2013). The results are also in agreement with the study that reported consumer acceptance of white rice varieties in Thailand, the participants preferred cooked white rice because of the soft texture (Wangcharoen et al., 2016). Suwansri and Meullenet (2004) reported that Asian consumers preferred rice with white appearance (colour) and less sticky texture (Suwansri & Meullenet, 2004). Similarly, the consumers from South Asia and Middle East did not prefer brown rice texture (Suwannaporn, Linnemann, & Chaveesuk, 2008). In the present study, the sensory results also suggest that brown rice texture (hardness and chewiness) is the most important sensory attribute that is driving the liking and consumer acceptance of brown rice. This study has several strengths as this is the first study that focused on consumer acceptance of Australian brown and white rice varieties. In addition, sensory attributes for consumer acceptance were evaluated using 9-point hedonic scale, Just About Right scale and penalty analysis was also conducted in this study. However, there are some limitations that need to be addressed. The participants were mainly living in urban areas and were well educated, with 79% of participants holding undergraduate degree or higher. That may affect their liking because of their awareness to the brown and white rice varieties which may cause bias in evaluation of rice attributes. For future investigation, the sample (participants) could be recruited from different geographical areas to predict the preference of Australian brown and white rice varieties. Future studies are suggested with more focus on texture attributes of brown rice. To identify the variability in texture of brown rice, different cooking methods and water to rice ratios are recommended. In addition, the instrumental analysis (colour and texture analyser) can be considered for the better understanding of texture attributes of brown and white rice varieties. 5. Conclusion Texture is the most important sensory attribute which explains the difference in liking between brown and white rice. Whereas differences in aroma, best explain variation in liking of white rice. Therefore, the sensory attributes of Australian brown rice varieties can be improved by the development of new rice varieties which are soft in texture. Future research is needed to investigate if an increased water absorption, milling process, packaging and storage of brown rice can positively improve the texture and subsequently increase consumer acceptance. Abbreviations JAR: Just About Right IRRI: International Rice Research Institute CASS: Centre for Analytical Sensory and Safety ANOVA: Analysis of Variance XLSTAT: Name of Statistical Sensory Software CI: Confidence Interval Ref: Reference Declarations Ethics approval and consent to participate The rice consumer study was approved by the research ethics committee Deakin University (HEAG-H 29_2018). The participants were asked to read Plain Language Statement (PLS) and all participants signed their consent forms. Consent for publication The written consent for publication from all authors has been received. Availability of supporting data The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding support: This work was funded by Bahauddin Zakariya University Multan, Pakistan and Deakin University Melbourne, Australia. Author’s contributions Tanweer Aslam Gondal: Conceptualization, Methodology, Writing, and original draft preparation. Russell Keast: Conceptualization, Supervision, Reviewing and Editing. Robert A Shellie: Writing- Reviewing and Editing. Snehal R Jadhav: Supervision, Writing- Reviewing and Editing. Shirani Gamlath: Supervision, reviewing and editing. Mohammadreza Mohebbi: statistical analysis . Djin Gie Liem: Conceptualisation, Methodology, Supervision, Writing- Reviewing and Editing. References Bergman, C. J., Delgado, J. T., Bryant, R., Grimm, C., Cadwallader, K. R., & Webb, B. D. (2000). Rapid Gas Chromatographic Technique for Quantifying 2-Acetyl-1-Pyrroline and Hexanal in Rice (Oryza sativa, L.). Cereal Chem J, 77 . doi:10.1094/cchem.2000.77.4.454 Bryant, R. J., & Mcclung, A. M. (2011). Volatile profiles of aromatic and non-aromatic rice cultivars using SPME/GC –MS. Food Chem, 124 . doi:10.1016/j.foodchem.2010.06.061 Bunyasiri, I. N., & Sirisupluxana, P. (2017). Consumers behavior and rice attributes for Thai hommali rice in Sichuan province of China. The Business & Management Review, 8 (4), 329. Buttery, R. G., Ling, L. C., Juliano, B. O., & Turnbaugh, J. G. (1983). Cooked rice aroma and 2-acetyl-1-pyrroline. Journal of Agricultural and Food Chemistry, 31 (4), 823-826. Buttery, R. G., Ling, L. C., & Mon, T. R. (1986). Quantitative analysis of 2-acetyl-1-pyrroline in rice. J Agric Food Chem, 34 . doi:10.1021/jf00067a031 Cadot, Y., Caillé, S., Samson, A., Barbeau, G., & Cheynier, V. (2010). Sensory dimension of wine typicality related to a terroir by Quantitative Descriptive Analysis, Just About Right analysis and typicality assessment. Analytica Chimica Acta, 660 (1-2), 53-62. Chaudhary, R. C., Tran, D. V., & Duffy, R. (2001). Speciality rices of the world: breeding, production and marketing : Food and Agriculture Organization of the United Nations (FAO). Civáň, P., Craig, H., Cox, C. J., & Brown, T. A. (2015). Three geographically separate domestications of Asian rice. Nature plants, 1 (11), 1-5. Combris, P., Bazoche, P., Giraud-Héraud, E., & Issanchou, S. (2009). Food choices: What do we learn from combining sensory and economic experiments? Food Quality and Preference, 20 (8), 550-557. Daygon, V. D., Calingacion, M., Forster, L. C., Voss, J. J. D., Schwartz, B. D., Ovenden, B., . . . Fitzgerald, M. A. (2017). Metabolomics and genomics combine to unravel the pathway for the presence of fragrance in rice. Scientific Reports, 7 , 8767. doi:10.1038/s41598-017-07693-9 Huang, X., Kurata, N., Wang, Z.-X., Wang, A., Zhao, Q., Zhao, Y., . . . Guo, Y. (2012). A map of rice genome variation reveals the origin of cultivated rice. Nature, 490 (7421), 497-501. IRRI. (2013). International Rice Research Institute: World Rice Statistics 2013. Retrieved from http://irri.org/index.php?option Januszewska, R., Pieniak, Z., & Verbeke, W. (2011). Food choice questionnaire revisited in four countries. Does it still measure the same? Appetite, 57 (1), 94-98. Jezussek, M., Juliano, B. O., & Schieberle, P. (2002). Comparison of key aroma compounds in cooked brown rice varieties based on aroma extract dilution analyses. J Agric Food Chem, 50 . doi:10.1021/jf0108720 Kwon, Y.-S., & Ju, S.-y. (2018). Sensory evaluation of commercial ready-to-eat rice between trained panelist and consumer. British Food Journal . Lee, J.-S., Sreenivasulu, N., Hamilton, R. S., & Kohli, A. (2019). Brown Rice, a Diet Rich in Health Promoting Properties. Journal of nutritional science and vitaminology, 65 (Supplement), S26-S28. Li, X., Jervis, S., & Drake, M. (2015). Examining extrinsic factors that influence product acceptance: a review. Journal of Food Science, 80 (5). Liu, K.-l., Zheng, J.-b., & Chen, F.-s. (2017). Relationships between degree of milling and loss of Vitamin B, minerals, and change in amino acid composition of brown rice. LWT-Food Science and Technology, 82 , 429-436. Maclean, J., Dawe, D., Hardy, B., & Hettel, G. (2002). Rice almanac. 3rd edn. Wallingford, Oxon. In: CABI Publishing. Mahattanatawee, K., & Rouseff, R. L. (2014). Comparison of aroma active and sulfur volatiles in three fragrant rice cultivars using GC-Olfactometry and GC-PFPD. Food Chem, 154 . doi:10.1016/j.foodchem.2013.12.105 Maleki, C., Oliver, P., Lewin, S., Liem, G., & Keast, R. (2020). Preference mapping of different water‐to‐rice ratios in cooked aromatic white jasmine rice. Journal of Food Science, 85 (5), 1576-1585. Mir, S. A., Bosco, S. J. D., Shah, M. A., & Mir, M. M. (2016). Effect of puffing on physical and antioxidant properties of brown rice. Food Chemistry, 191 , 139-146. doi:10.1016/j.foodchem.2014.11.025 Pagès, J., Berthelo, S., Brossier, M., & Gourret, D. (2014). Statistical penalty analysis. Food Quality and Preference, 32 , 16-23. Park, J. S., Kim, K.-Y., & Baek, H. H. (2010). Potent aroma-active compounds of cooked Korean non-aromatic rice. Food science and biotechnology, 19 (5), 1403-1407. Peryam, D. R., & Pilgrim, F. J. (1957). Hedonic scale method of measuring food preferences. Food technology . Pingret, D., Fabiano-Tixier, A.-S., & Chemat, F. (2013). Degradation during application of ultrasound in food processing: A review. Food control, 31 (2), 593-606. Pollard, C. M., Pulker, C. E., Meng, X., Scott, J. A., Denham, F. C., Solah, V. A., & Kerr, D. A. (2017). Consumer attitudes and misperceptions associated with trends in self-reported cereal foods consumption: cross-sectional study of Western Australian adults, 1995 to 2012. BMC public health, 17 (1), 597. Rothman, L., & Parker, M. (2009). Just-about-right (JAR) scales. West Conshohocken, PA: ASTM International . Sattari, A., Mahdinezhad, N., Fakheri, B., Noroozi, M., & Beheshtizadeh, H. (2015). Improvement of the eating and cooking qualities of rice: a review. International Journal of Farming and Allied Sciences, 4 (2), 153-160. Selvam, S., Masilamani, P., Umashankar, P., & Albert, V. A. (2017). Opportunities and challenges in marketing of brown rice. In Brown rice (pp. 271-282): Springer. Shobana, S., Malleshi, N., Sudha, V., Spiegelman, D., Hong, B., Hu, F., . . . Mohan, V. (2011). Nutritional and sensory profile of two Indian rice varieties with different degrees of polishing. International journal of food sciences and nutrition, 62 (8), 800-810. Sudha, V., Spiegelman, D., Hong, B., Malik, V., Jones, C., Wedick, N. M., . . . Ponnalagu, M. M. (2013). Consumer Acceptance and Preference Study (CAPS) on brown and undermilled Indian rice varieties in Chennai, India. Journal of the American College of Nutrition, 32 (1), 50-57. Suwannaporn, P., Linnemann, A., & Chaveesuk, R. (2008). Consumer preference mapping for rice product concepts. British Food Journal . Suwansri, S., & Meullenet, J. (2004). Physicochemical characterization and consumer acceptance by Asian consumers of aromatic jasmine rice. Journal of Food Science, 69 (1), SNQ30-SNQ37. Suwansri, S., Meullenet, J. F., Hankins, J., & Griffin, K. (2002). Preference mapping of domestic/imported jasmine rice for US‐Asian consumers. Journal of Food Science, 67 (6), 2420-2431. Tananuwong, K., & Lertsiri, S. (2010). Changes in volatile aroma compounds of organic fragrant rice during storage under different conditions. J Sci Food Agric, 90 . doi:10.1002/jsfa.3976 Tikapunya, T., Henry, R. J., & Smyth, H. (2018). Evaluating the sensory properties of unpolished Australian wild rice. Food Research International, 103 , 406-414. doi:10.1016/j.foodres.2017.10.037 Tomlins, K., Manful, J., Larwer, P., & Hammond, L. (2005). Urban consumer preferences and sensory evaluation of locally produced and imported rice in West Africa. Food Quality and Preference, 16 (1), 79-89. Wang, K. M., Wu, J. G., Li, G., Zhang, D. P., Yang, Z. W., & Shi, C. H. (2011). Distribution of phytic acid and mineral elements in three indica rice (Oryza sativa L.) cultivars. Journal of Cereal Science, 54 (1), 116-121. doi: https://doi.org/10.1016/j.jcs.2011.03.002 Wangcharoen, W., Phanchaisri, C., Daengpok, W., Phuttawong, R., Hangsoongnern, T., & Phanchaisri, B. (2016). Consumer acceptance test and some related properties of selected KDML 105 rice mutants. Journal of food science and technology, 53 (9), 3550-3556. XLSTAT. (2021). Penalty analysis in Excel tutorial. Retrieved from https://www.xlstat.com/en/solutions/features/penalty-analysis Yang, D. S., Shewfelt, R. L., Lee, K. S., & Kays, S. J. (2008). Comparison of odor-active compounds from six distinctly different rice flavor types. J Agric Food Chem, 56 . doi:10.1021/jf072685t Yang, J.-E., & Lee, J. (2020). Consumer perception and liking, and sensory characteristics of blended teas. Food Science and Biotechnology, 29 (1), 63-74. Younas, A., Yousaf, Z., Riaz, N., Rashid, M., Razzaq, Z., Tanveer, M., & Huang, S. (2020). Role of Nanotechnology for Enhanced Rice Production. In R. S. Meena (Ed.), Nutrient Dynamics for Sustainable Crop Production (pp. 315-350). Singapore: Springer Singapore. Zeng, Z., Zhang, H., & Zhang, T. (2009). Analysis of flavor volatiles of glutinous rice during cooking by combined gas chromatography–mass spectrometry with modified headspace solid-phase microextraction method. J Food Compos Anal, 22 . doi:10.1016/j.jfca.2008.11.020 Tables Table 1: Selected Australian rice varieties. Rice Varieties Samples (Types) Water to Rice ratio Jasmine (Kyeema) Brown rice 2 : 1 Jasmine (Kyeema) White rice 1.5 : 1 Low GI (Doongara) Brown rice 2 : 1 Low GI (Doongara) White rice 1.5 : 1 Medium grain (Amaroo) Brown rice 2 : 1 Medium grain (Amaroo) White rice 1.5 : 1 Table 2: Demographics of the participants for rice tasting study. Demographics Participants Age groups Age 18-30 years 56% Age 31-45 years 28% Age 46 years and above 16% Rice consumers Brown rice 10% White rice 37% Brown and white rice 53% Brown rice health benefits _ perceived knowledge Aware enough 67% Not aware enough 33% Education High school certificate/ Diploma 21% Bachelor and above 79% Table 3: Linear mix model (repeated measures) ANOVA table for brown and white rice varieties before tasting Overall liking Aroma liking Colour liking Mean (95% CI) Mean diff. (95 % CI) Chi-square (df) p value Mean (95% CI) Mean diff. (95 % CI) Chi-square (df) p value Mean (95% CI) Mean diff. (95 % CI) Chi-square (df) p value Rice variety Jasmine 6.6 (6.4, 6.8) Ref. 26.50 (2) P < 0.0001 6.7 (6.5, 6.9) Ref. 53.84 (2) P ≤ 0.0001 6.6 (6.4, 6.8) Ref. 20.08 (2) P ≤ 0.0001 Low GI 6.2 (6.0, 6.4) -0.5 (-0.7, -0.3)** 6.1 (5.9, 6.3) -0.6 (-0.8, -0.5)** 6.2 (6.1, 6.5) -0.4 (-0.5, -0.2)** Med. Grain 6.3 (6.1, 6.5) -0.3 (-0.5, -0.1)** 6.1 (5.9, 6.3) -0.7 (-0.9, -0.5)** 6.5 (6.3, 6.7) -0.1 (-0.3, -0.04) Rice type Brown 5.9 (5.6, 6.2) Ref. 45.50 (1) P < 0.0001 6.1 (5.8, 6.3) Ref. 13.93 (1) P ≤ 0.0002 5.9 (5.6, 6.2) Ref. 50.85 (1) P ≤ 0.0001 White 6.9 (6.7, 7.0) 1.0 (0.7, 1.2)** 6.5 (6.3, 6.7) 0.5 (0.2, 0.7)** 7.0 (6.9, 7.3) 1.2 (0.9, 1.5)** Pairwise comparisons of variety and rice type (Bonferroni groups) Overall liking by variety and rice type interaction Pairwise comparisons of variety and rice type (Bonferroni groups) Overall liking by variety and rice type interaction Pairwise comparisons of variety and rice type (Bonferroni groups) Overall liking by variety and rice type interaction Jasmine White 7.2 (6.9, 7.4) D 1.69 7.0 (6.9, 7.3) D 6.94 7.3 (7.1, 7.5) C*** 1.21 Low GI White 6.7 (6.5, 6.9) C*** (2) P= 0.43 6.2 (5.9, 6.4) AB*** (2) P =0.03 6.9 (6.7, 7.1) B (2) P =0.55 Med. Grain White 6.7 (6.5, 7.0) C 6.3 (6.1, 6.6) AB 7.0 (6.9, 7.2) BC Jasmine Brown 6.1 (5.8, 6.4) B 6.4 (6.1, 6.7) B 6.0 (5.7, 6.3) A Low GI Brown 5.7 (5.3, 6.0) A 6.0 (5.7, 6.3) AB 6.0 (5.7, 6.3) AB