Predictive modeling and correlation between the sensory and physicochemical attributes in ‘Rama Forte’ astringent persimmon fruit

Predictive modeling and correlation between the sensory and physicochemical attributes in ‘Rama Forte’ astringent persimmon fruit | 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 Research Article Predictive modeling and correlation between the sensory and physicochemical attributes in ‘Rama Forte’ astringent persimmon fruit Catherine Amorim, Elenilson Godoy Alves Filho, Deborah Santos Garruti, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4217960/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Correlations between quality attributes determined by destructive and non-destructive analysis methods are being investigated to enable quantification and prediction of internal quality characteristics without the need for destructive techniques. Our study correlated sensory and physicochemical attributes of 'Rama Forte' persimmons treated for astringency removal with 70 % CO 2 for 18 hours or 1.70 mL Kg -1 ethanol for 6 hours, to establish predictive models for destructive analytical methods based on non-destructive ones. Physicochemical and sensory analyses were carried out daily. Principal Component Analysis (PCA), Partial Least Squares Discriminant (PLS-DA) and regression analysis by Partial Least Squares (PLS) were applied to obtain prediction models. Two models based on fruit translucency (non-destructive) were obtained for persimmons treated with CO 2 , one for flesh firmness, and the other for color index prediction. A model based on sensory astringency (destructive) was developed to predict the astringency index for ethanol treatment. The models show a reliable fit, particularly in predicting flesh firmness by using the translucency of 'Rama Forte' fruit treated with CO 2 . Using the translucency scale and the prediction model, it is possible to establish the maximum period for logistic steps to reduce losses and waste in the persimmon chain. The low correlation between sensory astringency and proanthocyanidin content points to possible other compounds in the perception of astringency. Identifying these compounds will enable advances in the development of predictive models for quality attributes and shelf life of astringent persimmons. Diospyros kaki L. mathematical modeling proanthocyanidins astringency. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Brazil is the major persimmon ( Diospyros kaki L.) producer of the Americas and is fifth at global level. ‘Rama Forte’, the most important cultivar in the country (Prohort 2018), belongs to the pollination-variant group according to the classification of Ito (1971) i. e. , the fruit need to undergo astringency removal processes to become edible. The astringency is caused by the presence of soluble tannins in the mesocarp tissues, also termed proanthocyanidins, which are a class of phenolic compounds. Those compounds leave a sensation of dryness when fruit are ingested (Taira 1996; Xie and Dixon 2005). The most common methods for removing astringency are high CO 2 atmospheres or application of ethanol or ethylene (Amorim et al. 2023; Jin et al. 2018; Kato 1990; Tomoaki Matsuo et al. 1976; Min et al. 2014; Novillo et al. 2014; Tessmer et al. 2019). Acetaldehyde is a metabolite synthesized under anaerobic respiration (Nelson and Cox 2014) and is responsible for the polymerization and insolubilization of proanthocyanidins, reducing astringency (W. Chen et al. 2017; T. Matsuo and Itoo 1982; Xu et al. 2017). Along the deastringency process with exogenous ethanol there is an increase in ethanol by the activation of the enzyme alcohol dehydrogenase leading to the synthesis of acetaldehyde (Ben-Arie and Sonego 1993; Yamada et al. 2002). When applying CO 2 concentrations beyond 60 % as an astringency removal agent, the enzyme pyruvate dehydrogenase is activated converting pyruvate to acetaldehyde (W. Chen et al. 2017). During this reaction the enzyme alcohol dehydrogenase is also activated converting acetaldehyde to ethanol (Taira 1996). The literature on astringency removal of persimmons predominantly refers to aspects of deastringency and fruit quality with regards to physicochemical characteristics (Antoniolli et al. 2000; Arnal and Del Río 2003; Edagi et al. 2009; Novillo et al. 2015; Sato and Yamada 2016; Terra et al. 2014). Tessmer et al. (2018, 2019) studied, on the other hand, the cellular anatomical aspects of astringency removal. Nonetheless, there is little information on sensory perception of persimmons that have been treated for astringency removal. Even though physicochemical methods are largely employed to quantify metabolites and specific fruit characteristics they are unable to represent human perception. Therefore, sensory assessments are an important tool to estimate consumer perception of physical and chemical changes in fruit. Salvador et al. (2007, 2008) and Munera et al. (2019) used sensory evaluations as a tool to estimate astringency in 'Rojo Brillante’ persimmons. Akyldiz et al. (2004) used sensory evaluation to determine flavor, astringency and skin color of dehydrated ‘Türkay’ persimmons. Sanchís et al. (2016) also applied sensory evaluation together with physicochemical attributes to evaluate the quality of minimally processed ‘Rojo Brillante’ persimmons after anti-browning treatment. Das and Eun (2021) tested sensory astringency and evaluated the quality attributes of persimmons subjected to freezing temperatures. Arslan and Bayrakci (2016), El-Sayed (2017) and Milczarek et al. (2018) used sensory analysis to evaluate food products from processed persimmons. Martineli et al. (2019) reported the acceptability of ‘Rama Forte’ persimmons stored in three different packages. Comparative approaches between physicochemical and sensory attributes were tested in raspberries (Stavang et al. 2015), in minimally processed ´d`Anjou´ pears (Siddiq et al. 2020) and peaches and nectarines (Farina et al. 2019). Correlations linking sensory aspects with instrumental analysis may help not only in validation, but also in assembling predictive models employing simple analysis criteria. Non-destructive prediction models based on standard analysis methods might be advantageous and facilitate attainment and repeatability of results. Prediction models from non-destructive persimmon evaluations have been developed using near infrared (NIR) spectroscopy and hyperspectral imaging using six different Diospyros kaki cultivars (Milczarek et al. 2019). ‘Rojo Brillante’ (Munera et al. 2016; Munera et al. 2017), ‘Cheongdo-Bansi’ and ‘Daebong’ (Baek et al. 2023) and other fruits and vegetables (Nicoläi et al. 2014; Tziotzios et al. 2024; Zhang et al. 2024). In our study, 'Rama Forte' persimmons ( Diospyros kaki L.) subjected to two astringency removal processes were used to investigate correlations involving sensory and physicochemical attributes in order to establish prediction models for destructive analysis based on non-destructive methods. 2. Materials and Methods 2.1. Plant Material ‘Rama Forte’ ( Diospyros kaki L.) persimmons were harvested at the commercial ripening stage from a private orchard in Antônio Prado, Southern Brazil. The Programa Brasileiro para a Modernização da Horticultura (2009) (Persimmon Classification, Standardization, and Identification Rules) was used to select and standardize the fruit according to size and color: yellow-orange skin color and 6 to 8 cm in diameter. 2.2. Experimental procedure Persimmons were kept at room temperature (20 ± 5 °C) and treated with 70 % CO 2 for 18 hours or 1.70 mL Kg -1 ethanol for 6 hours. Both treatments were applied in hermetic plastic containers. No control treatment was included in the present trial because in three years of previous studies, ´Rama Forte´ persimmons that had not undergone deastringency processes remained firm, astringent, and with an orange skin color (Figure S1). Sensory and internal physicochemical changes were evaluated after deastringency treatments on the first day and daily thereafter up to eight days. The treated persimmons were kept at room temperature (20 ± 5 °C). The trial was carried out in a completely randomized design. For both methods of astringency removal, either CO 2 or ethanol, 30 fruit were evaluated every day. Panelists were provided with persimmon samples in two replicates for each day after deastringency treatments. 2.3 Sensory evaluation The descriptive quantitative method of Stone et al. (1974) was used to train the panelists and to lead the sensory analysis. Approval for our study (147.279/2012) was granted by the Ethics Committee from Ceará State University, Brazil. Fifteen trained panelists were selected for their discriminant power, reproducibility and consensus with the other panel members as recommended by Damasio and Costell (1991). The panelists were selected amongst the staff of the Embrapa - Centro Nacional de Uva e Vinho (Brazilian Agricultural Research Corporation - Grape and Wine) - by applying a questionnaire to identify persimmon consumers, their consumption habits, and the availability to participate in the panel. The panelists were subjected to the Basic Taste and Odor Recognition Tests (ABNT NBR ISO 8586, 2016), and to the Repertory Grid Method to define descriptors (Moskowitz 1983). The terms mentioned by each panelist were grouped and eleven descriptors were established for the evaluation of ‘Rama Forte’ persimmons: reddish orange color of the skin, orange color of the flesh, translucency, aroma, flavor, sweetness, bitterness, astringency, firmness, juiciness, and crispness (Table S1). The panelists were trained with regards to the descriptors and their respective references of intensity in a non-structured 9-cm linear scale anchored at both extremes by the terms absent or not very intense and very intense. The samples, composed of two longitudinal portions of the distal end of the persimmons, were offered in plastic plates labeled with three-digit codes as described in MacFie et al. (1989) together with the respective samples in glassware with lids destined for aroma evaluation. 2.4 Physical and chemical measurements Skin color of the persimmons was determined using a Konica/Minolta colorimeter, CR-400 model. The results were expressed as Hue angle, L*, C*, and color index. The color index was calculated as indicated by López-Camelo and Gómez (2004) using the following equation. Color Index = (2000 × a*) / L* × C* Flesh firmness was determined using a digital penetrometer (Güss Fruit Texture Analyzer) equipped with an 8 mm plunger of a flat surface. Two measurements were taken from each fruit at two opposite sites at the equatorial area. The results were expressed in Newton (N). Proanthocyanidins were evaluated using the Folin-Denis method (Taira 1996) at 725 nm on a spectrophotometer (Carry 60 UV-Vis, Agilent). The results were expressed in g gallic acid 100 g -1 fresh weight (g gallic acid 100 g -1 FW). Astringency index was determined using filter paper treated with a ferric chloride 5 % (w/v) solution and the imprint of the halved fruit on the filter paper as described in Gazit and Levy (1963). The imprints were estimated using a 5-point scale: 1 = non astringent, 2 = lightly astringent, 3 = moderately astringent, 4 = astringent e 5 = highly astringent (Gazit and Levy 1963). 2.5. Multivariate statistical analyses 2.5.1. Unsupervised and data fusion analyses A numerical matrix was separately constructed for the physicochemical and sensory attributes of persimmons subjected to the CO 2 or ethanol deastringency treatments totaling four matrices. The reddish orange color of the skin, orange color of flesh, translucency, juiciness, characteristic aroma, characteristic flavor, sweetness, bitterness, sensory astringency, sensory firmness, and crispness were used as sensory attributes. The color index, proanthocyanidins, astringency index, firmness, hue angle, C* and L* were used as physicochemical characteristics. The resultant matrices were imported by the PLS Toolbox™ program (version 8.6.2, Eigenvector Research Incorporated, Manson, WA USA) for unsupervised multivariate analysis via Principal Component Analysis (PCA). Before chemometric approaches, the data were autoscaled (mean centered with subsequent variance scaling) and the Singular Value Decomposition (SVD) algorithm was applied to decompose the matrices in scores, loadings, and modeling errors (Amorim et al. 2020). In order to improve the correlations between the physicochemical and sensory attributes among the samples from different matrices (sensory and physicochemical), the multiblock statistical analysis was further developed. The data scaling by block variance was applied to achieve the same loading strength on the matrices. The relevant information was obtained using the Venetian Blinds as cross-validation method with confidence level of 95 % (Filho et al. 2020; Mishra et al. 2021). 2.5.2. Supervised analyses Multivariate classification modeling by PLS-DA (Partial Least Squares Discriminant Analysis) was developed for each matrix (sensory and physicochemical) to achieve the most relevant compounds variability according to each deastringency treatment (CO 2 and ethanol). The persimmon data was also autoscaled and the SIMPLS (Simplified PLS) algorithm was applied to decompose the matrices in scores, loadings and figures of merit. The number of latent variables (LV) was selected based on the statistical parameters described in Table 1. The cross-validation of the models was developed using the Venetian Blind method under 10 splits and blind thickness equal to 1. Multivariate regression analyses by PLS (Partial Least Squares) were developed to investigate the correlation between the physicochemical and sensory attributes and therefore, to predict a parameter from a destructive method by a non-destructive and simpler method. The sensory persimmon fruit attributes were used as independent variables (X-matrix) and physicochemical attributes as dependent variables (Y-matrix), in order to find the maximum covariance among these datasets. The same SIMPLS algorithm was used on the autoscaled matrices, and the number of LV was selected according to the statistical parameters described in Table 2 (Wold et al., 2001). The robustness of the regression models was further evaluated by the proximity between the ideal and real regression curves from the measured and predicted physicochemical characteristics based on sensory attributes. 