Med. Grain Brown 5.9 (5.6, 6.2) AB 5.8 (5.5, 6.1) A 5.8 (5.5, 6.1) A * p 0.001 (p <0.05), ** p 0.000 (p <0.001), *** The pairs that share alphabet letter are not statistically significantly different Table 4: Mix model (repeated measures) ANOVA table for brown and white rice varieties after tasting Overall liking Texture liking Mean (95% CI) Mean diff. (95 % CI) Chi-square (df) p value Mean (95% CI) Mean diff. (95 % CI) Chi-square (df) p value Rice variety Jasmine 6.5 (6.3, 6.8) Ref. 47.59 (2) P ≤ 0.0001 6.3 (6.1, 6.6) Ref. 25.67 (2) P ≤ 0.0001 Low GI 5.9 (5.6, 6.1) -0.7 (-0.9, -0.5)** 5.7 (5.5, 6.1) -0.6 (-0.8, -0.4)** Med. Grain 6.0 (5.8, 6.2) -0.6 (-0.8, -0.4)** 5.9 (5.6, 6.1) -0.4 (-0.7,-0.2)* Rice type Brown 5.7 (5.4, 6.0) Ref. 37.45 (1) P ≤ 0.0001 5.5 (5.2, 5.8) Ref. 35.25 (1) P ≤ 0.0001 White 6.6 (6.4, 6.8) 0.8 (0.6, 1.1)** 6.4 (6.2, 6.6) 0.91 (0.6, 1.2)** Pairwise comparisons of variety and rice type (Bonferroni groups) Overall liking by variety and rice type interaction Pairwise comparisons of variety and rice type (Bonferroni groups) Overall liking by variety and rice type interaction Jasmine White 7.0 (6.7, 7.3) D 0.28 6.8 (6.5, 7.1) D 1.11 Low GI White 6.3 (6.0, 6.5) B (2) P =0.87 6.2 (5.9, 6.5) C (2) P =0.57 Med. Grain White 6.4 (6.2, 6.7) B 6.3 (6.0, 6.6) C Jasmin Brown 6.1 (5.8, 6.4) B 5.8 (5.5, 6.1) BC Low GI Brown 5.5 (5.2, 5.8) A 5.3 (5.0, 5.6) A Med. Grain Brown 5.5 (5.2, 5.9) A 5.5 (5.1, 5.8) AB *p 0.001 (p <0.05), ** p 0.000 (p <0.001), *** The pairs that share alphabet letter are non-significant Table 5. The Penalty analysis and JAR variables (before tasting) for Jasmine brown and white rice Rice Attribute Sensory test Correlation coefficient a Level Selection% b Mean c Mean drop d Penalty e Jasmine Brown Aroma Smell 0.12 Too low 17.99 5.28 1.25 JAR 53.24 6.53 0.85** Too high 28.78 5.93 0.60 Colour Visual -0.35 Too light 10.79 5.87 1.0 JAR 51.08 6.87 1.52* Too dark 38.13 5.21 1.67 Hardness Handling -0.22 Not hard enough 13.67 6.0 0.62 JAR 52.52 6.62 1.03* Too hard 33.81 5.43 1.19 Fluffiness Handling 0.16 Too low 35.97 5.70 0.81 JAR 48.20 6.50 0.73** Too much 15.83 5.96 0.55 Stickiness Handling 0.01 Too low 13.67 5.0 1.57 JAR 49.64 6.57 0.87 ** Too much 36.69 5.96 0.60 Jasmine White Aroma Smell 0.02 Too low 20.86 7.24 -0.05 JAR 48.92 7.19 0.08 Too high 30.22 7.02 0.17 Colour Visual -0.09 Too light 17.27 7.25 -0.06 JAR 70.50 7.19 0.15 Too dark 12.23 6.77 0.43 Hardness Handling -0.12 Not hard enough 28.06 7.41 -0.27 JAR 61.87 7.14 -0.30 Too hard 10.07 6.50 0.64 Fluffiness Handling 0.13 Too low 17.99 7.08 -0.05 JAR 65.47 7.03 -0.34 Too much 16.55 7.70 -0.66 Stickiness Handling -0.04 Too low 2.88 8.0 -0.72 JAR 41.01 7.28 0.22 Too much 56.12 7.01 0.27 a The impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman’s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (“low” or “high”) on overall liking for rice samples. When the correlation is positive, the “too little” has a bigger impact than the “too much”, and vice-versa for the negative correlations. If correlation is “0” for a JAR attribute then that attribute would have a strong impact on overall liking ( XLSTAT, 2021 ) e Penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together * p≤0.001 , ** p≤0.05 Table 6. The Penalty analysis and JAR variables (before tasting) for Low GI brown and white rice Rice Variable Sensory test Correlation coefficient a Level Selection% b Mean c Mean drop d Penalty e Low GI Brown Aroma Smell -0.15 Too low 24.46 5.79 0.22 JAR 43.88 6.02 0.63** Too high 31.65 5.07 0.95 Colour Visual -0.38 Too light 15.83 5.68 0.87 JAR 46.04 6.55 1.64* Too dark 38.13 4.59 1.96 Hardness Handling -0.18 Not hard enough 12.23 5.94 -0.04 JAR 44.60 5.90 0.44 Too hard 43.17 5.33 0.57 Fluffiness Handling 0.14 Too low 41.01 5.33 0.68 JAR 43.88 6.01 0.63** Too much 15.11 5.52 0.49 Stickiness Handling 0.16 Too low 28.06 4.90 1.22 JAR 48.92 6.12 0.89** Too much 23.02 5.63 0.49 Low GI White Aroma Smell 0.06 Too low 31.65 6.66 -0.09 JAR 46.76 6.57 -0.22 Too high 21.58 6.97 -0.40 Colour Visual 0.08 Too light 14.39 6.25 0.53 JAR 73.38 6.78 0.34 Too dark 12.23 6.65 0.13 Hardness Handling 0.02 Not hard enough 12.95 6.78 -0.11 JAR 61.15 6.67 -0.03 Too hard 25.90 6.67 0.004 Fluffiness Handling 0.17 Too low 18.71 6.0 0.88 JAR 65.47 6.88 0.57** Too much 15.83 6.68 0.20 Stickiness Handling 0.06 Too low 19.42 6.37 0.41 JAR 61.15 6.78 0.24 Too much 19.42 6.70 0.07 a The impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman’s correlation coefficient with a significance level a= 0.05. The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (“low” or “high”) on overall liking for rice samples. When the correlation is positive, the “too little” has a bigger impact than the “too much”, and vice-versa for the negative correlations. If correlation is “0” for a JAR attribute then that attribute would have a strong impact on overall liking ( XLSTAT, 2021 ) e Penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together * p≤0.001 , ** p≤0.05 Table 7. The Penalty analysis and JAR variables (before tasting) for Med. grain brown and Med. grain white rice Rice Variable Sensory test Correlation coefficient a Level Selection% b Mean c Mean drop d Penalty e Med. Grain Brown Aroma Smell -0.11 Too low 22.30 5.65 0.85 JAR 39.57 6.49 0.96** Too high 38.13 5.47 1.02 Colour Visual -0.44 Too light 8.63 5.83 1.02 JAR 48.92 6.85 1.84* Too dark 42.45 4.85 2.01 Hardness Handling -0.39 Not hard enough 4.32 6.0 0.70 JAR 41.01 6.70 1.34* Too hard 54.68 5.32 1.39 Fluffiness Handling 0.12 Too low 33.09 5.61 0.59 JAR 44.60 6.19 0.51 Too much 22.30 5.81 0.39 Stickiness Handling 0.07 Too low 24.46 5.21 1.18 JAR 50.36 6.39 0.95* Too much 25.18 5.66 0.73 Med. Grain White Aroma Smell 0.04 Too low 35.97 6.58 0.28 JAR 39.57 6.86 0.21 Too high 24.46 6.74 0.12 Colour Visual 0.05 Too light 14.39 6.50 0.28 JAR 72.66 6.78 0.20 Too dark 12.95 6.67 0.12 Hardness Handling 0.14 Not hard enough 24.46 6.44 0.30 JAR 57.55 6.74 0.26 Too hard 17.99 7.08 -0.34 Fluffiness Handling 0.08 Too low 23.02 6.34 0.62 JAR 54.68 6.96 0.52** Too much 22.30 6.55 0.41 Stickiness Handling -0.13 Too low 7.91 6.64 0.36 JAR 37.41 7.0 0.44 Too much 54.68 6.55 0.45 a The impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman’s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (“low” or “high”) on overall liking for rice samples. When the correlation is positive, the “too little” has a bigger impact than the “too much”, and vice-versa for the negative correlations. If correlation is “0” for a JAR attribute then that attribute would have a strong impact on overall liking ( XLSTAT, 2021 ) e Penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together * p≤0.001 , ** p≤0.05 Table 8. The Penalty analysis and JAR variables (after tasting) for Jasmine brown and Jasmine white rice Rice Variable Correlation coefficient a Level Selection% b Mean c Mean drop d Penalty e Jasmine Brown Flavour 0.02 Too low 25.90 5.83 0.62 JAR 53.96 6.45 0.72** Too high 20.14 5.61 0.85 Fluffiness 0.27 Too low 30.22 5.17 1.45 JAR 56.83 6.62 1.15* Too much 12.95 6.17 0.45 Hardness -0.26 Not hard enough 8.63 5.25 1.59 JAR 54.68 6.84 1.59* Too hard 36.69 5.26 1.59 Chewiness -0.20 Too low 8.63 5.67 1.06 JAR 46.76 6.73 1.13* Too much 44.60 5.58 1.14 Jasmine White Flavour 0.20 Too low 30.22 6.69 0.42 JAR 52.52 7.11 0.23 Too high 17.27 7.21 -0.10 Fluffiness 0.25 Too low 21.58 6.13 1.04 JAR 58.99 7.17 0.42 Too much 19.42 7.44 -0.27 Hardness 0.27 Not hard enough 24.46 6.00 1.37 JAR 69.06 7.37 1.18* Too hard 6.47 6.89 0.48 Chewiness -0.03 Too low 18.71 6.81 0.34 JAR 58.99 7.15 0.36 Too much 22.30 6.77 0.37 a The impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman’s correlation coefficient with a significance level a= 0.05. The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (“low” or “high”) on overall liking for rice samples. When the correlation is positive, the “too little” has a bigger impact than the “too much”, and vice-versa for the negative correlations. If correlation is “0” for a JAR attribute then that attribute would have a strong impact on overall liking ( XLSTAT, 2021 ) e Penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together * p≤0.001 , ** p≤0.05 Table 9. The Penalty analysis and JAR variables (after tasting) for Low GI brown and Low GI white rice Rice Variable Correlation coefficient a Level Selection% b Mean c Mean drop d Penalty e Low GI Brown Flavour -0.16 Too low 33.81 5.55 0.50 JAR 39.57 6.06 0.96** Too high 26.62 4.51 1.54 Fluffiness 0.19 Too low 35.97 5.0 0.83 JAR 51.80 5.83 0.74** Too much 12.23 5.35 0.48 Hardness -0.33 Not hard enough 5.76 5.38 0.81 JAR 35.25 6.18 1.10* Too hard 58.99 5.06 1.12 Chewiness 0.02 Too low 12.95 5.39 0.01 JAR 34.53 5.40 -0.12 Too much 52.52 5.55 -0.15 Low GI White Flavour 0.21 Too low 44.60 5.82 0.92 JAR 44.60 6.74 0.89* Too high 10.79 6.0 0.74 Fluffiness 0.12 Too low 20.86 5.31 1.39 JAR 64.03 6.70 1.24* Too much 15.11 5.67 1.03 Hardness -0.11 Not hard enough 15.83 5.68 0.97 JAR 60.43 6.66 1.02* Too hard 23.74 5.61 1.05 Chewiness 0.04 Too low 23.02 5.94 0.56 JAR 59.71 6.49 0.60** Too much 17.27 5.83 0.66 a The impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman’s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (“low” or “high”) on overall liking for rice samples. When the correlation is positive, the “too little” has a bigger impact than the “too much”, and vice-versa for the negative correlations. If correlation is “0” for a JAR attribute then that attribute would have a strong impact on overall liking ( XLSTAT, 2021 ) * p≤0.001 , ** p≤0.05 Table 10. The Penalty analysis and JAR variables (after tasting) for Med. Grain brown and white rice Rice Variable Correlation coefficient a Level Selection% b Mean c Mean drop d Penalty e Med. Grain Brown Flavour -0.11 Too low 25.18 5.23 1.02 JAR 46.76 6.25 1.31* Too high 28.06 4.67 1.58 Fluffiness 0.34 Too low 33.09 4.35 1.89 JAR 53.24 6.24 1.49* Too much 13.67 5.74 0.50 Hardness -0.52 Not hard enough 5.04 6.71 0.05 JAR 38.85 6.76 1.98* Too hard 56.12 4.60 2.16 Chewiness -0.07 Too low 12.95 6.06 -0.62 JAR 34.53 5.44 -0.17 Too much 52.52 5.49 -0.05 Med. Grain White Flavour 0.22 Too low 48.20 6.06 0.75 JAR 41.01 6.81 0.65** Too high 10.79 6.60 0.21 Fluffiness 0.13 Too low 23.02 5.75 0.99 JAR 58.99 6.74 0.78** Too much 17.99 6.24 0.50 Hardness 0.11 Not hard enough 5.04 6.29 -0.03 JAR 38.85 6.26 -0.27 Too hard 56.12 6.55 -0.29 Chewiness -0.07 Too low 16.55 6.17 0.53 JAR 53.96 6.71 0.61** Too much 29.50 6.05 0.66 a The impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman’s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (“low” or “high”) on overall liking for rice samples. When the correlation is positive, the “too little” has a bigger impact than the “too much”, and vice-versa for the negative correlations. If correlation is “0” for a JAR attribute then that attribute would have a strong impact on overall liking ( XLSTAT, 2021 ) e Penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together * p≤0.001 , ** p≤0.05 Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2021 Read the published version in Foods → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-526268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original article","associatedPublications":[],"authors":[{"id":27746032,"identity":"e0881140-bdc3-47f7-a219-3de181c3ba83","order_by":0,"name":"Tanweer Aslam Gondal","email":"","orcid":"https://orcid.org/0000-0002-3814-5227","institution":"Deakin University - Melbourne Burwood Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tanweer","middleName":"Aslam","lastName":"Gondal","suffix":""},{"id":27746033,"identity":"9776f4de-efde-44bb-87c1-dba9d7713a44","order_by":1,"name":"Russell SJ Keast","email":"","orcid":"","institution":"Deakin University - Melbourne Burwood Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Russell","middleName":"SJ","lastName":"Keast","suffix":""},{"id":27746034,"identity":"b27ecdec-a350-48c8-b57e-3c745c1dde7d","order_by":2,"name":"Robert A Shellie","email":"","orcid":"","institution":"Deakin University - Melbourne Burwood Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Robert","middleName":"A","lastName":"Shellie","suffix":""},{"id":27746035,"identity":"eef333e5-ab49-4338-b4b0-7e17f44cd71a","order_by":3,"name":"Snehal R Jadhav","email":"","orcid":"","institution":"Deakin University - Melbourne Burwood Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Snehal","middleName":"R","lastName":"Jadhav","suffix":""},{"id":27746036,"identity":"9d6d0742-50bb-4e42-9832-9dfe414180d5","order_by":4,"name":"Shirani Gamlath","email":"","orcid":"","institution":"Deakin University - Melbourne Burwood Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shirani","middleName":"","lastName":"Gamlath","suffix":""},{"id":27746037,"identity":"f430e8be-2771-4ba1-bbeb-e6e66cc5ece1","order_by":5,"name":"Mohammadreza Mohebbi","email":"","orcid":"","institution":"Deakin University - Melbourne Burwood Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Mohebbi","suffix":""},{"id":27746038,"identity":"24112c07-c8da-4003-937b-c9926b184f5d","order_by":6,"name":"Djin Gie Liem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACZiB+YMDAwM+QwMDYYAASSiBCSwJQpWQD0VpgagwOgLTAuXiAvDvzAYaEApto4+PJBz/OKLjDwM+eY8Dwsw23FsPDbAlAh6XlbjvzLFlyg8EzBsmeNwaMvfi0NPMYALUczt12I8dA8oHBYQYDIIOBF68W/g9ALf9zN8/I//wTpMUeqIXxLx4t8sw8oBA7kLtBIocN6DCgLRI5Bsz4bDFgZgOGlUFy7owzz8wsZxgc5pE486zgsMw5PLb0H3744MMfu9z+9uTHN3v+HJbjb0/e+PBNGR5bDjAwHEAW4AERB7CoRNjSgE92FIyCUTAKRgEIAADzD1SYsxfAmwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6619-6101","institution":"Deakin University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Djin","middleName":"Gie","lastName":"Liem","suffix":""}],"badges":[],"createdAt":"2021-05-14 22:41:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-526268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-526268/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/foods10081950","type":"published","date":"2021-08-22T14:02:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9397430,"identity":"3eb6985f-2a9d-4da4-8da7-1d30eb17ef1e","added_by":"auto","created_at":"2021-05-20 16:47:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48461,"visible":true,"origin":"","legend":"Mean liking (9-point hedonic scale) of sensory attributes for rice varieties \n*Shared letters within attribute are not statistically significantly different\n*9-point hedonic scale:1- Extremely Dislike, 9- Extremely like\n","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/320a88787ea9a7ebeb89260c.png"},{"id":9397926,"identity":"addde241-3d6b-4278-84ae-320798989234","added_by":"auto","created_at":"2021-05-20 16:50:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37992,"visible":true,"origin":"","legend":"Mean drop plots for Jasmine rice variety before tasting A) Jasmine Brown, and B) Jasmine White \n* Mean drop is the decrease in liking compared to the mean liking of those who rated the attribute as JAR\n**Consumer % are the consumers which judged an attribute as not ideal (Just About Right). The attributes with large percentages of consumers and penalties are in top right quadrant of the plot, which illustrate the critical points of the product (Rothman \u0026 Parker, 2009).