2.6. Univariate statistical analysis Analysis of variance (ANOVA) single factor was developed to statistically certify the variability of the sensory and physicochemical attributes according to each deastringency treatment (CO 2 and ethanol). The means comparison was developed using Tukey test at significance level of 0.05, and Levene test was used to verify the variance homogeneity among the sample groups based on days after the deastringency treatments (Sucupira et al. 2017). 3. Results 3.1. Multivariate exploratory analyses Figures 1 and 2 illustrate the PCA results from multiblock analysis for CO 2 and ethanol treatments respectively, highlighting the variables correlation between the physicochemical and sensory attributes. Persimmons treated with CO 2 ripened faster than those treated with ethanol. Consequently, CO 2 treated fruit became unsuitable for consumption after eight days and could not be evaluated by the panelists. For CO 2 treatment (Fig. 1), the PC1 axis retained the main information for the samples discrimination based on the days after treatment, with 69.11 % of the total variance. Differently, deastringency influenced by ethanol (Fig. 2) was achieved on PC1 and PC3 axes, which retained lower total variance (49.2 %) than the CO 2 treatment. The gradual effect on astringency reduction by ethanol decreased the differentiation among the fruit after treatment and therefore, and more axes (PCs) were necessary to explain the deastringency effect of fruit based on the sensory and physicochemical attributes. The CO 2 treatment (Fig. 1) clearly evidenced a drastic decrease of the values from the sensory attributes related to bitterness, sensory astringency, sensory firmness and crispness, as well as from the physicochemical characteristics related to the proanthocyanidins, astringency index, firmness, hue angle, C and L* after 3 days of treatment. On the other hand, sensory attributes achieved by the reddish orange color of the skin (ROCS), orange color of the flesh (OCF), skin translucency, juiciness, characteristic aroma, characteristic flavor and sweetness, and the color index (physicochemical attribute) significantly increased after 3 days of treatment. Fruit at the fourth and fifth days showed intermediary variability of all the aforementioned sensory and physicochemical attributes (imprecise samples distribution between negative and positive PC1 scores). Differently from the CO 2 treatment, the ethanol treatment showed clustering tendencies of the fruit on the PC1 × PC3 scores based on the sensory and physicochemical attributes (Fig. 2). Fruit from 0 (immediately after treatment) 1, 2 or 3 days after the deastringency treatment was clustered at negative scores of PC1 and positive of PC3 by the concomitant high values of bitterness and sensory astringency as sensory attributes, and proanthocyanidins, astringency index and firmness as physicochemical attributes. Fruit after 6, 7 and 8 days of the deastringency treatment clustered at positive PC1 scores by high values of the sensory attributes ROCS, OCF, skin translucency, juiciness, characteristic aroma, characteristic flavor, and sweetness, as well as high values of color index as physicochemical attributes. In general, fruit from the 4th and 5th days showed intermediate PC1 and PC3 scores (intersection between two circles), which ranged between the beginning and the ending of the astringency removal process. High values of sensory firmness, crispness, hue angle, C and L* (at negative scores of PC1 and PC3) contributed to evidence the gradual changes according to the days after deastringency treatment with ethanol. 3.2. Multivariate classification analysis A supervised classification analysis by PLS-DA was further developed in order to corroborate the samples clustering achieved by PCA evaluations, as well as to highlight the most important sensory and physicochemical attributes related to each deastringency treatment (CO 2 and ethanol) by VIP analysis (Variables Importance for Projection). Therefore, only fruit with clear discrimination based on the exploratory analyses results were selected for evaluation, such as 0, 1, 2, 6 and 7 days after treatment. The samples influence on modeling was verified by Hotelling T 2 and modeling errors by Q residuals, which are illustrated in Figures 3A and 3B for CO 2 and ethanol, respectively. Despite some persimmon fruit showed elevated influence on modeling as well as high modeling error (values above the threshold at 1), they did not negatively influence both modeling (CO 2 and ethanol), according to the statistical parameters described in Table 1. The variables’ relevance highlighted by the VIP analysis are illustrated in Figures 3C and 3D for the CO 2 and ethanol treatments, respectively. The reddish orange color of the skin (ROCS), orange color of the flesh (OCF), translucency, juiciness, sensory firmness, crispness, color index, firmness, C and L* were the most relevant variables (higher variability) for the deastringency treatment by CO 2 (Fig. 3C); and the characteristic flavor, sweetness, bitterness, sensory astringency, proanthocyanidins, astringency index and firmness were the most relevant variables for the deastringency treatment by ethanol. Table 1 Statistical parameters from the classification modeling of the ‘Rama Forte’ persimmon fruit under the deastringency treatments by CO 2 and ethanol Parameters CO 2 Ethanol LV number 2 3 Captured variance (%) a 76.10 57.18 True Positive and Sensitivity b 1.00 1.00 True Negative and Specificity c 1.00 0.98 RMSEC d 0.09 0.15 RMSECV e 0.10 0.18 RMSEC / RMSECV f 0.90 0.83 Bias g 2.20 x 10 -16 3.30 x 10 -16 CV Bias h 2.60 x 10 -3 5.10 x 10 -3 R 2 cal i 0.96 0.91 R 2 CV j 0.95 0.87 a Percent variance captured in X-block (matrix X); b Sensitivity on cross-validation; c Specificity on cross-validation; d Root Mean Standard Error of Calibration; e Root Mean Standard Error of Cross-Validation; f Similarity criterion; g Average difference between the estimator and real group during the calibration; h Average difference between the estimator and real group during the cross-calibration; i Coefficient of correlation between the real and predicted group during the calibration; j Coefficient of correlation between the real and predicted group during the cross-validation. 3.3. Univariate statistical analysis Analysis of variance (ANOVA single factor) was developed considering the highlighted variables by the VIP analysis (Fig. 3) in order to statistically certify their variability according to each deastringency treatment. Therefore, the reddish orange color of the skin (ROCS), orange color of the flesh (OCF), translucency, juiciness, sensory firmness, crispness, color index, firmness, C and L* from the CO 2 treatment; and the characteristic flavor, sweetness, bitterness, sensory astringency, proanthocyanidins, astringency index and firmness from the ethanol treatment were evaluated by ANOVA. The univariate statistical analysis by ANOVA from the CO 2 (Fig. 4) and ethanol (Fig. 5) treatments corroborated the attributes variability from the beginning to the end of both deastringency treatments detected by the multivariate statistical results. 3.4. Regression analyses For regression analysis, all the sensory attributes were tentatively correlated to physicochemical characteristics: for CO 2 treatment the translucence (non-destructive) was better adjusted to the color index (non-destructive) and firmness (destructive); and for ethanol treatment the sensory astringency (destructive) was better adjusted to the astringency index (destructive). The statistical parameters from the y-block (dependent variables) of these better-adjusted models are described in Table 2. Additionally, to the multivariate regressions, univariate regression models were developed considering the selected sensory and physicochemical attributes (based on the models quality) to complement the prediction ability, with the respective statistical parameters (equations and Pearson correlation coefficient) presented in Table 2. The models were better adjusted on CO 2 treatment than the ethanol treatment, which may be related to the gradual parameters variability into the ethanol treatment that decreased the samples differences among the deastringency treatment. This information may be corroborated by comparison of the PCA results illustrated in Figures 1 (CO 2 ) and 2 (ethanol). Table 2 Statistical parameters from the regression modeling of the ‘Rama Forte’ persimmon fruit under CO 2 treatment predicting the color index and firmness based on the translucence; and under ethanol treatment predicting the astringency index based on characteristic aroma and sensory astringency together Parameters CO 2 Ethanol Color index Firmness Astringency Index LV number 1 1 1 Captured variance (%) a 100 100 100 RMSEC b 3,08 14,29 0,83 RMSECV c 3,11 14,38 0,83 RMSEC / RMSECV d 0,99 0,99 11,00 Bias e 3.50 x 10 -15 2.10 x 10 -14 -1.78 x 10 -15 CV Bias f 1.40 x 10 -3 -1.00 x 10 -4 1.60 x 10 -3 R 2 cal g 0,70 0,82 0,56 R 2 CV h 0,69 0,81 0,55 Pearson’s R 0,83 0,91 0,75 Equation y = 0.6945x + 3.4455 y = - 0.8211x + 7.4025 y = 0.56457x + 1.28773 n / ν j 170 / 172 172 / 170 207 / 205 a Percent variance captured in X-block (matrix X); b Root Mean Standard Error of Calibration; c Root Mean Standard Error of Cross-Validation; d Similarity criterion; e Average difference between the estimator and real group during the calibration; f Average difference between the estimator and real group during the cross-calibration; g Coefficient of correlation between the real and predicted group during the calibration; h Coefficient of correlation between the real and predicted group during the cross-validation; i Equation from the linear regression; j Total number of points / Degrees of Freedom. Figure 6 illustrates the regression modeling described in Table 2, where: green curves fit the ideal linear relationship between the measured and predicted physicochemical characteristic based on the sensory attributes; and red curves provided the best fit (real linear relationship) between the measured (r 2 cal ) and cross-validation models (r 2 CV ), the low bias and CV bias values together with the predicted physicochemical characteristic based on the sensory attributes. Therefore, the proximity between the green and red regression curves helped to reach the models quality. In general, despite of the relatively elevate errors achieved on RMSEC and RMSECV methods, the r 2 on calibration proximity between the RMSEC and RMSECV values (similarity criterion) indicated well-adjusted models. However, source of experimental error should be strongly considered in future actions in order to improve the models quality. 4. Discussion The quality of fruits is determined by fundamental aspects ultimately related to consumers' perception: microbiological safety, and physical, chemical, and sensory attributes (Dutcosky 2013). ‘Rama Forte’ persimmons treated with CO 2 had a change in the astringency loss and ripening processes after 4 to 5 days of treatment. The first days were characterized by high flesh firmness, yellowish skin, bitterness, and astringency. At the final days, attributes such as sweetness, translucency, red-colored skin, and aroma stood out. ‘Rama Forte’ persimmons are a soft-fleshed cultivar. Along fruit ripening, flesh firmness loss is a result of enhanced activity of cell wall hydrolyzing enzymes (Besada et al. 2010; Nakano et al. 2003). CO 2 treatment triggers a gradual breakdown of parenchyma tissues of the persimmon mesocarp, which results in the firmness loss at the final stages of fruit ripening (Sandra Munera et al. 2019; Tessmer et al. 2016). The translucency is normally acquired concomitantly with firmness loss is associated with water soluble pectin content (Candan et al. 2008) and changes in membrane permeability. Translucency is typically acquired alongside a loss of firmness, which is associated with the water-soluble pectin content (Candan et al., 2008) and the changes in membrane permeability. The solubilization of pectic material is related to the hydrolysis of ester bonds and loss of neutral sugars in the chain (Voragen et al. 1995), trough the action of hydrolytic enzymes. The more intense red skin color observed in persimmon fruit during ripening is due to the increase in carotenoid contents (Chen et al. 2016), mainly β-carotene (Giordani et al. 2011), and the gelatinous appearance of some cultivars gives the translucency to the fruit. Ripening processes go along with the biosynthesis of aroma compounds whose mix gives the characteristic aroma of each fruit. The aroma complex also impacts the perception of fruit flavor (Lawless and Heymann 2016). Speeding up fruit ripening by CO 2 deastringency treatment validates the importance of aroma and flavor variables in the last days of evaluations. Persimmons treated with ethanol showed a transition related to ripening and astringency degree at the fourth and fifth days after the deastringency treatment, but less evident in comparison to persimmons treated with CO 2 . The initial days after deastringency treatments were characterized by high flesh firmness and bitterness. After the sixth day (final days), pleasant aroma and flavor, sweetness and translucency were evidenced together with an intensification of skin and flesh color, indicative of the advancement of ripening processes. Preceding trials pointed out that astringency removal with ethanol tended to delay changes in flesh firmness and skin color (Kato 1987; Monteiro et al. 2014; Muñoz 2002; Vitti 2009). Astringency removal with high CO 2 concentrations (beyond 70%) lead to hypoxia and elevated stress to cells. That condition is regulated according to Licausi et al. (2010) and Papdi et al. (2015) by ethylene response factors (ERFs). ERF family transcription factors are important regulators of ethylene-dependent pathways (Müller and Munné-Bosch 2015). Stresses set off by elevated CO 2 concentrations together with ERF´s activation might have contributed to an inverse ripening effect of both deastringency treatments resulting in faster ripening of persimmons treated with CO 2 compared to those treated with ethanol. High CO 2 concentrations (beyond 70%) used for astringency removal can lead to hypoxia and elevated stress in cells. Licausi et al. (2010) and Papdi et al. (2015) have shown that this condition is regulated by ethylene response factors (ERFs), which are important transcription factors for ethylene-dependent pathways (Müller and Munné-Bosch 2015). Elevated concentrations of CO 2 and ERF activation may have contributed to an opposite effect of both deastringency treatments, resulting in faster ripening of persimmons treated with CO 2 compared to those treated with ethanol. Prediction models of astringency removal and sensory quality of astringent persimmons are critical as they allow to infer the optimal period to consume the fruit and, consequently, the available time to transport and commercialize after the deastringency treatments. For the CO 2 treatment (70 %), the translucency that was evaluated in a non-destructive way by the panelists was used to determine the color index and the flesh firmness, both physical attributes. It is important to point out that firmness is generally obtained by a destructive method. The prediction models for the color index and flesh firmness based on translucency presented Pearson's R-values of 0.83 and 0.91, respectively. Munera et al. (2017) determined values of 0.77 and 0.80 to predict flesh firmness using hyperspectral imaging of ‘Rojo Brillante’ persimmons treated with CO 2. Furthermore, previous studies revealed that values of the r 2 CV (percentage of the variance in the dependent variables as Y matrix accounted by the independent variables in X matrix) between 0.50 and 0.65 indicate that more than 50 % of the variance in Y was accounted by variance in X; between 0.66 and 0.81 indicate approximate quantitative predictions; between 0.82 and 0.90 reveal satisfactory prediction; and above 0.91 are considered excellent model (Saeys et al. 2005; Williams et al. 2019). Based on these statements, our prediction model for flesh firmness of ‘Rama Forte’ persimmons treated with CO 2 for astringency removal (r 2 CV 0.81) showed approximate quantitative predictions. The proximity of the ideal and real regression curves for the attributes flesh firmness and color index obtained from translucency of persimmons treated with CO 2 indicates a good adjustment of the models. ‘Rama Forte’ persimmon is a cultivar of the pollination-variant astringent group (PVA), whose fruit turns juicy when is ripe (Prohort 2016). Traditionally ‘Rama Forte’ persimmons are consumed when the skin color is red, and the flesh is soft and juicy. Prediction models for fruit firmness based on visual evaluations, such as translucency, could be useful for logistics planning, considering the fruit must be firm to be transported, in order to reduce waste and losses along the postharvest handling chain. The equation “Flesh firmness = - 0.8211 x translucency + 7.4025” indicated that translucency close to 8 (scale from 1 = absent/less intense to 9 = very intense, Figure S2) corresponds to persimmons with flesh firmness of 1 N, i. e. , juicy and ready to eat persimmons. That firmness value was reached on the fifth day after astringency removal. On the other hand, translucency values close to 2 are indicative of flesh firmness values in the range of 6 N. Lower translucency scores and, consequently, higher flesh firmness values that were determined up to the third day are ideal for the distribution logistics of persimmons. The ethanol treatment (1.7 mL kg -1 ) permitted establishing a prediction model of astringency index from a destructive analysis method using the attribute astringency. The prediction model presented a Pearson's R-value of 0.75 and r 2 cv of 0.55, suggesting that more than 50 % of the variance of the astringency index is explained by the variation of the sensory attributes aroma and astringency. Establishing a prediction model for astringency loss of astringent persimmons is of great commercial importance since it is an attribute inherent to persimmon quality and for that reason it has been sought through methods such as NIR (near infrared) and hyperspectral images (Baek et al. 2023; Cortés et al. 2017; Sandra Munera et al. 2017, 2019; Zhu et al. 2020). However, constructing a suitable predictive model from simpler analytical methods may be more cost-effective. The absence of an adequate correlation between the sensory astringency and astringency index with the proanthocyanidins content has been already reported by Amorim et al. (2020) and Braga et al. (Braga et al. 2021). Amorim et al. (2020) suggested that other compounds, beyond proanthocyanidins, might be present in the sensory perception of astringency. Braga et al. (2021) demonstrated that cinnamoyl glucoside, a phenolic acid, and anacardic acids have a positive correlation with the sensory perception of astringency in cashews ( Anacardium occidentale ). These reports, along with the results of our study, provide evidence that proanthocyanidins alone may not fully represent astringency and reinforce the need for more studies to understand the involvement of other metabolites in the sensory perception of persimmon astringency. 5. Conclusion A prediction model for a destructive analysis method can be developed by correlating sensory and physicochemical attributes. The developed model is suitable for predicting flesh firmness from visual assessment of translucency of CO 2 -treated 'Rama Forte' persimmons. The low correlation between sensory astringency and astringency index with proanthocyanidins quantification is an indication that other metabolites are involved in the sensory astringency perception. Additional research is necessary to identify the compounds involved in astringency to develop predictive models for this attribute in persimmons. Declarations Founding sources The authors acknowledge to the Brazilian National Council for Scientific and Technological Development (CNPq) for a research grant [131725/2017-3], the Brazilian Agricultural Research Corporation [Project 02.14.03.011.00.00] and the Cearense Foundation for Scientific and Technological Development Support (FUNCAP) [01986340/2021]. Author Contribution Catherine Amorim: Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing. Elenilson Godoy Alves Filho: Formal analysis, Writing - Original Draft. Deborah Santos Garruti: Conceptualization, Methodology, Validation. Renar João Bender: Validation, Writing - Review & Editing. Lucimara Rogéria Antoniolli: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition, Resources. Statements and Declarations Competing Interests: The authors declare no competing interests. References Akyldiz, A., Aksay, S., Benli, H., Kiroǧlu, H., & Fenercioğlu, H. (2004). Determination of changes in some characteristics of persimmon during dehydration at different temperatures. Journal of Food Engineering , 65 (1), 95–99. https://doi.org/10.1016/j.jfoodeng.2004.01.001 Amorim, C., Alves Filho, E. G., Rodrigues, T. H. S., Bender, R. J., Canuto, K. M., Garruti, D. S., & Antoniolli, L. R. (2020). Volatile compounds associated to the loss of astringency in ‘Rama Forte’ persimmon fruit. Food Research International , 136 , 1–10. https://doi.org/10.1016/j.foodres.2020.109570 Amorim, C., Antoniolli, L. R., Orsi, B., & Kluge, R. A. (2023). Advances in metabolism and genetic control of astringency in persimmon (Diospyros kaki Thunb.) fruit: A review. Scientia Horticulturae , 308 (27), 111561. https://doi.org/10.1016/j.scienta.2022.111561 Antoniolli, L. R., Castro, P. R. de C. e, Kluge, R. A., & Filho, J. A. S. (2000). Remoção da adstringência de frutos de caquizeiro ‘Giombo’ sob diferentes períodos de exposição ao vapor de álcool etílico. Pesquisa Agropecuária Brasileira , 35 (10), 2083–2091. Arnal, L., & Del Río, M. A. (2003). Removing astringency by carbon dioxide and nitrogen-enriched atmospheres in persimmon fruit cv. “Rojo brillante.” Journal of Food Science , 68 (4), 1516–1518. https://doi.org/10.1111/j.1365-2621.2003.tb09676.x Arslan, S., & Bayrakci, S. (2016). Physicochemical, functional, and sensory properties of yogurts containing persimmon. Turkish Journal of Agriculture and Forestry , 40 , 68–74. https://doi.org/10.3906/tar-1406-150 Baek, M. W., Choi, H. R., Hwang, I. G., Tilahun, S., & Jeong, C. S. (2023). Prediction of tannin content and quality parameters in astringent persimmons from visible and near-infrared spectroscopy. Frontiers in Plant Science , 14 . https://doi.org/10.3389/fpls.2023.1260644 Ben-Arie, R., & Sonego, L. (1993). Temperature affects astringency removal and recurrence in persimmon. Journal of Food Science , 58 , 1397–1400. Besada, C., Jackman, R. C., Olsson, S., & Woolf, A. B. (2010). Response of ‘Fuyu’ persimmons to ethylene exposure before and during storage. Postharvest Biology and Technology , 57 (2), 124–131. https://doi.org/10.1016/j.postharvbio.2010.03.002 Braga, D. C., Alves Filho, E. G., Ribeiro, P. R. V., Araújo, Í. M. da S., Brito, E. S. de, & Garruti, D. dos S. (2021). Multivariate correlation of the astringency sensory perception with the phenolic profiling of cashew apple genotypes. Food Bioscience , 41 (February). https://doi.org/10.1016/j.fbio.2021.100931 Candan, A. P., Graell, J., & Larrigaudière, C. (2008). Roles of climacteric ethylene in the development of chilling injury in plums. Postharvest Biology and Technology , 47 (1), 107–112. https://doi.org/10.1016/j.postharvbio.2007.06.009 Chen, J., Du, J., Ge, Z., Zhu, W., Nie, R., & Li, C. (2016). Comparison of sensory and compositions of five selected persimmon cultivars (Diospyros kaki L.) and correlations between chemical components and processing characteristics. Journal of Food Science and Technology , 53 (1597–1607). https://doi.org/10.1007/s13197-015-2102-y Chen, W., Xiong, Y., Xu, L., Zhang, Q., & Luo, Z. (2017). An integrated analysis based on transcriptome and proteome reveals deastringency-related genes in CPCNA persimmon. Scientific Reports , 7 (44671), 1–18. https://doi.org/10.1038/srep44671 Cortés, V., Rodríguez, A., Blasco, J., Rey, B., Besada, C., Cubero, S., et al. (2017). Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy. Journal of Food Engineering , 204 , 27–37. https://doi.org/10.1016/j.jfoodeng.2017.02.017 Das, P. R., & Eun, J.-B. (2021). Removal of astringency in persimmon fruits (Diospyros kaki) subjected to different freezing temperature treatments. Journal of Food Science and Technology , 58 , 3154–3163. https://doi.org/10.1007/s13197-020-04818-3 Dutcosky, S. D. (2013). Análise sensorial de alimentos (4th ed.). Curitiba: PUCPRESS. Edagi, F. K., Chiou, D. G., Terra, F. de A. M., Sestari, I., & Kluge, R. A. (2009). Remoção da adstringência de caquis “Giombo” com subdosagens de etanol. Ciência Rural , 39 (7), 2022–2028. https://doi.org/10.1590/s0103-84782009005000165 El-Sayed, H. A. (2017). Production of Healthy Yoghurt Fortified With Kaki Juice (Persimmon). Egyptian Journal of Food Science , 45 (1), 179–190. Farina, V., Bianco, R. Lo, & Mazzaglia, A. (2019). Evaluation of Late-Maturing Peach and Nectarine Fruit Quality by Chemical, Physical, and Sensory Determinations. Agriculture , 189 (9), 2–11. https://doi.org/https://doi.org/10.3390/agriculture9090189 Filho, E. G. A., Silva, L. M. A., Wurlitzer, N. J., Fernandes, F. A. N., Fonteles, T. V., Rodrigues, S., & Brito, E. S. de. (2020). An integrated analytical approach based on NMR, LC–MS and GC–MS to evaluate thermal and non-thermal processing of cashew apple juice. Food Chemistry , 309 (125761). https://doi.org/10.1016/j.foodchem.2019.125761 Gazit, S., & Levy, Y. (1963). Adstringency and its removal in persimmon. Israel Journal of Agricultural Research , 13 (3), 125–132. Giordani, E., Doumett, S., Nin, S., & Del Bubba, M. (2011). Selected primary and secondary metabolites in fresh persimmon (Diospyros kaki Thunb.): A review of analytical methods and current knowledge of fruit composition and health benefits. Food Research International , 44 (7), 1752–1767. https://doi.org/10.1016/j.foodres.2011.01.036 Ito, S. (1971). The persimmon. In A. C. Hulme (Ed.), The biochemistry of fruits and their products (Vol. 2, pp. 281–301). London: Academic Press. Jin, R., Zhu, Q. G., Shen, X. Y., Wang, M. M., Jamil, W., Grierson, D., et al. (2018). DkNAC7, a novel high-CO 2 /hypoxia-induced NAC transcription factor, regulates persimmon fruit de-astringency. PLoS ONE , 13 (3). https://doi.org/10.1371/journal.pone.0194326 Kato, K. (1987). Astringency Removal and Ripening as Related to Temperature during the de-Astringency by Ethanol in Persimmon Fruits. Journal of the Japanese Society for Horticultural Science , 55 (4), 498–509. https://doi.org/10.2503/jjshs.55.498 Kato, K. (1990). Astringency Removal and Ripening in Persimmons Treated with Ethanol and Ethylene. Hortscience , 25 (2), 205–207. Lawless, H. T. , & Heymann, H. (2016). Sensory evaluation of food . New York: Springer. López-Camelo, A. F., & Gómez, P. A. (2004). Comparison of color ripening. Horticultura Brasileira , 22 (3), 534–537. https://doi.org/10.1590/S0102-05362004000300006 MacFie, H. J., Bratchell, N., Greenhoff, K., & Vallis, L. V. (1989). Designs to balance the effect of order of presentation and first‐order carry‐over effects in hall tests. Journal of Sensory Studies , 4 (2), 129–148. https://doi.org/10.1111/j.1745-459X.1989.tb00463.x Martineli, M., Rezende, C. M. de, Fonseca, M. J. de O., Soares, A. G., & Deliza, R. (2019). Packagings for the transportation of persimmon and their effects on sensory characteristics. Pesquisa Agropecuária Brasileira , 54 , 1–10. https://doi.org/0.1590/S1678-3921.pab2019.v54.00082. Matsuo, T., & Itoo, S. (1982). A model experimente for de-astringency in persimmon fruit with high carbon dioxide treatment: in vitro gelation of kaki-tannin by reacting with acetaldehyde. Agricultural and Biological Chemistry , 46 (3), 683–689. https://doi.org/10.1080/00021369.1982.10865131 Matsuo, Tomoaki, Shinohara, J. I., & Ito, S. (1976). An improvement on removing astringency in persimmon fruits by carbon dioxide gas. Agricultural and Biological Chemistry , 40 (1), 215–217. https://doi.org/10.1080/00021369.1976.10862021 Milczarek, R. R., Liang, P. S., Wong, T., Augustine, M. P., Smith, J. L., Woods, R. D., et al. (2019). Nondestructive determination of the astringency of pollination-variant persimmons (Diospyros kaki) using near-infrared (NIR) spectroscopy and nuclear magnetic resonance (NMR) relaxometry. Postharvest Biology and Technology , 149 , 50–57. https://doi.org/10.1016/j.postharvbio.2018.11.006 Milczarek, R. R., Woods, R. D., LaFond, S. I., Breksa, A. P., Preece, J. E., Smith, J. L., et al. (2018). Synthesis of descriptive sensory attributes and hedonic rankings of dried persimmon (Diospyros kaki sp.). Food Science and Nutrition , 6 (1), 124–136. https://doi.org/10.1002/fsn3.537 Min, T., Fang, F., Ge, H., Shi, Y. N., Luo, Z. R., Yao, Y. C., et al. (2014). Two novel anoxia-induced ethylene response factors that interact with promoters of deastringency-related genes from persimmon. PLoS ONE , 9 (5), 1–8. https://doi.org/10.1371/journal.pone.0097043 Mishra, P., Roger, J.