\n","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/78dcc345b9125df7d89637cf.png"},{"id":9397410,"identity":"25887f1d-a151-49a8-b5c5-4919a3320d04","added_by":"auto","created_at":"2021-05-20 16:47:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44586,"visible":true,"origin":"","legend":"Mean drop plots for Low GI rice variety before tasting A) Low GI Brown, and B) Low GI White \n* Mean drop is the decrease in liking compared to the mean liking of those who rated the attribute as JAR\n**Consumer % are the consumers which judged an attribute as not ideal (Just About Right). The attributes with large percentages of consumers and penalties are in top right quadrant of the plot, which illustrate the critical points of the product (Rothman \u0026 Parker, 2009).\n","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/e699e63a67f6f2bf855fed04.png"},{"id":9397428,"identity":"ebd22fbb-c98e-43f5-b43b-9d1d7fb190f8","added_by":"auto","created_at":"2021-05-20 16:47:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43085,"visible":true,"origin":"","legend":"Mean drop plots for Med. Grain rice variety before tasting A) Med. Grain Brown, and B) Med. Grain White \n* Mean drop is the decrease in liking compared to the mean liking of those who rated the attribute as JAR\n**Consumer % are the consumers which judged an attribute as not ideal (Just About Right). The attributes with large percentages of consumers and penalties are in top right quadrant of the plot, which illustrate the critical points of the product (Rothman \u0026 Parker, 2009).\n","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/22cae81cedb525e5143a6892.png"},{"id":9397412,"identity":"d7d6a71b-4441-46bb-96e4-5a615b605e20","added_by":"auto","created_at":"2021-05-20 16:47:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44379,"visible":true,"origin":"","legend":"Mean drop plots for Jasmine rice variety after tasting A) Jasmine Brown, and B) Jasmine White \n* Mean drop is the decrease in liking compared to the mean liking of those who rated the attribute as JAR\n**Consumer % are the consumers which judged an attribute as not ideal (Just About Right). The attributes with large percentages of consumers and penalties are in top right quadrant of the plot, which illustrate the critical points of the product (Rothman \u0026 Parker, 2009).\n","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/4753511a413a0b26acb55829.png"},{"id":9397411,"identity":"4ccf52cc-3336-4786-8239-f23a9095cb6d","added_by":"auto","created_at":"2021-05-20 16:47:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38355,"visible":true,"origin":"","legend":"Mean drop plots for Low GI rice variety after tasting A) Low GI Brown, and B) Low GI White \n* Mean drop is the decrease in liking compared to the mean liking of those who rated the attribute as JAR\n**Consumer % are the consumers which judged an attribute as not ideal (Just About Right). The attributes with large percentages of consumers and penalties are in top right quadrant of the plot, which illustrate the critical points of the product (Rothman \u0026 Parker, 2009).\n","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/ba0dc9946034bef05422c3db.png"},{"id":9397409,"identity":"da746895-8952-49be-a1ab-89c169faad5c","added_by":"auto","created_at":"2021-05-20 16:47:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":40772,"visible":true,"origin":"","legend":"Mean drop plots for Med. Grain rice variety after tasting A) Med. Grain Brown, and B) Med. Grain White \n* Mean drop is the decrease in liking compared to the mean liking of those who rated the attribute as JAR\n**Consumer % are the consumers which judged an attribute as not ideal (Just About Right). The attributes with large percentages of consumers and penalties are in top right quadrant of the plot, which illustrate the critical points of the product (Rothman \u0026 Parker, 2009).","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/94ca42e53e9be7a23f40a61d.png"},{"id":60508401,"identity":"89b64d08-46c9-4701-b045-65dd6426e3b3","added_by":"auto","created_at":"2024-07-17 14:02:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2121774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-526268/v1/2bda9281-11b7-47ff-a7d3-b32ffadc2bc1.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eConsumer Acceptance of Brown and White Rice Varieties\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eRice is consumed as a staple food by more than 4\u0026nbsp;billion people around the globe (Lee, Sreenivasulu, Hamilton, \u0026amp; Kohli, 2019; Sattari, Mahdinezhad, Fakheri, Noroozi, \u0026amp; Beheshtizadeh, 2015; Younas et al., 2020). Rice is a significant source of dietary nutrients such as carbohydrates, vitamins and minerals (Civ\u0026aacute;ň, Craig, Cox, \u0026amp; Brown, 2015; Huang et al., 2012). For populations that rely on rice as a staple food, it delivers approximately 21% of the consumed energy and 15% of the consumed protein (Maclean, Dawe, Hardy, \u0026amp; Hettel, 2002).\u003c/p\u003e \u003cp\u003eAustralia produces high quality rice from different varieties, which are categorised as aromatic-Thai jasmine origin and non-aromatic rice (Chaudhary, Tran, \u0026amp; Duffy, 2001). Aromatic rice varieties have distinctive popcorn like flavour notes due to the presence of 2-acetyl-1-pyrroline (Bryant \u0026amp; Mcclung, 2011; R. G. Buttery, Ling, \u0026amp; Mon, 1986; Chaudhary et al., 2001; Daygon et al., 2017). Furthermore, rice can be classified based on the milling process. The milling of the whole grain results in brown rice, a further removal of bran and germ, results in white rice. (IRRI, 2013). Although white rice is more commonly consumed, brown rice is considered healthier due to nutritional components such as lipids, proteins, dietary fibre and polyphenols (Mir, Bosco, Shah, \u0026amp; Mir, 2016; Tikapunya, Henry, \u0026amp; Smyth, 2018).\u003c/p\u003e \u003cp\u003eThe sensory profile of rice is an important driver of consumer acceptance. Sensory attributes have a strong influence on product selection, consumption and purchase decisions (Combris, Bazoche, Giraud-H\u0026eacute;raud, \u0026amp; Issanchou, 2009; Januszewska, Pieniak, \u0026amp; Verbeke, 2011). Sensory attributes such as physical appearance (i.e., uniformity, cleanliness, brightness, glossiness and translucency of the rice grain) (Tomlins, Manful, Larwer, \u0026amp; Hammond, 2005), taste (e.g., sweetness, bitterness) and aroma (e.g., floral notes) are drivers of liking (Li, Jervis, \u0026amp; Drake, 2015) that affect consumer acceptance of rice.\u003c/p\u003e \u003cp\u003eFurthermore, rice texture (i.e., cohesiveness, softness) has been suggested to be of high importance for consumer acceptance of rice. A previous study reported that brown rice texture was less liked compared to white rice and there was variation in liking of the various textures of different brown rice varieties (Kwon \u0026amp; Ju, 2018). Along the same lines, Suwansri et al suggested that an increase in hardness of rice is associated with a lower consumer acceptability (Suwansri, Meullenet, Hankins, and Griffin, 2002). The importance of texture has also been emphasised by Maleki et al, who suggests that consumers can be segmented based on their preference for different rice textures (Maleki, Oliver, Lewin, Liem, \u0026amp; Keast, 2020).\u003c/p\u003e \u003cp\u003eWithin each rice variety, the milling process (e.g., white vs brown rice) alters the nutrient composition and sensory attributes (Shobana et al., 2011) For example, brown rice has a higher lipid content compared with white rice. The lipid context affects the sensory profile due to lipid oxidation in the bran layer of brown rice (Park, Kim, \u0026amp; Baek, 2010). Lipid oxidation leads to the development of off-flavours (Pingret, Fabiano-Tixier, \u0026amp; Chemat, 2013), which potentially impact consumer perception and acceptance. In short, differences in the acceptance of white and brown rice is likely caused by differences in sensory profiles, which are related to differences in nutrient composition (Sudha et al., 2013).\u003c/p\u003e \u003cp\u003eIn Australia 90% of rice is consumed as white rice, whereas only 10% is consumed as brown rice (Pollard et al., 2017), which is similar to global rice consumption patterns (Pollard et al., 2017; Selvam, Masilamani, Umashankar, \u0026amp; Albert, 2017).. To understand what drives the difference in consumption of brown and white rice it is important to investigate the sensorial differences of brown and white rice.\u003c/p\u003e \u003cp\u003eThe objective of this study is to identify the drivers of liking of Australian-grown brown and white rice varieties. It will provide important information for rice industry and breeding programmes for the development of new rice varieties to meet consumer needs.\u003c/p\u003e "},{"header":"2. Participants, Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA within-subjects crossover design with randomised order (William\u0026rsquo;s Latin Square design) for liking and Just About Right scales with six repeated samples was used in the present study. To determine the required participant sample size, G*power [Version 3.1.9.2, (Franz Faul) Germany 2014] was used. Based on six measurements (six rice samples) comparisons within subjects with alpha level 0.05, power of 0.8, and a small effect size (f=.10), the minimum sample size was 109. To account for potential dropouts, 140 participants from CASS Food Research Centre database were recruited. Participants were excluded if they had food allergies, dietary restrictions, and/or were pregnant or lactating. Participants were asked to refrain from eating, drinking or brushing their teeth one hour prior to testing. The rice consumer study was approved by the research ethics committee Deakin University (HEAG-H 29_2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Measurements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were asked to complete two questionnaires concerning 1) demographics (age, gender, education, and marital status), and 2) rice consumption (type of rice (brown or white), number of times they eat rice daily, weekly or fortnightly, and awareness of brown rice health benefits). To assess the liking and sensory perception of the rice samples before and after tasting the rice samples, participants filled out 9-point hedonic scales (9= extremely like and 1= extremely dislike) (\u003ca href=\"#_ENREF_25\"\u003ePeryam \u0026amp; Pilgrim, 1957\u003c/a\u003e) for overall liking, aroma, colour and texture. In addition, participants completed Just About Right scales for aroma intensity, colour, hardness, fluffiness, stickiness and chewiness. A Just About Right scale, is a bipolar labelled attribute scale (\u003ca href=\"#_ENREF_23\"\u003ePag\u0026egrave;s, Berthelo, Brossier, \u0026amp; Gourret, 2014\u003c/a\u003e), which has an anchored mid-point that corresponded to Just About Right for each attribute (\u003ca href=\"#_ENREF_6\"\u003eCadot, Caill\u0026eacute;, Samson, Barbeau, \u0026amp; Cheynier, 2010\u003c/a\u003e). The Just About Right scales provided the participants with 3 answer options per sensory attribute (1=not enough, 2= Just about Right, 3= too much) (\u003ca href=\"#_ENREF_43\"\u003eJ.-E. Yang \u0026amp; Lee, 2020\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree Australian rice varieties [Jasmine rice (Kyeema), Low GI (Doongara) and Medium grain (Amaroo) (Table 1)] with both brown and white rice types were sourced from Sunrice (Ricegrowers Ltd, Leeton, Australia) Australia.\u003c/p\u003e\n\u003cp\u003eRice samples were washed 2 to 3 times in cold running water until the water ran clear. Rice samples were cooked in dedicated rice cookers (\u0026ldquo;Grain Master\u0026rdquo; HD4514/72_ UM_ US_v1.0, Philips), to avoid cross flavour contamination, according to manufacturer\u0026rsquo;s instructions with specific water to rice ratios (Table 1). Rice samples and water quantities were measured by a measuring cup. Rice was cooked at quick rice cooking mode and kept warm in the rice cooker for no longer than the duration of the sensory test (approximately 45 minutes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Testing procedure:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSensory testing took place in a sensory laboratory, which consisted of partitioned booths and a high-capacity air filtration system, of the CASS Food Research Centre, Deakin University Melbourne, Australia. On arrival participants were instructed to carefully read the Plain Language Statement and sign the consent form. Ten participants participated in each one-hour session. Rice samples were served to the participants in 30 mL serving cups that were labelled with three-digit unique codes. The rice samples were randomly presented one at a time directly from the rice cooker at a temperature of 55 \u0026plusmn;3 \u003csup\u003eo\u003c/sup\u003eC. The participants were instructed to rinse their mouth with filtered water for five seconds and use crackers between tasting the different rice samples.\u003c/p\u003e\n\u003cp\u003eThe test consisted of two parts (i.e., before tasting, after tasting). In the first part, the participants received the following instruction: \u0026ldquo;do not eat the rice samples, only look, feel (e.g., hold the rice between your fingers) and smell the rice\u0026rdquo;. Next, participants were asked to rate overall liking and their liking for aroma and colour on a 9-point hedonic scale, and fluffiness, stickiness, hardness and aroma intensity on Just About Right Scales.\u003c/p\u003e\n\u003cp\u003eIn the second part, the participants were instructed to taste the rice samples (one by one) and rate on 9-point hedonic scales, their overall liking, and texture for each rice sample. In addition, participants rated their perceived intensity of flavour, fluffiness, hardness and chewiness on Just About Right scales. There was a one-minute break after the tasting of each sample to avoid tasting fatigue of the participants.\u003c/p\u003e\n\u003cp\u003eThe data was collected on computers using Compusense Software Academic Consortium (Compusense, Inc., Guelph, Ontario, Canada). Gift vouchers were served to each participant on completion of the rice consumer test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll rice consumer study data was exported from Compusense Cloud into Microsoft Excel version 1708 (Microsoft Corporation) for data cleaning. For the statistical analysis of liking, the program Stata/IC 15.0 (StataCorp LLC,4905 Lakeway Drive, College Station, Texas 77845, USA) was used. Descriptive statistics (mean, standard deviation and correlation coefficient) were calculated for overall liking scores and all sensory attributes. Box plots and scatter plots were extracted for overall liking and for other sensory attributes. Linear mixed model approach was used to analyse repeated measure Analysis of Variance (ANOVA) data to determine the effect of rice varieties (Jasmine, Low GI and medium grain rice samples) and rice types (brown, white) on overall liking, aroma, colour and texture linking. This approach accounts for within subjects autocorrelation via a random intercept in the model. The combined effect of rice varieties and types of rice was tested through a model that contained main effects of rice type (brown and white) and varieties (Jasmine, Low GI and Medium Grain) as well as the two-way interaction between varieties, and types of rice. The post-hoc pairwise comparison (Bonferroni adjusted) was conducted to identify the significant difference in sensory attributes among rice varieties and rice types.\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics for Just About Right attributes, overall liking and penalty analysis (p\u0026lt;0.05) of brown and white rice from the three varieties was conducted in XLSTAT-Sensory version 2020.3 (Addinsoft, New York, NY, USA). The penalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together (\u003ca href=\"#_ENREF_43\"\u003eJ.-E. Yang \u0026amp; Lee, 2020\u003c/a\u003e). Spearman\u0026rsquo;s correlation coefficients were calculated. Mean drop plots were extracted to identify the effect of JAR attributes on overall liking of rice. The mean drops were plotted against the percentage of consumers. For penalty analysis and mean drop plots, 20% consumers were considered as threshold level for each JAR attribute (\u003ca href=\"#_ENREF_23\"\u003ePag\u0026egrave;s et al., 2014\u003c/a\u003e).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants (n=140, female 52%, male 48%) from different age groups participated in the consumer study, one participant was excluded during data cleaning because of incomplete rice tasting session. The participants were rice consumers and mostly thought they were aware of the health benefits of brown rice. The demographics are shown in table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Liking (9-point hedonic scale) of brown and white rice varieties before tasting: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the result section rice variety refers to the different varieties which were tested (i.e., Jasmine white, Jasmine brown, Low GI white, Low GI brown, Medium grain white and Medium grain brown) and rice type refers to brown and white rice. The results (Table 3) indicate that there was a main effect of rice varieties and their types (i.e., brown vs white) on overall liking before tasting the rice samples (p\u0026lt;0.05). However, there was no statistically significant interaction between rice variety (i.e., Jasmine, Low GI, Medium Grain) and rice type. That is, white rice was preferred over brown rice, regardless of the rice variety (p\u0026lt;0.05) (see Figure 1). Pairwise comparisons show that Jasmine white rice was more liked than any of the other rice varieties (p\u0026lt;0.05). While, liking of Low GI white and Med. grain was not statistically significantly different (represented with shared letter \u0026ldquo;C\u0026rdquo;). Likewise, no difference was observed between the overall liking of brown rice varieties.\u003c/p\u003e\n\u003cp\u003eFor aroma liking, there was a significant difference (Table 3) between rice varieties and their types (i.e., brown vs white) before tasting the rice samples. The differences in mean values of Low GI and Medium Grain were -0.6, 95% CI (-0.8, -0.5) and -0.7, 95% CI (-0.9, -0.5) respectively when compared with Jasmine rice (a reference sample). The interaction between rice variety (i.e., Jasmine, Low GI, Medium Grain) and rice type was also statistically significant. That is, the aroma of white rice was preferred over brown rice, regardless of the rice variety (p\u0026lt;0.05) (see Figure 1). Pairwise comparisons show that the aroma of Jasmine white rice was more liked than any of the other rice varieties (i.e., Jasmine white rice has the highest mean 7.0, 95% CI (6.9, 7.3) and Medium Grain brown has lowest mean 5.8, 95% CI (5.5, 6.1)). While, liking of Low GI white and Med. grain was not statistically significantly different (represented with shared letter \u0026ldquo;AB\u0026rdquo;). Similarly, no difference was observed between the aroma liking of Low GI and Med. Grain brown rice varieties.\u003c/p\u003e\n\u003cp\u003eThe rice varieties and their types (i.e., brown vs white) were significantly associated with colour liking before tasting the rice samples. The differences in mean values of Low GI and Medium Grain for colour liking were -0.4, 95% CI (-0.5, -0.2) and -0.1, 95% CI (-0.3, -0.04) respectively when compared with Jasmine rice (a reference sample). However, the interaction between rice variety (i.e., Jasmine, Low GI, Medium Grain) and rice type was not statistically significant. That is, the colour of white rice was liked more than the colour of brown rice, regardless of the rice variety (see Figure 1). Pairwise comparisons show that there was no difference in colour liking of Jasmine white and Med. Grain white rice (represented with shared letter \u0026ldquo;C\u0026rdquo;). Likewise, no difference was observed in colour liking of Jasmine brown, Low GI brown and Med. Grain brown (represented with shared letter \u0026ldquo;A\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. Liking (9-point hedonic scale) of brown and white rice varieties after tasting: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results show rice variety and rice type (i.e., brown and white) after tasting significantly affect liking (see Table 4). Jasmine rice was liked more than Low GI and Medium Grain rice. For all rice varieties white rice was preferred over brown rice (mean difference= 0.8, 95% CI (0.6, 1.1). The significant interaction between rice varieties and rice types (i.e., brown and white) shows that Jasmine white rice was liked more than any of the other brown and white rice varieties (see Figure 1). The pairwise comparisons show that no difference was observed between Low GI white rice, Med. Grain white rice and Jasmine brown rice in overall liking after tasting.\u003c/p\u003e\n\u003cp\u003eThere was significant correlation between rice variety and rice type on texture liking (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) after tasting rice samples (Table 4). That is, the texture of Jasmine rice was liked more than the texture of Low GI and Medium Grain. The mean liking of Low GI and Medium Grain rice were reduced by -0.6, 95% CI (-0.8, -0.4) and -0.4, 95% CI (-0.7, -0.2) respectively when compared with Jasmine rice (a reference sample). Likewise, the texture of white rice was preferred over brown rice, regardless of rice varieties (mean difference=0.91, 95% CI (0.6, 1.2). The significant interaction between rice varieties and rice types also indicates that the texture of Jasmine white rice was liked more than the texture of any of the other brown and white rice varieties (see Figure 1). The pairwise comparisons show that no difference was observed between Low GI white rice, Med. Grain white rice and Jasmine brown rice in texture liking after tasting. However, the texture liking of brown rice varieties was not statistically different.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Just About Right attributes and Penalty Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1. Penalty analysis of Jasmine brown and white rice before tasting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePenalty analysis shown in Table 5, indicates that the overall penalty is significant (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) for all attributes of Jasmine brown rice. This means that the rice was not perceived at optimum level for all attributes tested. The Jasmine brown rice was rated as being too low in aroma, too dark in colour, too hard in texture, too low in fluffiness and/or too low in stickiness. Similarly, for Jasmine white rice, the overall penalty (Table 5) is not significant for any of the attributes. This means that across all tested attributes, a deviation from JAR did not have a significant influence on overall liking. The mean drop plot against consumers for each attribute of Jasmine brown and white rice shown in Figure 2 (A \u0026amp; B), which visually represents the results of the penalty analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2. Penalty analysis of Low GI brown and white rice before tasting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall penalty analysis for Low GI brown rice is significant (p\u0026lt;0.05) for all attributes except \u0026ldquo;hardness\u0026rdquo; (p=0.18) (see table 6). This means that the hardness of Low GI brown rice was the only attribute which was rated as being optimal. The penalty analysis (Table 6) shown that the overall penalty for Low GI white rice is significant for fluffiness (p\u0026lt;0.05). This means that the rating of liking was significantly negatively influenced when participant rated Low GI white as low in fluffiness. Specific changes in liking due to suboptimal attributes are shown in Figure 3 (A \u0026amp; B) which visually represents the penalty analysis of Low GI rice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3. Penalty analysis of Med. Grain brown and white rice before tasting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the penalty analysis (Table 7) for Med. Grain brown rice show that the overall liking was significantly (p\u0026lt;0.05) influenced when majority of the participants considered aroma, colour and hardness are not at optimum level, the attributes were too high in aroma, too dark in colour and too hard in texture. Similarly, the overall penalty (Table 7) for Med. Grain white rice is significant for fluffiness (\u003cem\u003ep\u003c/em\u003e=0.02). That means, for fluffiness, the deviations from the Just about right level have significant impact on overall liking. The impact on liking of each attribute shown in Figure 4(A \u0026amp; B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4 Penalty analysis of Jasmine brown and Jasmine white rice after tasting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePenalty analysis (Table 8) indicate that the overall liking was significantly (p\u0026lt;0.05) influenced when participants rated the Jasmine brown rice as not being ideal for flavour, fluffiness, hardness or chewiness. For Jasmine white rice, the overall penalty (Table 8) is only significant for hardness and not significant for all other attributes after rice tasting. This means that most of the participants considered Jasmine white rice \u0026ldquo;not hard enough\u0026rdquo; in texture. The mean drop plot against participants for each attribute by tasting of Jasmine brown and Jasmine white rice shown in Figure 5(A \u0026amp; B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.5 Penalty analysis of Low GI brown and Low GI white rice after tasting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe penalty analysis (Table 9) of Low GI brown rice by tasting shows that the overall liking was significantly (p\u0026lt;0.05) influenced when most of the participants judged that flavour, hardness and fluffiness were not optimal in Low GI brown rice. For Low GI white rice, the overall penalty (Table 9) is significant (p\u0026lt;0.05) for all attributes tested. This means, the overall liking was significantly influenced, when majority of the participants rated Low GI white rice as being not ideal for flavour, fluffiness, hardness or chewiness. The influence on liking of sensory attributes shown in Figure 6(A \u0026amp; B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.6 Penalty analysis of Med. Grain brown and white rice after tasting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor Med. Grain brown rice, the penalty analysis (Table 10) shows that the overall liking of Med. Grain brown rice was significantly (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) influenced when participant rated flavour, fluffiness and hardness were not at optimum level. Similarly, the overall penalty of Med. Grain white rice is significant for flavour intensity, fluffiness and chewiness. This means that significant participants perceived Med. Grain white as too low in flavour and fluffiness, and too high in chewiness. The mean drop plots against participants for each attribute of Med. Grain brown rice shown in Figure 7(A \u0026amp; B).\u003c/p\u003e"},{"header":"4. Discussion","content":" \u003cp\u003eThis study aimed to identify the consumer liking, sensory attributes, and drivers of liking of brown and white rice varieties. The results suggest that, overall, participants liked Jasmine rice varieties more than Low GI and Medium grain rice varieties. This was also reflected in a higher liking of the aroma, colour and texture of Jasmine rice, compared to Low GI and Medium grain rice varieties. However, white rice is preferred over brown rice regardless of rice varieties.\u003c/p\u003e \u003cp\u003eThe present study suggests, in line with previous studies (Bunyasiri \u0026amp; Sirisupluxana, 2017; Sudha et al., 2013; Suwansri et al., 2002; Wangcharoen et al., 2016) that texture, colour and aroma are important drivers of consumer liking for rice. However, these drivers of liking do not seem to equally explain the differences in liking of white and brown rice. That is, differences in aroma mainly explain the difference in liking for white rice varieties and the aroma of Jasmine white rice was liked more than any of the other rice varieties. The most liked white rice (Jasmine rice), contains more of the compound 2-acetyle-1-pyrroline (Daygon et al., 2017) which is known to elicit a distinctive popcorn/pandan aroma (Bergman et al., 2000; Ron G Buttery, Ling, Juliano, \u0026amp; Turnbaugh, 1983; Jezussek, Juliano, \u0026amp; Schieberle, 2002; Lee et al., 2019) that has strong impact on consumer acceptance of rice (Tananuwong \u0026amp; Lertsiri, 2010). Whereas the other white rice (non-fragrant) varieties contain less 2AP (Mahattanatawee \u0026amp; Rouseff, 2014; D. S. Yang, Shewfelt, Lee, \u0026amp; Kays, 2008; Zeng, Zhang, \u0026amp; Zhang, 2009) that may have an impact on liking of non-fragrant white rice varieties. This is also reflected in the sensory data of the present study that aroma of Jasmine white rice is an important sensory attribute in predicting consumer liking and acceptance of white rice varieties. Therefore, the aroma of Jasmine white rice was preferred over all other white and brown rice varieties. In contrast to aroma being able to explain liking differences for white rice varieties, aroma does not fully explain differences in liking for brown rice.