-M., Jouan-Rimbaud-Bouveresse, D., Biancolillo, A., Marini, F., Nordon, A., & Rutledge, D. N. (2021). Recent trends in multi-block data analysis in chemometrics for multi-source data integration. Trends in Analytical Chemistry , 137 (116206). https://doi.org/10.1016/j.trac.2021.116206 Monteiro, M. F., Edagi, F. K., Majado, M., Sasaki, F. C., Saavedra, J., & Kluge, R. A. (2014). Remoção da adstringência de caqui ‘Giombo’ Com etanol em Doses e tempos diferentes, 15 , 160–167. https://www.redalyc.org/articulo.oa?id=81333269006 Moskowitz, H. R. (1983). Product testing and sensory evaluation of foods: Marketing and R & D approaches . Westport: Food and Nutrition Press. Müller, M., & Munné-Bosch, S. (2015). Ethylene Response Factors: A Key Regulatory Hub in Hormone and Stress Signaling. Plant Physiology , 169 (1), 32–41. https://doi.org/10.1104/pp.15.00677 Munera, S, Blasco, J., Cubero, S., Besada, C., Salvador, A., Talens, P., & Aleixos, N. (2016). Maturity assessment of “Rojo Brillante” persimmon by Hyperspectral Imaging. In CIGR - International Commission of Agricultural and Biosystems Engineering (pp. 26–29). Aarhus. Munera, Sandra, Aleixos, N., Besada, C., Gómez-Sanchis, J., Salvador, A., Cubero, S., et al. (2019). Discrimination of astringent and deastringed hard ‘Rojo Brillante’ persimmon fruit using a sensory threshold by means of hyperspectral imaging. Journal of Food Engineering , 263 (January), 173–180. https://doi.org/10.1016/j.jfoodeng.2019.06.008 Munera, Sandra, Besada, C., Blasco, J., Cubero, S., Salvador, A., Talens, P., & Aleixos, N. (2017). Astringency assessment of persimmon by hyperspectral imaging. Postharvest Biology and Technology , 125 , 35–41. https://doi.org/10.1016/j.postharvbio.2016.11.006 Muñoz, V. R. de S. (2002). Destanização do caqui (Diospyrus kaki L .) “Rama Forte.” Universidade Estadual de Campinas - Faculdade de Engenharia Agrícola. Nakano, R., Ogura, E., Kubo, Y., & Inaba, A. (2003). Ethylene Biosynthesis in Detached Young Persimmon Fruit Is Initiated in Calyx and Modulated by Water Loss from the Fruit. Plant Physiology , 131 (1), 276–286. https://doi.org/10.1104/pp.010462 Nelson, D. L., & Cox, M. M. (2014). Princípios de Bioquímica de Lehninger (6th ed.). Porto Alegre: Artmed. Nicoläi, B. M., Defraeye, T., De Ketelaere, B., Herremans, E., Hertog, M. L. A. T. M., Saeys, W., et al. (2014). Nondestructive measurement of fruit and vegetable quality. Annual Review of Food Science and Technology , 5 (1), 285–312. https://doi.org/10.1146/annurev-food-030713-092410 Novillo, P., Gil, R., Besada, C., & Salvador, A. (2015). Astringency removal of “Rojo Brillante” persimmon by combining CO2 and ethanol application. Acta Horticulturae , 1079 , 599–604. https://doi.org/10.17660/ActaHortic.2015.1079.81 Novillo, P., Salvador, A., Magalhaes, T., & Besada, C. (2014). Deastringency treatment with CO2 induces oxidative stress in persimmon fruit. Postharvest Biology and Technology , 92 , 16–22. https://doi.org/10.1016/j.postharvbio.2013.12.019 Programa Brasileiro para modernização da Horticultura. (2009). Normas de classificação: caqui . Prohort. (2016). Panorama do caqui comercializado no ETSP-Ceagesp. São Paulo. http://www.hortibrasil.org.br/2016-06-03-10-48/decidindo-o-futuro-do-caqui.html?showall=&start=05. Accessed 26 November 2023 Prohort. (2018). Programa Brasileiro de Modernização do Mercado de Hortigranjeiro. CEAGESP . http://www.ceasa.gov.br/. Accessed 26 June 2023 Saeys, W., Mouazen, A. M., & Ramon, H. (2005). Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy. Biosystems Engineering , 91 (4), 393–402. https://doi.org/10.1016/j.biosystemseng.2005.05.001 Salvador, A., Arnal, L., Besada, C., Larrea, V., Hernando, I., Perez-Munuera, I., & Pérez-Munuera, I. (2008). Reduced effectiveness of the treatment for removing astringency in persimmon fruit when stored at 15 °C: physiological and microstructural study. Postharvest Biology and Technology , 49 (3), 340–347. https://doi.org/10.1016/j.postharvbio.2008.01.015 Salvador, A., Arnal, L., Besada, C., Larrea, V., Quiles, A., & Pérez-Munuera, I. (2007). Physiological and structural changes during ripening and deastringency treatment of persimmon fruit cv. ‘Rojo Brillante.’ Postharvest Biology and Technology , 46 (2), 181–188. https://doi.org/10.1016/j.postharvbio.2007.05.003 Sanchís, E., Mateos, M., & Pérez-Gago, M. B. (2016). Physicochemical, sensory, and nutritional quality of fresh-cut “Rojo Brillante” persimmon affected by maturity stage and antibrowning agents. Food Science and Technology International , 22 (7), 1–13. https://doi.org/10.1177/1082013216629262 Sato, A., & Yamada, M. (2016). Persimmon breeding in Japan for pollination-constant non-astringent (PCNA) type with marker-assisted selection. Breeding Science , 66 (1), 60–68. https://doi.org/0.1270/jsbbs.66.60 Siddiq, R., Auras, R., Siddiq, M., Dolan, K. D., & Harte, B. (2020). Effect of modified atmosphere packaging (MAP) and NatureSeal® treatment on the physico-chemical, microbiological, and sensory quality of fresh-cut d’Anjou pears. Food Packaging and Shelf Life , 23 , 1–6. https://doi.org/https://doi.org/10.1016/j.fpsl.2019.100454 Stavang, J. A., Freitag, S., Foito, A., Verral, S., Heide, O. M., Stewart, D., & Sønsteby, A. (2015). Raspberry fruit quality changes during ripening and storage as assessed by colour, sensory evaluation and chemical analyses. Scientia Horticulturae , 195 (12), 216–225. https://doi.org/https://doi.org/10.1016/j.scienta.2015.08.045 Sucupira, N. R., AlvesFilho, E. G., Silva, L. M. A., Brito, E. S. de, Wurlitzer, N. J., & Sousa, P. H. M. (2017). NMR spectroscopy and chemometrics to evaluate different processing of coconut water. Food Chemistry , 2016 , 2017–224. https://doi.org/10.1016/j.foodchem.2016.08.035 Taira, S. (1996). Astringency in persimmon. In H. F. Linkskens & J. F. Jackson (Eds.), Modern methods of plant analysis (Vol. 18, pp. 97–110). Berlin: Springer. https://doi.org/10.1007/978-3-642-79660-9_6 Terra, F. de A. M., Edagi, F. K., Sasaki, F. F. C., Frassetto Filho, M. E., Silva, M. M. da, Giro, B., et al. (2014). Aplicação do 1-metilciclopropeno e sua influência no processo de remoção da adstringência com etanol em caqui “Giombo” refrigerado. Ciência Rural , 44 (2), 210–216. https://doi.org/10.1590/s0103-84782014000200003 Tessmer, M. A., Appezzato-da-Glória, B., & Kluge, R. A. (2018). Astringency reduction using ethanol-associated to the storing under refrigeration at 5 o C promotes physiological and structural alterations in ‘Giombo’ persimmons. Comunicata Scientiae , 9 (3), 49-456. https://doi.org/https://doi.org/10.14295/cs.v9i3.2014 Tessmer, M. A., Appezzato-da-Glória, B., & Kluge, R. A. (2019). Evaluation of storage temperatures to astringency ‘Giombo’ persimmon: Storage at 1 °C combined with 1-MCP is recommended to alleviate chilling injury. Scientia Horticulturae , 257 , 1–7. https://doi.org/https://doi.org/10.1016/j.scienta.2019.108675 Tessmer, M. A., Besada, C., Hernando, I., Appezzato-da-Glória, B., Quiles, A., & Salvador, A. (2016). Microstructural changes while persimmon fruits mature and ripen. Comparison between astringent and non-astringent cultivars. Postharvest Biology and Technology , 120 , 52–60. https://doi.org/10.1016/j.postharvbio.2016.05.014 Tziotzios, G., Pantazi, X. E., Paraskevas, C., Tsitsopoulos, C., Valasiadis, D., Nasiopoulou, E., et al. (2024). Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits. Horticulturae , 10 (3), 251. https://doi.org/10.3390/horticulturae10030251 Vitti, D. C. (2009). Destanização e armazenamento refrigerado de caqui ‘ Rama Forte ’ em função da época de colheita (Tese). Universidade de São Paulo - Escola Superior de Agricultura “ Luiz de Queiroz .” Voragen, G. J., Pilnik, W., Thibault, J. F., Axelos, M. A. V., & Renard, C. M. G. C. (1995). Pectins. In Stephen A. M. (Ed.), Food polysaccharides and their applications . New York: Marcel Dekker Inc. Williams, P., Manley, M., & Antoniszyn, J. (2019). Near infrared technology: getting the best out of light . African Sun Media. Xie, D. Y., & Dixon, R. A. (2005). Proanthocyanidin biosynthesis - still more questionsthan answers? Phytochemistry , 66 , 2127–2144. https://doi.org/10.1016/j.phytochem.2005.01.008 Xu, J. C., Ding, J. G., Gan, J. P., Mo, R. L., Xu, L. Q., Zhang, Q. L., & Luo, Z. R. (2017). ALDH2 genes are negatively correlated with natural deastringency in Chinese PCNA persimmon (Diospyros kaki Thunb.). Tree Genetics and Genomes , 13 (122), 1–9. https://doi.org/10.1007/s11295-017-1207-z Yamada, M., Taira, S., Ohtsuki, M., Sato, A., Iwanami, H., Yakushiji, H., et al. (2002). Varietal differences in the ease of astringency removal by carbon dioxide gas and ethanol vapor treatments among Oriental astringent persimmons of Japanese and Chinese origin. Scientia Horticulturae , 94 (1–2), 63–72. https://doi.org/10.1016/S0304-4238(01)00367-3 Zhang, P., Wu, Q., Wang, Y., Huang, Y., Xie, M., & Fan, L. (2024). Rapid Detection of Tannin Content in Wine Grapes Using Hyperspectral Technology. Life , 14 (3), 416. https://doi.org/10.3390/life14030416 Zhu, W., Khalifa, I., Wang, R., & Li, C. (2020). Persimmon highly galloylated‐tannins in vitro mitigated α‐amylase and α‐glucosidase via statically binding with their catalytic‐closed sides and altering their secondary structure elements. Journal of Foof Biochemistry , 44 (7), 1–12. https://doi.org/10.1111/jfbc.13234 Additional Declarations No competing interests reported. Supplementary Files Suplementardata.pdf Cite Share Download PDF Status: Posted 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. 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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-4217960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288922454,"identity":"bee2ad4a-be60-4937-8c6b-f85430227dde","order_by":0,"name":"Catherine Amorim","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Amorim","suffix":""},{"id":288922455,"identity":"1ffbb33d-535c-4cfc-9454-026fce97f945","order_by":1,"name":"Elenilson Godoy Alves Filho","email":"","orcid":"","institution":"Universidade Federal do Ceará","correspondingAuthor":false,"prefix":"","firstName":"Elenilson","middleName":"Godoy Alves","lastName":"Filho","suffix":""},{"id":288922456,"identity":"57c1624c-1811-422a-9c8a-375c4eeba8e2","order_by":2,"name":"Deborah Santos Garruti","email":"","orcid":"","institution":"Embrapa Agroindústria Tropical","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"Santos","lastName":"Garruti","suffix":""},{"id":288922458,"identity":"16d472bb-0967-43f2-a90e-3ebf4fbd24ce","order_by":3,"name":"Renar João Bender","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Renar","middleName":"João","lastName":"Bender","suffix":""},{"id":288922459,"identity":"c38b5e16-02b5-4e23-bce9-b721ca9fb1c0","order_by":4,"name":"Lucimara Rogéria Antoniolli","email":"data:image/png;base64,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","orcid":"","institution":"Embrapa Uva e Vinho","correspondingAuthor":true,"prefix":"","firstName":"Lucimara","middleName":"Rogéria","lastName":"Antoniolli","suffix":""}],"badges":[],"createdAt":"2024-04-04 12:41:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4217960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4217960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54588177,"identity":"bd8769eb-243e-4089-88e9-17e87582f24a","added_by":"auto","created_at":"2024-04-12 16:23:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4314336,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) results from the multiblock evaluation of the physicochemical and sensory datasets of the ‘Rama Forte’ persimmon fruit under CO\u003csub\u003e2\u003c/sub\u003e deastringency treatment on the subsequent seven days of treatment: A) PC1 scores; B) PC1 loadings. Legend: ROCS (reddish orange color of the skin); OCF (orange color of the flesh); PA (proanthocyanidins g 100 g\u003csup\u003e-1\u003c/sup\u003e FW); C (chroma) and