\u003c/p\u003e \u003cp\u003eDifferences between brown rice varieties can be explained by texture (hardness and chewiness). That is, brown rice is considered as too hard and chewy in texture which is driving the difference between brown rice varieties. Whereas Jasmine brown rice was preferred over Low GI and Med. grain brown rice. However, the results are in line with previous study conducted on ready to eat rice in Korea that the brown rice was scored less in overall acceptability due to being high in hardness, chewiness and yellowness (Kwon \u0026amp; Ju, 2018). Brown rice hardness in texture is associated with dietary fibre that is present in bran layer (Wang et al., 2011) whereas, in white rice, polishing removes bran and germ during rice processing (Liu, Zheng, \u0026amp; Chen, 2017). This significantly improves texture liking and consumer acceptance of white rice. However, the current study investigated consumer acceptance of brown and white rice with Western consumers using 9-Point hedonic scales, JAR scales and penalty analysis that was not reported by previous studies. This study also compared a range of brown and white rice varieties which enabled to compare brown and white rice, but also identify the drivers of liking between brown rice varieties as well as the drivers of liking within white. In addition, Therefore, it is interesting to note that rice texture (hardness) is more important for the consumer acceptance and overall liking of Australian brown rice varieties. This study suggests that the decrease in hardness and chewiness will increase the overall liking of Australian brown rice varieties, which can eventually increase brown rice acceptance and consumption.\u003c/p\u003e \u003cp\u003eBrown rice texture (hardness and chewiness) and colour are the sensory attributes that are driving the difference between white and brown rice varieties. That is, the texture of brown rice is less liked as compare to white rice regardless of rice varieties, because the majority of participants rated brown rice varieties as too hard and too chewy. However, differences in texture seem to be more important when comparing liking between white and brown rice. This is in line with study conducted on consumer acceptance of parboiled brown and white rice reported that white rice was preferred to brown rice because of texture and colour (Sudha et al., 2013). The results are also in agreement with the study that reported consumer acceptance of white rice varieties in Thailand, the participants preferred cooked white rice because of the soft texture (Wangcharoen et al., 2016). Suwansri and Meullenet (2004) reported that Asian consumers preferred rice with white appearance (colour) and less sticky texture (Suwansri \u0026amp; Meullenet, 2004). Similarly, the consumers from South Asia and Middle East did not prefer brown rice texture (Suwannaporn, Linnemann, \u0026amp; Chaveesuk, 2008). In the present study, the sensory results also suggest that brown rice texture (hardness and chewiness) is the most important sensory attribute that is driving the liking and consumer acceptance of brown rice.\u003c/p\u003e \u003cp\u003eThis study has several strengths as this is the first study that focused on consumer acceptance of Australian brown and white rice varieties. In addition, sensory attributes for consumer acceptance were evaluated using 9-point hedonic scale, Just About Right scale and penalty analysis was also conducted in this study. However, there are some limitations that need to be addressed. The participants were mainly living in urban areas and were well educated, with 79% of participants holding undergraduate degree or higher. That may affect their liking because of their awareness to the brown and white rice varieties which may cause bias in evaluation of rice attributes. For future investigation, the sample (participants) could be recruited from different geographical areas to predict the preference of Australian brown and white rice varieties. Future studies are suggested with more focus on texture attributes of brown rice. To identify the variability in texture of brown rice, different cooking methods and water to rice ratios are recommended. In addition, the instrumental analysis (colour and texture analyser) can be considered for the better understanding of texture attributes of brown and white rice varieties.\u003c/p\u003e "},{"header":"5. Conclusion","content":" \u003cp\u003eTexture is the most important sensory attribute which explains the difference in liking between brown and white rice. Whereas differences in aroma, best explain variation in liking of white rice. Therefore, the sensory attributes of Australian brown rice varieties can be improved by the development of new rice varieties which are soft in texture. Future research is needed to investigate if an increased water absorption, milling process, packaging and storage of brown rice can positively improve the texture and subsequently increase consumer acceptance.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eJAR: \u003c/strong\u003eJust About Right\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRRI: \u003c/strong\u003eInternational Rice Research Institute\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCASS: \u003c/strong\u003eCentre for Analytical Sensory and Safety\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA: \u003c/strong\u003eAnalysis of Variance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXLSTAT: \u003c/strong\u003eName of Statistical Sensory Software\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI: \u003c/strong\u003eConfidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRef: \u003c/strong\u003eReference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rice consumer study was approved by the research ethics committee Deakin University (HEAG-H 29_2018). The participants were asked to read Plain Language Statement (PLS) and all participants signed their consent forms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe written consent for publication from all authors has been received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding support: \u003c/strong\u003eThis work was funded by Bahauddin Zakariya University Multan, Pakistan and Deakin University Melbourne, Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTanweer Aslam Gondal:\u003c/strong\u003e Conceptualization, Methodology, Writing, and original draft preparation. \u003cstrong\u003eRussell Keast:\u003c/strong\u003e Conceptualization, Supervision, Reviewing and Editing. \u003cstrong\u003eRobert A Shellie:\u003c/strong\u003e Writing- Reviewing and Editing. \u003cstrong\u003eSnehal R Jadhav:\u003c/strong\u003e Supervision, Writing- Reviewing and Editing. \u003cstrong\u003eShirani Gamlath: \u003c/strong\u003eSupervision, reviewing and editing. \u003cstrong\u003eMohammadreza Mohebbi:\u003c/strong\u003e statistical analysis\u003cstrong\u003e. Djin Gie Liem: \u003c/strong\u003eConceptualisation, Methodology, Supervision, Writing- Reviewing and Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBergman, C. J., Delgado, J. T., Bryant, R., Grimm, C., Cadwallader, K. R., \u0026amp; Webb, B. D. (2000). Rapid Gas Chromatographic Technique for Quantifying 2-Acetyl-1-Pyrroline and Hexanal in Rice (Oryza sativa, L.). \u003cem\u003eCereal Chem J, 77\u003c/em\u003e. doi:10.1094/cchem.2000.77.4.454\u003c/li\u003e\n\u003cli\u003eBryant, R. J., \u0026amp; Mcclung, A. M. (2011). Volatile profiles of aromatic and non-aromatic rice cultivars using SPME/GC \u0026ndash;MS. \u003cem\u003eFood Chem, 124\u003c/em\u003e. doi:10.1016/j.foodchem.2010.06.061\u003c/li\u003e\n\u003cli\u003eBunyasiri, I. N., \u0026amp; Sirisupluxana, P. (2017). Consumers behavior and rice attributes for Thai hommali rice in Sichuan province of China. \u003cem\u003eThe Business \u0026amp; Management Review, 8\u003c/em\u003e(4), 329.\u003c/li\u003e\n\u003cli\u003eButtery, R. G., Ling, L. C., Juliano, B. O., \u0026amp; Turnbaugh, J. G. (1983). Cooked rice aroma and 2-acetyl-1-pyrroline. \u003cem\u003eJournal of Agricultural and Food Chemistry, 31\u003c/em\u003e(4), 823-826.\u003c/li\u003e\n\u003cli\u003eButtery, R. G., Ling, L. C., \u0026amp; Mon, T. R. (1986). Quantitative analysis of 2-acetyl-1-pyrroline in rice. \u003cem\u003eJ Agric Food Chem, 34\u003c/em\u003e. doi:10.1021/jf00067a031\u003c/li\u003e\n\u003cli\u003eCadot, Y., Caill\u0026eacute;, S., Samson, A., Barbeau, G., \u0026amp; Cheynier, V. (2010). Sensory dimension of wine typicality related to a terroir by Quantitative Descriptive Analysis, Just About Right analysis and typicality assessment. \u003cem\u003eAnalytica Chimica Acta, 660\u003c/em\u003e(1-2), 53-62.\u003c/li\u003e\n\u003cli\u003eChaudhary, R. C., Tran, D. V., \u0026amp; Duffy, R. (2001). \u003cem\u003eSpeciality rices of the world: breeding, production and marketing\u003c/em\u003e: Food and Agriculture Organization of the United Nations (FAO).\u003c/li\u003e\n\u003cli\u003eCiv\u0026aacute;ň, P., Craig, H., Cox, C. J., \u0026amp; Brown, T. A. (2015). Three geographically separate domestications of Asian rice. \u003cem\u003eNature plants, 1\u003c/em\u003e(11), 1-5.\u003c/li\u003e\n\u003cli\u003eCombris, P., Bazoche, P., Giraud-H\u0026eacute;raud, E., \u0026amp; Issanchou, S. (2009). Food choices: What do we learn from combining sensory and economic experiments? \u003cem\u003eFood Quality and Preference, 20\u003c/em\u003e(8), 550-557.\u003c/li\u003e\n\u003cli\u003eDaygon, V. D., Calingacion, M., Forster, L. C., Voss, J. J. D., Schwartz, B. D., Ovenden, B., . . . Fitzgerald, M. A. (2017). Metabolomics and genomics combine to unravel the pathway for the presence of fragrance in rice. \u003cem\u003eScientific Reports, 7\u003c/em\u003e, 8767. doi:10.1038/s41598-017-07693-9\u003c/li\u003e\n\u003cli\u003eHuang, X., Kurata, N., Wang, Z.-X., Wang, A., Zhao, Q., Zhao, Y., . . . Guo, Y. (2012). A map of rice genome variation reveals the origin of cultivated rice. \u003cem\u003eNature, 490\u003c/em\u003e(7421), 497-501.\u003c/li\u003e\n\u003cli\u003eIRRI. (2013). International Rice Research Institute: World Rice Statistics 2013. Retrieved from \u003ca href=\"http://irri.org/index.php?option\"\u003ehttp://irri.org/index.php?option\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eJanuszewska, R., Pieniak, Z., \u0026amp; Verbeke, W. (2011). Food choice questionnaire revisited in four countries. Does it still measure the same? \u003cem\u003eAppetite, 57\u003c/em\u003e(1), 94-98.\u003c/li\u003e\n\u003cli\u003eJezussek, M., Juliano, B. O., \u0026amp; Schieberle, P. (2002). Comparison of key aroma compounds in cooked brown rice varieties based on aroma extract dilution analyses. \u003cem\u003eJ Agric Food Chem, 50\u003c/em\u003e. doi:10.1021/jf0108720\u003c/li\u003e\n\u003cli\u003eKwon, Y.-S., \u0026amp; Ju, S.-y. (2018). Sensory evaluation of commercial ready-to-eat rice between trained panelist and consumer. \u003cem\u003eBritish Food Journal\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLee, J.-S., Sreenivasulu, N., Hamilton, R. S., \u0026amp; Kohli, A. (2019). Brown Rice, a Diet Rich in Health Promoting Properties. \u003cem\u003eJournal of nutritional science and vitaminology, 65\u003c/em\u003e(Supplement), S26-S28.\u003c/li\u003e\n\u003cli\u003eLi, X., Jervis, S., \u0026amp; Drake, M. (2015). Examining extrinsic factors that influence product acceptance: a review. \u003cem\u003eJournal of Food Science, 80\u003c/em\u003e(5).\u003c/li\u003e\n\u003cli\u003eLiu, K.-l., Zheng, J.-b., \u0026amp; Chen, F.-s. (2017). Relationships between degree of milling and loss of Vitamin B, minerals, and change in amino acid composition of brown rice. \u003cem\u003eLWT-Food Science and Technology, 82\u003c/em\u003e, 429-436.\u003c/li\u003e\n\u003cli\u003eMaclean, J., Dawe, D., Hardy, B., \u0026amp; Hettel, G. (2002). Rice almanac. 3rd edn. Wallingford, Oxon. In: CABI Publishing.\u003c/li\u003e\n\u003cli\u003eMahattanatawee, K., \u0026amp; Rouseff, R. L. (2014). Comparison of aroma active and sulfur volatiles in three fragrant rice cultivars using GC-Olfactometry and GC-PFPD. \u003cem\u003eFood Chem, 154\u003c/em\u003e. doi:10.1016/j.foodchem.2013.12.105\u003c/li\u003e\n\u003cli\u003eMaleki, C., Oliver, P., Lewin, S., Liem, G., \u0026amp; Keast, R. (2020). Preference mapping of different water‐to‐rice ratios in cooked aromatic white jasmine rice. \u003cem\u003eJournal of Food Science, 85\u003c/em\u003e(5), 1576-1585.\u003c/li\u003e\n\u003cli\u003eMir, S. A., Bosco, S. J. D., Shah, M. A., \u0026amp; Mir, M. M. (2016). Effect of puffing on physical and antioxidant properties of brown rice. \u003cem\u003eFood Chemistry, 191\u003c/em\u003e, 139-146. doi:10.1016/j.foodchem.2014.11.025\u003c/li\u003e\n\u003cli\u003ePag\u0026egrave;s, J., Berthelo, S., Brossier, M., \u0026amp; Gourret, D. (2014). Statistical penalty analysis. \u003cem\u003eFood Quality and Preference, 32\u003c/em\u003e, 16-23.\u003c/li\u003e\n\u003cli\u003ePark, J. S., Kim, K.-Y., \u0026amp; Baek, H. H. (2010). Potent aroma-active compounds of cooked Korean non-aromatic rice. \u003cem\u003eFood science and biotechnology, 19\u003c/em\u003e(5), 1403-1407.\u003c/li\u003e\n\u003cli\u003ePeryam, D. R., \u0026amp; Pilgrim, F. J. (1957). Hedonic scale method of measuring food preferences. \u003cem\u003eFood technology\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ePingret, D., Fabiano-Tixier, A.-S., \u0026amp; Chemat, F. (2013). Degradation during application of ultrasound in food processing: A review. \u003cem\u003eFood control, 31\u003c/em\u003e(2), 593-606.\u003c/li\u003e\n\u003cli\u003ePollard, C. M., Pulker, C. E., Meng, X., Scott, J. A., Denham, F. C., Solah, V. A., \u0026amp; Kerr, D. A. (2017). Consumer attitudes and misperceptions associated with trends in self-reported cereal foods consumption: cross-sectional study of Western Australian adults, 1995 to 2012. \u003cem\u003eBMC public health, 17\u003c/em\u003e(1), 597.\u003c/li\u003e\n\u003cli\u003eRothman, L., \u0026amp; Parker, M. (2009). Just-about-right (JAR) scales. \u003cem\u003eWest Conshohocken, PA: ASTM International\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSattari, A., Mahdinezhad, N., Fakheri, B., Noroozi, M., \u0026amp; Beheshtizadeh, H. (2015). Improvement of the eating and cooking qualities of rice: a review. \u003cem\u003eInternational Journal of Farming and Allied Sciences, 4\u003c/em\u003e(2), 153-160.\u003c/li\u003e\n\u003cli\u003eSelvam, S., Masilamani, P., Umashankar, P., \u0026amp; Albert, V. A. (2017). Opportunities and challenges in marketing of brown rice. In \u003cem\u003eBrown rice\u003c/em\u003e (pp. 271-282): Springer.\u003c/li\u003e\n\u003cli\u003eShobana, S., Malleshi, N., Sudha, V., Spiegelman, D., Hong, B., Hu, F., . . . Mohan, V. (2011). Nutritional and sensory profile of two Indian rice varieties with different degrees of polishing. \u003cem\u003eInternational journal of food sciences and nutrition, 62\u003c/em\u003e(8), 800-810.\u003c/li\u003e\n\u003cli\u003eSudha, V., Spiegelman, D., Hong, B., Malik, V., Jones, C., Wedick, N. M., . . . Ponnalagu, M. M. (2013). Consumer Acceptance and Preference Study (CAPS) on brown and undermilled Indian rice varieties in Chennai, India. \u003cem\u003eJournal of the American College of Nutrition, 32\u003c/em\u003e(1), 50-57.\u003c/li\u003e\n\u003cli\u003eSuwannaporn, P., Linnemann, A., \u0026amp; Chaveesuk, R. (2008). Consumer preference mapping for rice product concepts. \u003cem\u003eBritish Food Journal\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSuwansri, S., \u0026amp; Meullenet, J. (2004). Physicochemical characterization and consumer acceptance by Asian consumers of aromatic jasmine rice. \u003cem\u003eJournal of Food Science, 69\u003c/em\u003e(1), SNQ30-SNQ37.\u003c/li\u003e\n\u003cli\u003eSuwansri, S., Meullenet, J. F., Hankins, J., \u0026amp; Griffin, K. (2002). Preference mapping of domestic/imported jasmine rice for US‐Asian consumers. \u003cem\u003eJournal of Food Science, 67\u003c/em\u003e(6), 2420-2431.\u003c/li\u003e\n\u003cli\u003eTananuwong, K., \u0026amp; Lertsiri, S. (2010). Changes in volatile aroma compounds of organic fragrant rice during storage under different conditions. \u003cem\u003eJ Sci Food Agric, 90\u003c/em\u003e. doi:10.1002/jsfa.3976\u003c/li\u003e\n\u003cli\u003eTikapunya, T., Henry, R. J., \u0026amp; Smyth, H. (2018). Evaluating the sensory properties of unpolished Australian wild rice. \u003cem\u003eFood Research International, 103\u003c/em\u003e, 406-414. doi:10.1016/j.foodres.2017.10.037\u003c/li\u003e\n\u003cli\u003eTomlins, K., Manful, J., Larwer, P., \u0026amp; Hammond, L. (2005). Urban consumer preferences and sensory evaluation of locally produced and imported rice in West Africa. \u003cem\u003eFood Quality and Preference, 16\u003c/em\u003e(1), 79-89.\u003c/li\u003e\n\u003cli\u003eWang, K. M., Wu, J. G., Li, G., Zhang, D. P., Yang, Z. W., \u0026amp; Shi, C. H. (2011). Distribution of phytic acid and mineral elements in three indica rice (Oryza sativa L.) cultivars. \u003cem\u003eJournal of Cereal Science, 54\u003c/em\u003e(1), 116-121. doi:\u003ca href=\"https://doi.org/10.1016/j.jcs.2011.03.002\"\u003ehttps://doi.org/10.1016/j.jcs.2011.03.002\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eWangcharoen, W., Phanchaisri, C., Daengpok, W., Phuttawong, R., Hangsoongnern, T., \u0026amp; Phanchaisri, B. (2016). Consumer acceptance test and some related properties of selected KDML 105 rice mutants. \u003cem\u003eJournal of food science and technology, 53\u003c/em\u003e(9), 3550-3556.\u003c/li\u003e\n\u003cli\u003eXLSTAT. (2021). Penalty analysis in Excel tutorial. Retrieved from \u003ca href=\"https://www.xlstat.com/en/solutions/features/penalty-analysis\"\u003ehttps://www.xlstat.com/en/solutions/features/penalty-analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYang, D. S., Shewfelt, R. L., Lee, K. S., \u0026amp; Kays, S. J. (2008). Comparison of odor-active compounds from six distinctly different rice flavor types. \u003cem\u003eJ Agric Food Chem, 56\u003c/em\u003e. doi:10.1021/jf072685t\u003c/li\u003e\n\u003cli\u003eYang, J.-E., \u0026amp; Lee, J. (2020). Consumer perception and liking, and sensory characteristics of blended teas. \u003cem\u003eFood Science and Biotechnology, 29\u003c/em\u003e(1), 63-74.\u003c/li\u003e\n\u003cli\u003eYounas, A., Yousaf, Z., Riaz, N., Rashid, M., Razzaq, Z., Tanveer, M., \u0026amp; Huang, S. (2020). Role of Nanotechnology for Enhanced Rice Production. In R. S. Meena (Ed.), \u003cem\u003eNutrient Dynamics for Sustainable Crop Production\u003c/em\u003e (pp. 315-350). Singapore: Springer Singapore.\u003c/li\u003e\n\u003cli\u003eZeng, Z., Zhang, H., \u0026amp; Zhang, T. (2009). Analysis of flavor volatiles of glutinous rice during cooking by combined gas chromatography\u0026ndash;mass spectrometry with modified headspace solid-phase microextraction method. \u003cem\u003eJ Food Compos Anal, 22\u003c/em\u003e. doi:10.1016/j.jfca.2008.11.020\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Selected Australian rice varieties.\u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003e\u003cstrong\u003eRice Varieties\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003e\u003cstrong\u003eSamples (Types)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eWater to Rice ratio\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003eJasmine (Kyeema)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003eBrown rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e2 : 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003eJasmine (Kyeema)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003eWhite rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.5 : 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003eLow GI (Doongara)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003eBrown rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e2 : 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003eLow GI (Doongara)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003eWhite rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.5 : 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003eMedium grain (Amaroo)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003eBrown rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e2 : 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"247\"\u003e\n\u003cp\u003eMedium grain (Amaroo)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003eWhite rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.5 : 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 2: Demographics of the participants for rice tasting study.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eDemographics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eParticipants\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"147\"\u003e\n\u003cp\u003eAge groups\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eAge 18-30 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e56%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eAge 31-45 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e28%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eAge 46 years and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e16%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"147\"\u003e\n\u003cp\u003eRice consumers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eBrown rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e10%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eWhite rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e37%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eBrown and white rice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e53%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"147\"\u003e\n\u003cp\u003eBrown rice health benefits _ perceived knowledge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eAware enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e67%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eNot aware enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e33%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"147\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eHigh school certificate/ Diploma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e21%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"334\"\u003e\n\u003cp\u003eBachelor and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e79%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 3: Linear mix model (repeated measures) ANOVA table for brown and white rice varieties before tasting\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"80%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"386\"\u003e\n\u003cp\u003eOverall liking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"287\"\u003e\n\u003cp\u003eAroma liking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"277\"\u003e\n\u003cp\u003eColour liking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eMean diff.\u003cbr /\u003e (95 % CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eChi-square (df)\u003c/p\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eMean (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eMean diff.\u003cbr /\u003e (95 % CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eChi-square (df)\u003c/p\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eMean (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eMean diff.\u003cbr /\u003e (95 % CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eChi-square (df)\u003c/p\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eRice variety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eJasmine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.6 (6.4, 6.8)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e26.50\u003c/p\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026lt;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.7 (6.5, 6.9)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e53.84\u003c/p\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e6.6 (6.4, 6.8)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e20.08\u003c/p\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eLow GI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.2 (6.0, 6.4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-0.5\u003c/p\u003e\n\u003cp\u003e(-0.7, -0.3)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.1 (5.9, 6.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e-0.6\u003c/p\u003e\n\u003cp\u003e(-0.8, -0.5)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e6.2 (6.1, 6.5)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e-0.4\u003c/p\u003e\n\u003cp\u003e(-0.5, -0.2)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eMed. Grain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.3 (6.1, 6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-0.3\u003c/p\u003e\n\u003cp\u003e(-0.5, -0.1)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.1 (5.9, 6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e-0.7\u003c/p\u003e\n\u003cp\u003e(-0.9, -0.5)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e6.5 (6.3, 6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e-0.1\u003c/p\u003e\n\u003cp\u003e(-0.3, -0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eRice type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eBrown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e5.9 (5.6, 6.2)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e45.50\u003c/p\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026lt;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.1 (5.8, 6.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e13.93\u003c/p\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e5.9 (5.6, 6.2)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e50.85\u003c/p\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.9 (6.7, 7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.0 (0.7, 1.2)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.5 (6.3, 6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.5 (0.2, 0.7)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e7.0 (6.9, 7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.2 (0.9, 1.5)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003ePairwise comparisons of variety and rice type (Bonferroni groups)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eOverall liking by variety and rice type\u003c/p\u003e\n\u003cp\u003einteraction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003ePairwise comparisons of variety and rice type (Bonferroni groups)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eOverall liking by variety and rice type\u003c/p\u003e\n\u003cp\u003einteraction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003ePairwise comparisons of variety and rice type (Bonferroni groups)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eOverall liking by variety and rice type\u003c/p\u003e\n\u003cp\u003einteraction\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eJasmine White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e7.2 (6.9, 7.4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp; D\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e7.0 (6.9, 7.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp; D\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e7.3 (7.1, 7.5)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; C***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eLow GI White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.7 (6.5, 6.9)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp; C***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"89\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP=\u003c/em\u003e 0.43\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.2 (5.9, 6.4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;AB***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e=0.03\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e6.9 (6.7, 7.1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e =0.55\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eMed. Grain White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.7 (6.5, 7.0)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp; C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.3 (6.1, 6.6)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;AB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e7.0 (6.9, 7.2)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; BC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eJasmine Brown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e6.1 (5.8, 6.4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp; B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.4 (6.1, 6.7)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp; B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e6.0 (5.7, 6.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eLow GI Brown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e5.7 (5.3, 6.0)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp; A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6.0 (5.7, 6.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp; AB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e6.0 (5.7, 6.