L* (lightness)\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/c87543f1b8e0d6f9a66072ac.jpg"},{"id":54588121,"identity":"07ecb22e-520a-48dc-ad1e-322063c56a02","added_by":"auto","created_at":"2024-04-12 16:22:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4767580,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) results from the multiblock evaluation of the sensory and physicochemical datasets of the ‘Rama Forte’ persimmon fruit under ethanol deastringency treatment on the subsequent eight days of treatment: A) PC1 × PC3 scores coordinate system; B) respective PC1 × PC3 loadings. Legend: ROCS (reddish orange color of the skin); OCF (orange color of the flesh); PA (proanthocyanidins g 100 g\u003csup\u003e-1\u003c/sup\u003e FW); C (chroma) and L* (lightness)\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/92edbbb2cd7293dc93de9b8c.jpg"},{"id":54588118,"identity":"38d11675-3124-4394-b12d-a5b73a02be5d","added_by":"auto","created_at":"2024-04-12 16:22:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5128067,"visible":true,"origin":"","legend":"\u003cp\u003eHotelling T\u003csup\u003e2\u003c/sup\u003e × Q residuals from the PLS-DA of the ‘Rama Forte’ persimmon fruit under CO\u003csub\u003e2\u003c/sub\u003e (A) and ethanol (B) treatments. Most relevant loadings for the samples discrimination based on the CO\u003csub\u003e2\u003c/sub\u003e (C) and ethanol (D) treatments achieved by the Variables Importance in Projection (VIP) analysis. Legend: ROCS (reddish orange color of the skin); OCF (orange color of the flesh); PA (proanthocyanidins g 100 g\u003csup\u003e-1\u003c/sup\u003e FW); C (chroma) and L* (lightness)\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/c47b6389f5523ef8358ccd2e.jpg"},{"id":54588119,"identity":"ca2e944c-27de-4391-b020-8517376f2dbf","added_by":"auto","created_at":"2024-04-12 16:22:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6362653,"visible":true,"origin":"","legend":"\u003cp\u003eANOVA results of the reddish orange color of the skin (A), orange color of the flesh (B), translucency (C), juiciness (D), sensory firmness (E), crispness (F), color index (G), firmness (N) (H), C* (I) and L* (J) of the ‘Rama Forte’ persimmon fruit under the CO\u003csub\u003e2\u003c/sub\u003e treatment. Legend: ROCS (reddish orange color of the skin); OCF (orange color of the flesh); C (chroma), L* (lightness); X axis (days after deastringency treatment)\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/0a58c497f5e6780cd182e26c.jpg"},{"id":54588123,"identity":"3dcb13c6-1730-42e5-bba8-c3c913d22ddc","added_by":"auto","created_at":"2024-04-12 16:22:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5649634,"visible":true,"origin":"","legend":"\u003cp\u003eANOVA results of the characteristic flavor (A), sweetness (B), bitterness (C), sensory astringency (D), proanthocyanidins (g 100 g\u003csup\u003e-1\u003c/sup\u003e FW) (E), astringency index (1-5) (F) and firmness (N) (G) of ‘Rama Forte’ persimmon fruit under the ethanol treatment. Legend: X axis (days after deastringency treatment)\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/feb6847aae780ad730940c71.jpg"},{"id":54588172,"identity":"997df877-60b6-42c2-9ae5-bce8012d8ebf","added_by":"auto","created_at":"2024-04-12 16:23:12","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7646781,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate regression analysis to predict the firmness (A) and color index (B) of the ‘Rama Forte’ persimmon fruit during the CO\u003csub\u003e2\u003c/sub\u003e deastringency treatment, and the astringency index (C) on the ethanol treatment. Green curves fit the ideal linear relationship among the measured and predicted physicochemical characteristics based on the sensory attribute and red curves provided the best fit (real linear relationship)\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/843461837883084aff4f2875.jpg"},{"id":54980640,"identity":"b25b6cc0-3651-437b-b670-1dfc140049ff","added_by":"auto","created_at":"2024-04-19 13:55:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/e6f59a7c-0310-49eb-9a31-21ba27e70b08.pdf"},{"id":54588168,"identity":"05a68936-0840-474f-8bc2-1b3e0e78967b","added_by":"auto","created_at":"2024-04-12 16:23:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":310199,"visible":true,"origin":"","legend":"","description":"","filename":"Suplementardata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4217960/v1/afc28ff3039713eb94291095.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive modeling and correlation between the sensory and physicochemical attributes in ‘Rama Forte’ astringent persimmon fruit","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eBrazil is the major persimmon (\u003cem\u003eDiospyros kaki\u003c/em\u003e L.) producer of the Americas and is fifth at global level. ‘Rama Forte’, the most important cultivar in the country (Prohort 2018), belongs to the pollination-variant group according to the classification of Ito (1971)\u003cem\u003e\u0026nbsp;i. e.\u003c/em\u003e, the fruit need to undergo astringency removal processes to become edible. The astringency is caused by the presence of soluble tannins in the mesocarp tissues, also termed proanthocyanidins, which are a class of phenolic compounds. Those compounds leave a sensation of dryness when fruit are ingested (Taira 1996; Xie and Dixon 2005). The most common methods for removing astringency are high CO\u003csub\u003e2\u003c/sub\u003e atmospheres or application of ethanol or ethylene (Amorim et al. 2023; Jin et al. 2018; Kato 1990; Tomoaki Matsuo et al. 1976; Min et al. 2014; Novillo et al. 2014; Tessmer et al. 2019). Acetaldehyde is a metabolite synthesized under anaerobic respiration (Nelson and Cox 2014) and is responsible for the polymerization and insolubilization of proanthocyanidins, reducing astringency (W. Chen et al. 2017; T. Matsuo and Itoo 1982; Xu et al. 2017). Along the deastringency process with exogenous ethanol there is an increase in ethanol by the activation of the enzyme alcohol dehydrogenase leading to the synthesis of acetaldehyde (Ben-Arie and Sonego 1993; Yamada et al. 2002). When applying CO\u003csub\u003e2\u003c/sub\u003e concentrations beyond 60 % as an astringency removal agent, the enzyme pyruvate dehydrogenase is activated converting pyruvate to acetaldehyde (W. Chen et al. 2017). During this reaction the enzyme alcohol dehydrogenase is also activated converting acetaldehyde to ethanol (Taira 1996).\u003c/p\u003e\n\u003cp\u003eThe literature on astringency removal of persimmons predominantly refers to aspects of deastringency and fruit quality with regards to physicochemical characteristics (Antoniolli et al. 2000; Arnal and Del Río 2003; Edagi et al. 2009; Novillo et al. 2015; Sato and Yamada 2016; Terra et al. 2014). Tessmer et al. (2018, 2019) studied, on the other hand, the cellular anatomical aspects of astringency removal. Nonetheless, there is little information on sensory perception of persimmons that have been treated for astringency removal. Even though physicochemical methods are largely employed to quantify metabolites and specific fruit characteristics they are unable to represent human perception. Therefore, sensory assessments are an important tool to estimate consumer perception of physical and chemical changes in fruit.\u003c/p\u003e\n\u003cp\u003eSalvador et al. (2007, 2008) and Munera et al. (2019) used sensory evaluations as a tool to estimate astringency in 'Rojo Brillante’ persimmons. Akyldiz et al. (2004) used sensory evaluation to determine flavor, astringency and skin color of dehydrated ‘Türkay’ persimmons. Sanchís et al. (2016) also applied sensory evaluation together with physicochemical attributes to evaluate the quality of minimally processed ‘Rojo Brillante’ persimmons after anti-browning treatment. Das and Eun (2021) tested sensory astringency and evaluated the quality attributes of persimmons subjected to freezing temperatures. Arslan and Bayrakci (2016), El-Sayed (2017) and Milczarek et al. (2018) used sensory analysis to evaluate food products from processed persimmons. Martineli et al. (2019)\u0026nbsp;reported the acceptability of ‘Rama Forte’ persimmons stored in three different packages.\u003c/p\u003e\n\u003cp\u003eComparative approaches between physicochemical and sensory attributes were tested in raspberries (Stavang et al. 2015), in minimally processed ´d`Anjou´ pears (Siddiq et al. 2020) and peaches and nectarines (Farina et al. 2019). Correlations linking sensory aspects with instrumental analysis may help not only in validation, but also in assembling predictive models employing simple analysis criteria. Non-destructive prediction models based on standard analysis methods might be advantageous and facilitate attainment and repeatability of results. Prediction models from non-destructive persimmon evaluations have been developed using near infrared (NIR) spectroscopy and hyperspectral imaging using six different \u003cem\u003eDiospyros kaki\u003c/em\u003e cultivars (Milczarek et al. 2019). \u0026nbsp;‘Rojo Brillante’ (Munera et al. 2016; Munera et al. 2017), ‘Cheongdo-Bansi’ and ‘Daebong’ (Baek et al. 2023)\u0026nbsp;and other fruits and vegetables (Nicoläi et al. 2014; Tziotzios et al. 2024; Zhang et al. 2024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, 'Rama Forte' persimmons (\u003cem\u003eDiospyros kaki\u0026nbsp;\u003c/em\u003eL.) subjected to two astringency removal processes were used to investigate correlations involving sensory and physicochemical attributes in order to establish prediction models for destructive analysis based on non-destructive methods.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1. Plant Material\u003c/p\u003e\n\u003cp\u003e‘Rama Forte’ (\u003cem\u003eDiospyros kaki\u0026nbsp;\u003c/em\u003eL.) persimmons were harvested at the commercial ripening stage from a private orchard in Antônio Prado, Southern Brazil. The Programa Brasileiro para a Modernização da Horticultura (2009) (Persimmon Classification, Standardization, and Identification Rules) was used to select and standardize the fruit according to size and color: yellow-orange skin color and 6 to 8 cm in diameter.\u003c/p\u003e\n\u003cp\u003e2.2.\u0026nbsp;Experimental procedure\u003c/p\u003e\n\u003cp\u003ePersimmons were kept at room temperature (20 ± 5 °C) and treated with 70 % CO\u003csub\u003e2\u003c/sub\u003e for 18 hours or 1.70 mL Kg\u003csup\u003e-1\u003c/sup\u003e ethanol for 6 hours. Both treatments were applied in hermetic plastic containers. No control treatment was included in the present trial because in three years of previous studies, ´Rama Forte´ persimmons that had not undergone deastringency processes remained firm, astringent, and with an orange skin color (Figure S1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSensory and internal physicochemical changes were evaluated after deastringency treatments on the first day and daily thereafter up to eight days. The treated persimmons were kept at room temperature (20 ± 5 °C).\u003c/p\u003e\n\u003cp\u003eThe trial was carried out in a completely randomized design. For both methods of astringency removal, either CO\u003csub\u003e2\u003c/sub\u003e or ethanol, 30 fruit were evaluated every day. Panelists were provided with persimmon samples in two replicates for each day after deastringency treatments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 Sensory evaluation\u003c/p\u003e\n\u003cp\u003eThe descriptive quantitative method of Stone et al. (1974) was used to train the panelists and to lead the sensory analysis. Approval for our study (147.279/2012) was granted by the Ethics Committee from Ceará State University, Brazil. Fifteen trained panelists were selected for their discriminant power, reproducibility and consensus with the other panel members as recommended by Damasio and Costell (1991). The panelists were selected amongst the staff of the Embrapa - Centro Nacional de Uva e Vinho (Brazilian Agricultural Research Corporation - Grape and Wine) - by applying a questionnaire to identify persimmon consumers, their consumption habits, and the availability to participate in the panel. The panelists were subjected to the Basic Taste and Odor Recognition Tests (ABNT NBR ISO 8586, 2016), and to the Repertory Grid Method to define descriptors (Moskowitz 1983). The terms mentioned by each panelist were grouped and eleven descriptors were established for the evaluation of ‘Rama Forte’ persimmons: reddish orange color of the skin, orange color of the flesh, translucency, aroma, flavor, sweetness, bitterness, astringency, firmness, juiciness, and crispness (Table S1). The panelists were trained with regards to the descriptors and their respective references of intensity in a non-structured 9-cm linear scale anchored at both extremes by the terms absent or not very intense and very intense. The samples, composed of two longitudinal portions of the distal end of the persimmons, were offered in plastic plates labeled with three-digit codes as described in MacFie et al. (1989) together with the respective samples in glassware with lids destined for aroma evaluation.\u003c/p\u003e\n\u003cp\u003e2.4 Physical and chemical measurements\u003c/p\u003e\n\u003cp\u003eSkin color of the persimmons was determined using a Konica/Minolta colorimeter, CR-400 model. The results were expressed as Hue angle, L*, C*, and color index. The color index was calculated as indicated by López-Camelo and Gómez (2004) using the following equation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Color Index = (2000 × a*) / L* × C* \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFlesh firmness was determined using a digital penetrometer (Güss Fruit Texture Analyzer) equipped with an 8 mm plunger of a flat surface. Two measurements were taken from each fruit at two opposite sites at the equatorial area. The results were expressed in Newton (N).\u003c/p\u003e\n\u003cp\u003eProanthocyanidins were evaluated using the Folin-Denis method (Taira 1996) at 725 nm on a spectrophotometer (Carry 60 UV-Vis, Agilent). The results were expressed in g gallic acid 100 g\u003csup\u003e-1\u003c/sup\u003e fresh weight (g gallic acid 100 g\u003csup\u003e-1\u003c/sup\u003e FW). Astringency index was determined using filter paper treated with a ferric chloride 5 % (w/v) solution and the imprint of the halved fruit on the filter paper as described in Gazit and Levy (1963). The imprints were estimated using a 5-point scale: 1 = non astringent, 2 = lightly astringent, 3 = moderately astringent, 4 = astringent e 5 = highly astringent (Gazit and Levy 1963).\u003c/p\u003e\n\u003cp\u003e2.5. Multivariate statistical analyses\u003c/p\u003e\n\u003cp\u003e2.5.1. Unsupervised and data fusion analyses\u003c/p\u003e\n\u003cp\u003eA numerical matrix was separately constructed for the physicochemical and sensory attributes of persimmons subjected to the CO\u003csub\u003e2\u003c/sub\u003e or ethanol\u0026nbsp;deastringency treatments totaling four matrices. The reddish orange color of the skin, orange color of flesh, translucency, juiciness, characteristic aroma, characteristic flavor, sweetness, bitterness, sensory astringency, sensory firmness, and crispness were used as sensory attributes. The color index, proanthocyanidins, astringency index, firmness, hue angle, C* and L* were used as physicochemical characteristics.\u003c/p\u003e\n\u003cp\u003eThe resultant matrices were imported by the PLS Toolbox™ program (version 8.6.2, Eigenvector Research Incorporated, Manson, WA USA) for unsupervised multivariate analysis via Principal Component Analysis (PCA). Before chemometric approaches, the data were autoscaled (mean centered with subsequent variance scaling) and the Singular Value Decomposition (SVD) algorithm was applied to decompose the matrices in scores, loadings, and modeling errors (Amorim et al. 2020).\u003c/p\u003e\n\u003cp\u003eIn order to improve the correlations between the physicochemical and sensory attributes among the samples from different matrices (sensory and physicochemical), the multiblock statistical analysis was further developed. The data scaling by block variance was applied to achieve the same loading strength on the matrices. The relevant information was obtained using the Venetian Blinds as cross-validation method with confidence level of 95 % (Filho et al. 2020; Mishra et al. 2021).\u003c/p\u003e\n\u003cp\u003e2.5.2. Supervised analyses\u003c/p\u003e\n\u003cp\u003eMultivariate classification modeling by PLS-DA (Partial Least Squares Discriminant Analysis) was developed for each matrix (sensory and physicochemical) to achieve the most relevant compounds variability according to each deastringency treatment (CO\u003csub\u003e2\u003c/sub\u003e and ethanol). The persimmon data was also autoscaled and the SIMPLS (Simplified PLS) algorithm was applied to decompose the matrices in scores, loadings and figures of merit. The number of latent variables (LV) was selected based on the statistical parameters described in Table 1. The cross-validation of the models was developed using the Venetian Blind method under 10 splits and blind thickness equal to 1.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Multivariate regression analyses by PLS (Partial Least Squares) were developed to investigate the correlation between the physicochemical and sensory attributes and therefore, to predict a parameter from a destructive method by a non-destructive and simpler method. The sensory persimmon fruit attributes were used as independent variables (X-matrix) and physicochemical attributes as dependent variables (Y-matrix), in order to find the maximum covariance among these datasets. The same SIMPLS algorithm was used on the autoscaled matrices, and the number of LV was selected according to the statistical parameters described in Table 2 (Wold et al., 2001). The robustness of the regression models was further evaluated by the proximity between the ideal and real regression curves from the measured and predicted physicochemical characteristics based on sensory attributes.\u003c/p\u003e\n\u003cp\u003e2.6. Univariate statistical analysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Analysis of variance (ANOVA) single factor was developed to statistically certify the variability of the sensory and physicochemical attributes according to each deastringency treatment (CO\u003csub\u003e2\u003c/sub\u003e and ethanol). The means comparison was developed using Tukey test at significance level of 0.05, and Levene test was used to verify the variance homogeneity among the sample groups based on days after the deastringency treatments (Sucupira et al. 2017).\u003c/p\u003e"},{"header":"3. Results ","content":"\u003cp\u003e3.1. Multivariate exploratory analyses\u003c/p\u003e\n\u003cp\u003eFigures 1 and 2 illustrate the PCA results from multiblock analysis for CO\u003csub\u003e2\u003c/sub\u003e and ethanol treatments respectively, highlighting the variables correlation between the physicochemical and sensory attributes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePersimmons treated with CO\u003csub\u003e2\u003c/sub\u003e ripened faster than those treated with ethanol. Consequently, CO\u003csub\u003e2\u003c/sub\u003e treated fruit became unsuitable for consumption after eight days and could not be evaluated by the panelists. For CO\u003csub\u003e2\u003c/sub\u003e treatment (Fig. 1), the PC1 axis retained the main information for the samples discrimination based on the days after treatment, with 69.11 % of the total variance. Differently, deastringency influenced by ethanol (Fig. 2) was achieved on PC1 and PC3 axes, which retained lower total variance (49.2 %) than the CO\u003csub\u003e2\u003c/sub\u003e treatment. The gradual effect on astringency reduction by ethanol decreased the differentiation among the fruit after treatment and therefore, and more axes (PCs) were necessary to explain the deastringency effect of fruit based on the sensory and physicochemical attributes.\u003c/p\u003e\n\u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e treatment (Fig. 1) clearly evidenced a drastic decrease of the values from the sensory attributes related to bitterness, sensory astringency, sensory firmness and crispness, as well as from the physicochemical characteristics related to the proanthocyanidins, astringency index, firmness, hue angle, C and L* after 3 days of treatment. On the other hand, sensory attributes achieved by the reddish orange color of the skin (ROCS), orange color of the flesh (OCF), skin translucency, juiciness, characteristic aroma, characteristic flavor and sweetness, and the color index (physicochemical attribute) significantly increased after 3 days of treatment. Fruit at the fourth and fifth days showed intermediary variability of all the aforementioned sensory and physicochemical attributes (imprecise samples distribution between negative and positive PC1 scores).\u003c/p\u003e\n\u003cp\u003eDifferently from the CO\u003csub\u003e2\u003c/sub\u003e treatment, the ethanol treatment showed clustering tendencies of the fruit on the PC1 \u0026times; PC3 scores based on the sensory and physicochemical attributes (Fig. 2). Fruit from 0 (immediately after treatment) 1, 2 or 3 days after the deastringency treatment was clustered at negative scores of PC1 and positive of PC3 by the concomitant high values of bitterness and sensory astringency as sensory attributes, and proanthocyanidins, astringency index and firmness as physicochemical attributes. Fruit after 6, 7 and 8 days of the deastringency treatment clustered at positive PC1 scores by high values of the sensory attributes ROCS, OCF, skin translucency, juiciness, characteristic aroma, characteristic flavor, and sweetness, as well as high values of color index as physicochemical attributes. In general, fruit from the 4th and 5th days showed intermediate PC1 and PC3 scores (intersection between two circles), which ranged between the beginning and the ending of the astringency removal process. High values of sensory firmness, crispness, hue angle, C and L* (at negative scores of PC1 and PC3) contributed to evidence the gradual changes according to the days after deastringency treatment with ethanol.\u003c/p\u003e\n\u003cp\u003e3.2. Multivariate classification analysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A supervised classification analysis by PLS-DA was further developed in order to corroborate the samples clustering achieved by PCA evaluations, as well as to highlight the most important sensory and physicochemical attributes related to each deastringency treatment (CO\u003csub\u003e2\u003c/sub\u003e and ethanol) by VIP analysis (Variables Importance for Projection). Therefore, only fruit with clear discrimination based on the exploratory analyses results were selected for evaluation, such as 0, 1, 2, 6 and 7 days after treatment. The samples influence on modeling was verified by Hotelling T\u003csup\u003e2\u003c/sup\u003e and modeling errors by Q residuals, which are illustrated in Figures 3A and 3B for CO\u003csub\u003e2\u003c/sub\u003e and ethanol, respectively. Despite some persimmon fruit showed elevated influence on modeling as well as high modeling error (values above the threshold at 1), they did not negatively influence both modeling (CO\u003csub\u003e2\u003c/sub\u003e and ethanol), according to the statistical parameters described in Table 1.\u003c/p\u003e\n\u003cp\u003eThe variables\u0026rsquo; relevance highlighted by the VIP analysis are illustrated in Figures 3C and 3D for the CO\u003csub\u003e2\u003c/sub\u003e and ethanol treatments, respectively. The reddish orange color of the skin (ROCS), orange color of the flesh (OCF), translucency, juiciness, sensory firmness, crispness, color index, firmness, C and L* were the most relevant variables (higher variability) for the deastringency treatment by CO\u003csub\u003e2\u003c/sub\u003e (Fig. 3C); and the characteristic flavor, sweetness, bitterness, sensory astringency, proanthocyanidins, astringency index and firmness were the most relevant variables for the deastringency treatment by ethanol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Statistical parameters from the classification modeling of the \u0026lsquo;Rama Forte\u0026rsquo; persimmon fruit under the deastringency treatments by CO\u003csub\u003e2\u003c/sub\u003e and ethanol\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"387\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthanol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eLV number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eCaptured variance (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e76.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e57.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eTrue Positive and Sensitivity\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eTrue Negative and Specificity\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eRMSEC\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eRMSECV\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eRMSEC / RMSECV\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eBias\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.20 x 10\u003csup\u003e-16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e3.30 x 10\u003csup\u003e-16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eCV Bias\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.60 x 10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e5.10 x 10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ecal\u003c/sub\u003e\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.50515463917526%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCV\u003c/sub\u003e\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.876288659793815%\"\u003e\n \u003cp\u003e0.87\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\u003e Percent variance captured in X-block (matrix X); \u003csup\u003eb\u003c/sup\u003e Sensitivity on cross-validation; \u003csup\u003ec\u003c/sup\u003e Specificity on cross-validation; \u003csup\u003ed\u003c/sup\u003e Root Mean Standard Error of Calibration; \u003csup\u003ee\u003c/sup\u003e Root Mean Standard Error of Cross-Validation; \u003csup\u003ef\u003c/sup\u003e Similarity criterion; \u003csup\u003eg\u003c/sup\u003e Average difference between the estimator and real group during the calibration; \u003csup\u003eh\u003c/sup\u003e Average difference between the estimator and real group during the cross-calibration; \u003csup\u003ei\u003c/sup\u003e Coefficient of correlation between the real and predicted group during the calibration; \u003csup\u003ej\u003c/sup\u003e Coefficient of correlation between the real and predicted group during the cross-validation.\u003c/p\u003e\n\u003cp\u003e3.3. Univariate statistical analysis\u003c/p\u003e\n\u003cp\u003eAnalysis of variance (ANOVA single factor) was developed considering the highlighted variables by the VIP analysis (Fig. 3) in order to statistically certify their variability according to each deastringency treatment. Therefore, the reddish orange color of the skin (ROCS), orange color of the flesh (OCF), translucency, juiciness, sensory firmness, crispness, color index, firmness, C and L* from the CO\u003csub\u003e2\u003c/sub\u003e treatment; and the characteristic flavor, sweetness, bitterness, sensory astringency, proanthocyanidins, astringency index and firmness from the ethanol treatment were evaluated by ANOVA. The univariate statistical analysis by ANOVA from the CO\u003csub\u003e2\u003c/sub\u003e (Fig. 4) and ethanol (Fig. 5) treatments corroborated the attributes variability from the beginning to the end of both deastringency treatments detected by the multivariate statistical results.