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; AB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eMed. Grain Brown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e5.9 (5.6, 6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp; AB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e5.8 (5.5, 6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e5.8 (5.5, 6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e* p 0.001 (p\u003c/em\u003e\u003cem\u003e\u0026lt;0.05), \u003c/em\u003e\u003cem\u003e** p 0.000 (p\u003c/em\u003e\u003cem\u003e\u0026lt;0.001),\u0026nbsp;\u0026nbsp; *** \u003c/em\u003eThe pairs that share alphabet letter are not statistically significantly different\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Mix model (repeated measures) ANOVA table for brown and white rice varieties after tasting \u0026nbsp;\u003cbr /\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"80%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"539\"\u003e\n\u003cp\u003eOverall liking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"415\"\u003e\n\u003cp\u003eTexture liking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eMean (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eMean diff.\u003cbr /\u003e (95 % CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003eChi-square (df)\u003c/p\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eMean (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eMean diff.\u003cbr /\u003e (95 % CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eChi-square (df)\u003c/p\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eRice variety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eJasmine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.5 (6.3, 6.8)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e47.59\u003c/p\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.3 (6.1, 6.6)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e25.67\u003c/p\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eLow GI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.9 (5.6, 6.1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e-0.7\u003c/p\u003e\n\u003cp\u003e(-0.9, -0.5)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.7 (5.5, 6.1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e-0.6\u003c/p\u003e\n\u003cp\u003e(-0.8, -0.4)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eMed. Grain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.0 (5.8, 6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e-0.6\u003c/p\u003e\n\u003cp\u003e(-0.8, -0.4)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.9 (5.6, 6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e-0.4\u003c/p\u003e\n\u003cp\u003e(-0.7,-0.2)*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eRice type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eBrown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.7 (5.4, 6.0)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e37.45\u003c/p\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.5 (5.2, 5.8)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e35.25\u003c/p\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP \u0026le;\u003c/em\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.6 (6.4, 6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e0.8 (0.6, 1.1)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.4 (6.2, 6.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e0.91 (0.6, 1.2)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003ePairwise comparisons of variety and rice type (Bonferroni groups)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003eOverall liking by variety and rice type\u003c/p\u003e\n\u003cp\u003einteraction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003ePairwise comparisons of variety and rice type (Bonferroni groups)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eOverall liking by variety and rice type\u003c/p\u003e\n\u003cp\u003einteraction\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eJasmine White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e7.0 (6.7, 7.3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.8 (6.5, 7.1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e\u0026nbsp;D\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eLow GI White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.3 (6.0, 6.5)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"125\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e=0.87\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.2 (5.9, 6.5)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e=0.57\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eMed. Grain White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.4 (6.2, 6.7)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.3 (6.0, 6.6)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eJasmin Brown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.1 (5.8, 6.4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.8 (5.5, 6.1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eLow GI Brown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.5 (5.2, 5.8)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.3 (5.0, 5.6)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eMed. Grain Brown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.5 (5.2, 5.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e5.5 (5.1, 5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eAB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*p 0.001 (p\u003c/em\u003e\u003cem\u003e\u0026lt;0.05), \u003c/em\u003e\u003cem\u003e** p 0.000 (p\u003c/em\u003e\u003cem\u003e\u0026lt;0.001), *** \u003c/em\u003eThe pairs that share alphabet letter are non-significant\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 5. The Penalty analysis and JAR variables (before tasting) for Jasmine brown and white rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAttribute\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eSensory test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eCorrelation coefficient\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSelection%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean drop\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePenalty\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u003cstrong\u003eJasmine Brown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eSmell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e53.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.85**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e28.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eColour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eVisual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo light\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e10.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e51.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.52*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo dark\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e38.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e13.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e52.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e33.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e35.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e48.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.73**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eStickiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e13.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e49.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.87 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e36.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u003cstrong\u003eJasmine White\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eSmell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e20.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e48.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e30.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eColour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eVisual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo light\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e70.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo dark\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e28.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e61.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e10.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e65.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e16.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eStickiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e8.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e41.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e56.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman\u0026rsquo;s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (\u0026ldquo;low\u0026rdquo; or \u0026ldquo;high\u0026rdquo;) on overall liking for rice samples. When the correlation is positive, the \u0026ldquo;too little\u0026rdquo; has a bigger impact than the \u0026ldquo;too much\u0026rdquo;, and vice-versa for the negative correlations.\u0026nbsp; If correlation is \u0026ldquo;0\u0026rdquo; for a JAR attribute then that attribute would have a strong impact on overall liking (\u003ca href=\"#_ENREF_41\"\u003eXLSTAT, 2021\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003ePenalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u0026le;0.001\u003c/em\u003e, **\u003cem\u003ep\u0026le;0.05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. The Penalty analysis and JAR variables (before tasting) for Low GI brown and white rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eSensory test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCorrelation coefficient\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSelection%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean drop\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePenalty\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow GI\u003c/strong\u003e\u003cstrong\u003e Brown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eSmell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e24.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e43.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.63**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e31.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eColour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eVisual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo light\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e46.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.64*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo dark\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e38.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e44.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e43.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e41.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e43.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.63**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eStickiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e28.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e48.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.89**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e23.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow GI\u003c/strong\u003e\u003cstrong\u003e White\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eSmell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e31.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e46.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e21.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eColour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eVisual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo light\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e14.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e73.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo dark\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e61.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e25.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e18.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e65.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.57**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eStickiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e19.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e61.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e19.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman\u0026rsquo;s correlation coefficient with a significance level a= 0.05. The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (\u0026ldquo;low\u0026rdquo; or \u0026ldquo;high\u0026rdquo;) on overall liking for rice samples. When the correlation is positive, the \u0026ldquo;too little\u0026rdquo; has a bigger impact than the \u0026ldquo;too much\u0026rdquo;, and vice-versa for the negative correlations.\u0026nbsp; If correlation is \u0026ldquo;0\u0026rdquo; for a JAR attribute then that attribute would have a strong impact on overall liking (\u003ca href=\"#_ENREF_41\"\u003eXLSTAT, 2021\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003ePenalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u0026le;0.001\u003c/em\u003e, **\u003cem\u003ep\u0026le;0.05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. The Penalty analysis and JAR variables (before tasting) for Med. grain brown and Med. grain white rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eSensory test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCorrelation coefficient\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSelection%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003eMean drop\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePenalty\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eMed. Grain Brown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eSmell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e22.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e39.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.96**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e38.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eColour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eVisual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo light\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e8.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e48.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.84*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo dark\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e42.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e41.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.34*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e54.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e33.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e44.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e22.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eStickiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e24.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e50.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.95*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e25.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eMed. Grain White\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAroma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eSmell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e35.