\u003c/p\u003e\n\u003cp\u003e3.4. Regression analyses\u003c/p\u003e\n\u003cp\u003eFor regression analysis, all the sensory attributes were tentatively correlated to physicochemical characteristics: for CO\u003csub\u003e2\u003c/sub\u003e treatment the translucence (non-destructive) was better adjusted to the color index (non-destructive) and firmness (destructive); and for ethanol treatment the sensory astringency (destructive) was better adjusted to the astringency index (destructive). The statistical parameters from the y-block (dependent variables) of these better-adjusted models are described in Table 2. Additionally, to the multivariate regressions, univariate regression models were developed considering the selected sensory and physicochemical attributes (based on the models quality) to complement the prediction ability, with the respective statistical parameters (equations and Pearson correlation coefficient) presented in Table 2. The models were better adjusted on CO\u003csub\u003e2\u003c/sub\u003e treatment than the ethanol treatment, which may be related to the gradual parameters variability into the ethanol treatment that decreased the samples differences among the deastringency treatment. This information may be corroborated by comparison of the PCA results illustrated in Figures 1 (CO\u003csub\u003e2\u003c/sub\u003e) and 2 (ethanol).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Statistical parameters from the regression modeling of the \u0026lsquo;Rama Forte\u0026rsquo; persimmon fruit under CO\u003csub\u003e2\u003c/sub\u003e treatment predicting the color index and firmness based on the translucence; and under ethanol treatment predicting the astringency index based on characteristic aroma and sensory astringency together\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.673946957878314%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthanol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.421052631578945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eColor index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.26315789473684%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirmness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.31578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAstringency Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eLV number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eCaptured variance (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eRMSEC\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e3,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e14,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eRMSECV\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e3,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e14,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eRMSEC / RMSECV\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e11,00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eBias\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e3.50 x 10\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e2.10 x 10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e-1.78 x 10\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eCV Bias\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e1.40 x 10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e-1.00 x 10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e1.60 x 10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003ecal\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e0,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCV\u003c/sub\u003e\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e0,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003ePearson\u0026rsquo;s R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e0,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003eEquation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003ey = 0.6945x + 3.4455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003ey = - 0.8211x + 7.4025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003ey = 0.56457x + 1.28773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897035881435258%\"\u003e\n \u003cp\u003en / \u0026nu;\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.024960998439937%\"\u003e\n \u003cp\u003e170 / 172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.648985959438377%\"\u003e\n \u003cp\u003e172 / 170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.429017160686428%\"\u003e\n \u003cp\u003e207 / 205\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\u003e Percent variance captured in X-block (matrix X); \u003csup\u003eb\u003c/sup\u003e Root Mean Standard Error of Calibration; \u003csup\u003ec\u003c/sup\u003e Root Mean Standard Error of Cross-Validation; \u003csup\u003ed\u003c/sup\u003e Similarity criterion; \u003csup\u003ee\u003c/sup\u003e Average difference between the estimator and real group during the calibration; \u003csup\u003ef\u003c/sup\u003e Average difference between the estimator and real group during the cross-calibration; \u003csup\u003eg\u003c/sup\u003e Coefficient of correlation between the real and predicted group during the calibration; \u003csup\u003eh\u003c/sup\u003e Coefficient of correlation between the real and predicted group during the cross-validation; \u003csup\u003ei\u003c/sup\u003e Equation from the linear regression; \u003csup\u003ej\u003c/sup\u003e Total number of points / Degrees of Freedom.\u003c/p\u003e\n\u003cp\u003eFigure 6 illustrates the regression modeling described in Table 2, where: green curves fit the ideal linear relationship between the measured and predicted physicochemical characteristic based on the sensory attributes; and red curves provided the best fit (real linear relationship) between the measured (r\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ecal\u003c/sub\u003e) and cross-validation models (r\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCV\u003c/sub\u003e), the low bias and CV bias values together with the predicted physicochemical characteristic based on the sensory attributes. Therefore, the proximity between the green and red regression curves helped to reach the models quality. In general, despite of the relatively elevate errors achieved on RMSEC and RMSECV methods, the r\u003csup\u003e2\u003c/sup\u003e on calibration proximity between the RMSEC and RMSECV values (similarity criterion) indicated well-adjusted models. However, source of experimental error should be strongly considered in future actions in order to improve the models quality.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe quality of fruits is determined by fundamental aspects ultimately related to consumers\u0026apos; perception: microbiological safety, and physical, chemical, and sensory attributes (Dutcosky 2013). \u0026nbsp;\u0026lsquo;Rama Forte\u0026rsquo; persimmons treated with CO\u003csub\u003e2\u003c/sub\u003e had a change in the astringency loss and ripening processes after 4 to 5 days of treatment. The first days were characterized by high flesh firmness, yellowish skin, bitterness, and astringency. At the final days, attributes such as sweetness, translucency, red-colored skin, and aroma stood out. \u0026lsquo;Rama Forte\u0026rsquo; persimmons are a soft-fleshed cultivar. Along fruit ripening, flesh firmness loss is a result of enhanced activity of cell wall hydrolyzing enzymes (Besada et al. 2010; Nakano et al. 2003). CO\u003csub\u003e2\u003c/sub\u003e treatment triggers a gradual breakdown of parenchyma tissues of the persimmon mesocarp, which results in the firmness loss at the final stages of fruit ripening (Sandra Munera et al. 2019; Tessmer et al. 2016). The translucency is normally acquired concomitantly with firmness loss is associated with water soluble pectin content (Candan et al. 2008) and changes in membrane permeability. Translucency is typically acquired alongside a loss of firmness, which is associated with the water-soluble pectin content (Candan et al., 2008) and the changes in membrane permeability. The solubilization of pectic material is related to the hydrolysis of ester bonds and loss of neutral sugars in the chain (Voragen et al. 1995), trough the action of hydrolytic enzymes. The more intense red skin color observed in persimmon fruit during ripening is due to the increase in carotenoid contents (Chen et al. 2016), mainly \u0026beta;-carotene (Giordani et al. 2011), and the gelatinous appearance of some cultivars gives the translucency to the fruit. Ripening processes go along with the biosynthesis of aroma compounds whose mix gives the characteristic aroma of each fruit. The aroma complex also impacts the perception of fruit flavor (Lawless and Heymann 2016). Speeding up fruit ripening by CO\u003csub\u003e2\u003c/sub\u003e deastringency treatment validates the importance of aroma and flavor variables in the last days of evaluations.\u003c/p\u003e\n\u003cp\u003ePersimmons treated with ethanol showed a transition related to ripening and astringency degree at the fourth and fifth days after the deastringency treatment, but less evident in comparison to persimmons treated with CO\u003csub\u003e2\u003c/sub\u003e. The initial days after deastringency treatments were characterized by high flesh firmness and bitterness. After the sixth day (final days), pleasant aroma and flavor, sweetness and translucency were evidenced together with an intensification of skin and flesh color, indicative of the advancement of ripening processes. Preceding trials pointed out that astringency removal with ethanol tended to delay changes in flesh firmness and skin color (Kato 1987; Monteiro et al. 2014; Mu\u0026ntilde;oz 2002; Vitti 2009).\u003c/p\u003e\n\u003cp\u003eAstringency removal with high CO\u003csub\u003e2\u003c/sub\u003e concentrations (beyond 70%) lead to hypoxia and elevated stress to cells. That condition is regulated according to Licausi et al. (2010) and Papdi et al. (2015) by ethylene response factors (ERFs). ERF family transcription factors are important regulators of ethylene-dependent pathways (M\u0026uuml;ller and Munn\u0026eacute;-Bosch 2015). Stresses set off by elevated CO\u003csub\u003e2\u003c/sub\u003e concentrations together with ERF\u0026acute;s activation might have contributed to an inverse ripening effect of both deastringency treatments resulting in faster ripening of persimmons treated with CO\u003csub\u003e2\u003c/sub\u003e compared to those treated with ethanol. High CO\u003csub\u003e2\u003c/sub\u003e concentrations (beyond 70%) used for astringency removal can lead to hypoxia and elevated stress in cells. Licausi et al. (2010) and Papdi et al. (2015) have shown that this condition is regulated by ethylene response factors (ERFs), which are important transcription factors for ethylene-dependent pathways (M\u0026uuml;ller and Munn\u0026eacute;-Bosch 2015). Elevated concentrations of CO\u003csub\u003e2\u003c/sub\u003e and ERF activation may have contributed to an opposite effect of both deastringency treatments, resulting in faster ripening of persimmons treated with CO\u003csub\u003e2\u003c/sub\u003e compared to those treated with ethanol.\u003c/p\u003e\n\u003cp\u003ePrediction models of astringency removal and sensory quality of astringent persimmons are critical as they allow to infer the optimal period to consume the fruit and, consequently, the available time to transport and commercialize after the deastringency treatments. For the CO\u003csub\u003e2\u003c/sub\u003e treatment (70 %), the translucency that was evaluated in a non-destructive way by the panelists was used to determine the color index and the flesh firmness, both physical attributes. It is important to point out that firmness is generally obtained by a destructive method. The prediction models for the color index and flesh firmness based on translucency presented Pearson\u0026apos;s R-values of 0.83 and 0.91, respectively. Munera et al. (2017) determined values of 0.77 and 0.80 to predict flesh firmness using hyperspectral imaging of \u0026lsquo;Rojo Brillante\u0026rsquo; persimmons treated with CO\u003csub\u003e2.\u0026nbsp;\u003c/sub\u003e Furthermore, previous studies revealed that values of the r\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCV\u003c/sub\u003e (percentage of the variance in the dependent variables as Y matrix accounted by the independent variables in X matrix) between 0.50 and 0.65 indicate that more than 50 % of the variance in Y was accounted by variance in X; between 0.66 and 0.81 indicate approximate quantitative predictions; between 0.82 and 0.90 reveal satisfactory prediction; and above 0.91 are considered excellent model (Saeys et al. 2005; Williams et al. 2019). Based on these statements, our prediction model for flesh firmness of \u0026lsquo;Rama Forte\u0026rsquo; persimmons treated with CO\u003csub\u003e2\u003c/sub\u003e for astringency removal (r\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCV\u003c/sub\u003e 0.81) showed approximate quantitative predictions. The proximity of the ideal and real regression curves for the attributes flesh firmness and color index obtained from translucency of persimmons treated with CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eindicates a good adjustment of the models.\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Rama Forte\u0026rsquo; persimmon is a cultivar of the pollination-variant astringent group (PVA), whose fruit turns juicy when is ripe (Prohort 2016). Traditionally \u0026lsquo;Rama Forte\u0026rsquo; persimmons are consumed when the skin color is red, and the flesh is soft and juicy. Prediction models for fruit firmness based on visual evaluations, such as translucency, could be useful for logistics planning, considering the fruit must be firm to be transported, in order to reduce waste and losses along the postharvest handling chain. The equation \u0026ldquo;Flesh firmness = - 0.8211 x translucency + 7.4025\u0026rdquo; indicated that translucency close to 8 (scale from 1 = absent/less intense to 9 = very intense, Figure S2) corresponds to persimmons with flesh firmness of 1 N, \u003cem\u003ei. e.