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e39.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e24.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eColour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eVisual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo light\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e14.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e72.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo dark\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e24.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e57.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e23.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e54.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.52**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e22.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eStickiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eHandling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e7.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e37.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e54.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman\u0026rsquo;s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (\u0026ldquo;low\u0026rdquo; or \u0026ldquo;high\u0026rdquo;) on overall liking for rice samples. When the correlation is positive, the \u0026ldquo;too little\u0026rdquo; has a bigger impact than the \u0026ldquo;too much\u0026rdquo;, and vice-versa for the negative correlations.\u0026nbsp; If correlation is \u0026ldquo;0\u0026rdquo; for a JAR attribute then that attribute would have a strong impact on overall liking (\u003ca href=\"#_ENREF_41\"\u003eXLSTAT, 2021\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003ePenalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u0026le;0.001\u003c/em\u003e, **\u003cem\u003ep\u0026le;0.05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. The Penalty analysis and JAR variables (after tasting) for Jasmine brown and Jasmine white rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eCorrelation coefficient\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSelection%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eMean\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003eMean drop\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePenalty\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eJasmine Brown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eFlavour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e25.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e53.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.72**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e20.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e30.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e56.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.15*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e8.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e54.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.59*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e36.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eChewiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e8.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e46.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.13*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e44.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eJasmine White\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eFlavour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e30.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e52.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e21.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e58.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e19.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e24.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;1.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e69.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.18*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e6.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003eChewiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e-0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e18.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e58.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e22.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman\u0026rsquo;s correlation coefficient with a significance level a= 0.05. The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (\u0026ldquo;low\u0026rdquo; or \u0026ldquo;high\u0026rdquo;) on overall liking for rice samples. When the correlation is positive, the \u0026ldquo;too little\u0026rdquo; has a bigger impact than the \u0026ldquo;too much\u0026rdquo;, and vice-versa for the negative correlations.\u0026nbsp; If correlation is \u0026ldquo;0\u0026rdquo; for a JAR attribute then that attribute would have a strong impact on overall liking (\u003ca href=\"#_ENREF_41\"\u003eXLSTAT, 2021\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003ePenalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u0026le;0.001\u003c/em\u003e, **\u003cem\u003ep\u0026le;0.05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9. The Penalty analysis and JAR variables (after tasting) for Low GI brown and Low GI white rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCorrelation coefficient\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSelection%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eMean\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean drop\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePenalty\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow GI Brown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eFlavour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e33.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e39.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.96**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e26.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e4.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e35.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e51.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.74**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e35.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.10*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e58.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eChewiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e34.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e52.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow GI White\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eFlavour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e44.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e44.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.89*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e10.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e20.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e64.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.24*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e60.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.02*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e23.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eChewiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e23.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e59.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.60**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman\u0026rsquo;s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (\u0026ldquo;low\u0026rdquo; or \u0026ldquo;high\u0026rdquo;) on overall liking for rice samples. When the correlation is positive, the \u0026ldquo;too little\u0026rdquo; has a bigger impact than the \u0026ldquo;too much\u0026rdquo;, and vice-versa for the negative correlations.\u0026nbsp; If correlation is \u0026ldquo;0\u0026rdquo; for a JAR attribute then that attribute would have a strong impact on overall liking (\u003ca href=\"#_ENREF_41\"\u003eXLSTAT, 2021\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u0026le;0.001\u003c/em\u003e, **\u003cem\u003ep\u0026le;0.05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10. The Penalty analysis and JAR variables (after tasting) for Med. Grain brown and white rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCorrelation coefficient\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSelection%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eMean\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003eMean drop\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePenalty\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eMed. Grain Brown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eFlavour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e25.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e46.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.31*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e28.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e33.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e53.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.49*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e13.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e38.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.98*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e56.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eChewiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e34.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e52.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eMed. Grain White\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eFlavour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e48.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e41.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.65**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e10.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eFluffiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e23.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e58.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.78**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eHardness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eNot hard enough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e38.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo hard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e56.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eChewiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e16.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eJAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e53.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;0.61**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eToo much\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e29.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe impact of JAR variables for Jasmine brown and white rice on the overall liking (Spearman\u0026rsquo;s correlation coefficient with a significance level a= 0.05). The correlation coefficients (between JAR attributes and overall liking) show how much JAR attributes have impacted (\u0026ldquo;low\u0026rdquo; or \u0026ldquo;high\u0026rdquo;) on overall liking for rice samples. When the correlation is positive, the \u0026ldquo;too little\u0026rdquo; has a bigger impact than the \u0026ldquo;too much\u0026rdquo;, and vice-versa for the negative correlations.\u0026nbsp; If correlation is \u0026ldquo;0\u0026rdquo; for a JAR attribute then that attribute would have a strong impact on overall liking (\u003ca href=\"#_ENREF_41\"\u003eXLSTAT, 2021\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003ePenalty is a weighted difference between means (mean liking of JAR category minus the mean of liking for other two levels (too low and too high) taken together\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u0026le;0.001\u003c/em\u003e, **\u003cem\u003ep\u0026le;0.05\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brown rice, white rice, sensory, consumer acceptance, Just About Right scale, JAR, penalty analysis. ","lastPublishedDoi":"10.21203/rs.3.rs-526268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-526268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRice is consumed as a staple food by more than half of the world\u0026rsquo;s population. Due to a higher fibre and micronutrient content, brown rice is more nutritious than white rice, but the consumption of brown rice is significantly lower than that of white rice, primarily due to sensory attributes. Therefore, the present research aimed to identify the sensory attributes which drive liking of Australian-grown brown and white rice varieties. Participants (n\u0026thinsp;=\u0026thinsp;139) tasted and scored (9-point hedonic scale) their liking (i.e., overall liking, aroma, colour and texture) of brown and white rice types of Jasmine (Kyeema), Low GI (Doongara) and Medium grain rice (Amaroo). In addition, participants scored, aroma, colour, hardness, fluffiness, stickiness and chewiness, on Just About Right Scales. A within-subjects crossover design with randomised order (William\u0026rsquo;s Latin Square design) was used with six repeated samples for liking and Just About Right scales. Penalty analyses were applied to determine the relative influence of perception of sensory attributes on consumer liking of the rice varieties. Across all varieties, white rice was liked more than brown rice due to texture and colour, and Jasmine rice was preferred over Low GI and Medium Grain. Rice texture (hardness and chewiness) was the most important sensory attribute among all rice varieties and aroma was important for driving of liking between white rice varieties.\u003c/p\u003e","manuscriptTitle":"Consumer Acceptance of Brown and White Rice Varieties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-20 16:44:27","doi":"10.21203/rs.3.rs-526268/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28f3b6b9-6091-4a67-baf1-bc579f35e035","owner":[],"postedDate":"May 20th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4444930,"name":"Agronomy"}],"tags":[],"updatedAt":"2024-07-17T14:02:24+00:00","versionOfRecord":{"articleIdentity":"rs-526268","link":"https://doi.org/10.3390/foods10081950","journal":{"identity":"foods","isVorOnly":true,"title":"Foods"},"publishedOn":"2021-08-22 14:02:24","publishedOnDateReadable":"August 22nd, 2021"},"versionCreatedAt":"2021-05-20 16:44:27","video":"","vorDoi":"10.3390/foods10081950","vorDoiUrl":"https://doi.org/10.3390/foods10081950","workflowStages":[]},"version":"v1","identity":"rs-526268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-526268","identity":"rs-526268","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}