\u003c/em\u003e, juicy and ready to eat persimmons. That firmness value was reached on the fifth day after astringency removal. On the other hand, translucency values close to 2 are indicative of flesh firmness values in the range of 6 N. Lower translucency scores and, consequently, higher flesh firmness values that were determined up to the third day are ideal for the distribution logistics of persimmons.\u003c/p\u003e\n\u003cp\u003eThe ethanol treatment (1.7 mL kg\u003csup\u003e-1\u003c/sup\u003e) permitted establishing a prediction model of astringency index from a destructive analysis method using the attribute astringency. The prediction model presented a Pearson\u0026apos;s R-value of 0.75 and r\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ecv\u003c/sub\u003e of 0.55, suggesting that more than 50 % of the variance of the astringency index is explained by the variation of the sensory attributes aroma and astringency. Establishing a prediction model for astringency loss of astringent persimmons is of great commercial importance since it is an attribute inherent to persimmon quality and for that reason it has been sought through methods such as NIR (near infrared) and hyperspectral images (Baek et al. 2023; Cort\u0026eacute;s et al. 2017; Sandra Munera et al. 2017, 2019; Zhu et al. 2020). However, constructing a suitable predictive model from simpler analytical methods may be more cost-effective.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe absence of an adequate correlation between the sensory astringency and astringency index with the proanthocyanidins content has been already reported by Amorim et al. (2020) and Braga et al. (Braga et al. 2021). Amorim et al. (2020) suggested that other compounds, beyond proanthocyanidins, might be present in the sensory perception of astringency. Braga et al. (2021) demonstrated that cinnamoyl glucoside, a phenolic acid, and anacardic acids have a positive correlation with the sensory perception of astringency in cashews (\u003cem\u003eAnacardium occidentale\u003c/em\u003e). These reports, along with the results of our study, provide evidence that proanthocyanidins alone may not fully represent astringency and reinforce the need for more studies to understand the involvement of other metabolites in the sensory perception of persimmon astringency.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eA prediction model for a destructive analysis method can be developed by correlating sensory and physicochemical attributes. The developed model is suitable for predicting flesh firmness from visual assessment of translucency of CO\u003csub\u003e2\u003c/sub\u003e-treated \u0026apos;Rama Forte\u0026apos; persimmons. The low correlation between sensory astringency and astringency index with proanthocyanidins quantification is an indication that other metabolites are involved in the sensory astringency perception. Additional research is necessary to identify the compounds involved in astringency to develop predictive models for this attribute in persimmons. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFounding sources\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge to the Brazilian National Council for Scientific and Technological Development (CNPq) for a research grant [131725/2017-3], the Brazilian Agricultural Research Corporation [Project 02.14.03.011.00.00] and the Cearense Foundation for Scientific and Technological Development Support (FUNCAP) [01986340/2021].\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eCatherine Amorim: Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing. Elenilson Godoy Alves Filho: Formal analysis, Writing - Original Draft. Deborah Santos Garruti: Conceptualization, Methodology, Validation. \u0026nbsp;Renar João Bender: Validation, Writing - Review \u0026amp; Editing. Lucimara Rogéria Antoniolli: Conceptualization, Methodology, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision, Project administration, Funding acquisition, Resources.\u003c/p\u003e\n\u003cp\u003eStatements and Declarations\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkyldiz, A., Aksay, S., Benli, H., Kiroǧlu, H., \u0026amp; Fenercioğlu, H. (2004). Determination of changes in some characteristics of persimmon during dehydration at different temperatures. \u003cem\u003eJournal of Food Engineering\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(1), 95\u0026ndash;99. https://doi.org/10.1016/j.jfoodeng.2004.01.001\u003c/li\u003e\n\u003cli\u003eAmorim, C., Alves Filho, E. G., Rodrigues, T. H. S., Bender, R. J., Canuto, K. M., Garruti, D. S., \u0026amp; Antoniolli, L. R. (2020). Volatile compounds associated to the loss of astringency in \u0026lsquo;Rama Forte\u0026rsquo; persimmon fruit. \u003cem\u003eFood Research International\u003c/em\u003e, \u003cem\u003e136\u003c/em\u003e, 1\u0026ndash;10. https://doi.org/10.1016/j.foodres.2020.109570\u003c/li\u003e\n\u003cli\u003eAmorim, C., Antoniolli, L. R., Orsi, B., \u0026amp; Kluge, R. A. (2023). Advances in metabolism and genetic control of astringency in persimmon (Diospyros kaki Thunb.) fruit: A review. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, \u003cem\u003e308\u003c/em\u003e(27), 111561. https://doi.org/10.1016/j.scienta.2022.111561\u003c/li\u003e\n\u003cli\u003eAntoniolli, L. R., Castro, P. R. de C. e, Kluge, R. A., \u0026amp; Filho, J. A. S. (2000). Remo\u0026ccedil;\u0026atilde;o da adstring\u0026ecirc;ncia de frutos de caquizeiro \u0026lsquo;Giombo\u0026rsquo; sob diferentes per\u0026iacute;odos de exposi\u0026ccedil;\u0026atilde;o ao vapor de \u0026aacute;lcool et\u0026iacute;lico. \u003cem\u003ePesquisa Agropecu\u0026aacute;ria Brasileira\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(10), 2083\u0026ndash;2091.\u003c/li\u003e\n\u003cli\u003eArnal, L., \u0026amp; Del R\u0026iacute;o, M. A. (2003). Removing astringency by carbon dioxide and nitrogen-enriched atmospheres in persimmon fruit cv. \u0026ldquo;Rojo brillante.\u0026rdquo; \u003cem\u003eJournal of Food Science\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(4), 1516\u0026ndash;1518. https://doi.org/10.1111/j.1365-2621.2003.tb09676.x\u003c/li\u003e\n\u003cli\u003eArslan, S., \u0026amp; Bayrakci, S. (2016). Physicochemical, functional, and sensory properties of yogurts containing persimmon. \u003cem\u003eTurkish Journal of Agriculture and Forestry\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e, 68\u0026ndash;74. https://doi.org/10.3906/tar-1406-150\u003c/li\u003e\n\u003cli\u003eBaek, M. W., Choi, H. R., Hwang, I. G., Tilahun, S., \u0026amp; Jeong, C. S. (2023). Prediction of tannin content and quality parameters in astringent persimmons from visible and near-infrared spectroscopy. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e. https://doi.org/10.3389/fpls.2023.1260644\u003c/li\u003e\n\u003cli\u003eBen-Arie, R., \u0026amp; Sonego, L. (1993). Temperature affects astringency removal and recurrence in persimmon. \u003cem\u003eJournal of Food Science\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e, 1397\u0026ndash;1400.\u003c/li\u003e\n\u003cli\u003eBesada, C., Jackman, R. C., Olsson, S., \u0026amp; Woolf, A. B. (2010). Response of \u0026lsquo;Fuyu\u0026rsquo; persimmons to ethylene exposure before and during storage. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(2), 124\u0026ndash;131. https://doi.org/10.1016/j.postharvbio.2010.03.002\u003c/li\u003e\n\u003cli\u003eBraga, D. C., Alves Filho, E. G., Ribeiro, P. R. V., Ara\u0026uacute;jo, \u0026Iacute;. M. da S., Brito, E. S. de, \u0026amp; Garruti, D. dos S. (2021). Multivariate correlation of the astringency sensory perception with the phenolic profiling of cashew apple genotypes. \u003cem\u003eFood Bioscience\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(February). https://doi.org/10.1016/j.fbio.2021.100931\u003c/li\u003e\n\u003cli\u003eCandan, A. P., Graell, J., \u0026amp; Larrigaudi\u0026egrave;re, C. (2008). Roles of climacteric ethylene in the development of chilling injury in plums. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(1), 107\u0026ndash;112. https://doi.org/10.1016/j.postharvbio.2007.06.009\u003c/li\u003e\n\u003cli\u003eChen, J., Du, J., Ge, Z., Zhu, W., Nie, R., \u0026amp; Li, C. (2016). Comparison of sensory and compositions of five selected persimmon cultivars (Diospyros kaki L.) and correlations between chemical components and processing characteristics. \u003cem\u003eJournal of Food Science and Technology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(1597\u0026ndash;1607). https://doi.org/10.1007/s13197-015-2102-y\u003c/li\u003e\n\u003cli\u003eChen, W., Xiong, Y., Xu, L., Zhang, Q., \u0026amp; Luo, Z. (2017). An integrated analysis based on transcriptome and proteome reveals deastringency-related genes in CPCNA persimmon. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(44671), 1\u0026ndash;18. https://doi.org/10.1038/srep44671\u003c/li\u003e\n\u003cli\u003eCort\u0026eacute;s, V., Rodr\u0026iacute;guez, A., Blasco, J., Rey, B., Besada, C., Cubero, S., et al. (2017). Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy. \u003cem\u003eJournal of Food Engineering\u003c/em\u003e, \u003cem\u003e204\u003c/em\u003e, 27\u0026ndash;37. https://doi.org/10.1016/j.jfoodeng.2017.02.017\u003c/li\u003e\n\u003cli\u003eDas, P. R., \u0026amp; Eun, J.-B. (2021). Removal of astringency in persimmon fruits (Diospyros kaki) subjected to different freezing temperature treatments. \u003cem\u003eJournal of Food Science and Technology\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e, 3154\u0026ndash;3163. https://doi.org/10.1007/s13197-020-04818-3\u003c/li\u003e\n\u003cli\u003eDutcosky, S. D. (2013). \u003cem\u003eAn\u0026aacute;lise sensorial de alimentos\u003c/em\u003e (4th ed.). Curitiba: PUCPRESS.\u003c/li\u003e\n\u003cli\u003eEdagi, F. K., Chiou, D. G., Terra, F. de A. M., Sestari, I., \u0026amp; Kluge, R. A. (2009). Remo\u0026ccedil;\u0026atilde;o da adstring\u0026ecirc;ncia de caquis \u0026ldquo;Giombo\u0026rdquo; com subdosagens de etanol. \u003cem\u003eCi\u0026ecirc;ncia Rural\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(7), 2022\u0026ndash;2028. https://doi.org/10.1590/s0103-84782009005000165\u003c/li\u003e\n\u003cli\u003eEl-Sayed, H. A. (2017). Production of Healthy Yoghurt Fortified With Kaki Juice (Persimmon). \u003cem\u003eEgyptian Journal of Food Science\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), 179\u0026ndash;190.\u003c/li\u003e\n\u003cli\u003eFarina, V., Bianco, R. Lo, \u0026amp; Mazzaglia, A. (2019). Evaluation of Late-Maturing Peach and Nectarine Fruit Quality by Chemical, Physical, and Sensory Determinations. \u003cem\u003eAgriculture\u003c/em\u003e, \u003cem\u003e189\u003c/em\u003e(9), 2\u0026ndash;11. https://doi.org/https://doi.org/10.3390/agriculture9090189\u003c/li\u003e\n\u003cli\u003eFilho, E. G. A., Silva, L. M. A., Wurlitzer, N. J., Fernandes, F. A. N., Fonteles, T. V., Rodrigues, S., \u0026amp; Brito, E. S. de. (2020). An integrated analytical approach based on NMR, LC\u0026ndash;MS and GC\u0026ndash;MS to evaluate thermal and non-thermal processing of cashew apple juice. \u003cem\u003eFood Chemistry\u003c/em\u003e, \u003cem\u003e309\u003c/em\u003e(125761). https://doi.org/10.1016/j.foodchem.2019.125761\u003c/li\u003e\n\u003cli\u003eGazit, S., \u0026amp; Levy, Y. (1963). Adstringency and its removal in persimmon. \u003cem\u003eIsrael Journal of Agricultural Research\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 125\u0026ndash;132.\u003c/li\u003e\n\u003cli\u003eGiordani, E., Doumett, S., Nin, S., \u0026amp; Del Bubba, M. (2011). Selected primary and secondary metabolites in fresh persimmon (Diospyros kaki Thunb.): A review of analytical methods and current knowledge of fruit composition and health benefits. \u003cem\u003eFood Research International\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(7), 1752\u0026ndash;1767. https://doi.org/10.1016/j.foodres.2011.01.036\u003c/li\u003e\n\u003cli\u003eIto, S. (1971). The persimmon. In A. C. Hulme (Ed.), \u003cem\u003eThe biochemistry of fruits and their products\u003c/em\u003e (Vol. 2, pp. 281\u0026ndash;301). London: Academic Press.\u003c/li\u003e\n\u003cli\u003eJin, R., Zhu, Q. G., Shen, X. Y., Wang, M. M., Jamil, W., Grierson, D., et al. (2018). DkNAC7, a novel high-CO 2 /hypoxia-induced NAC transcription factor, regulates persimmon fruit de-astringency. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3). https://doi.org/10.1371/journal.pone.0194326\u003c/li\u003e\n\u003cli\u003eKato, K. (1987). Astringency Removal and Ripening as Related to Temperature during the de-Astringency by Ethanol in Persimmon Fruits. \u003cem\u003eJournal of the Japanese Society for Horticultural Science\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(4), 498\u0026ndash;509. https://doi.org/10.2503/jjshs.55.498\u003c/li\u003e\n\u003cli\u003eKato, K. (1990). Astringency Removal and Ripening in Persimmons Treated with Ethanol and Ethylene. \u003cem\u003eHortscience\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 205\u0026ndash;207.\u003c/li\u003e\n\u003cli\u003eLawless, H. T. , \u0026amp; Heymann, H. (2016). \u003cem\u003eSensory evaluation of food\u003c/em\u003e. New York: Springer.\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Camelo, A. F., \u0026amp; G\u0026oacute;mez, P. A. (2004). Comparison of color ripening. \u003cem\u003eHorticultura Brasileira\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 534\u0026ndash;537. https://doi.org/10.1590/S0102-05362004000300006\u003c/li\u003e\n\u003cli\u003eMacFie, H. J., Bratchell, N., Greenhoff, K., \u0026amp; Vallis, L. V. (1989). Designs to balance the effect of order of presentation and first‐order carry‐over effects in hall tests. \u003cem\u003eJournal of Sensory Studies\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 129\u0026ndash;148. https://doi.org/10.1111/j.1745-459X.1989.tb00463.x\u003c/li\u003e\n\u003cli\u003eMartineli, M., Rezende, C. M. de, Fonseca, M. J. de O., Soares, A. G., \u0026amp; Deliza, R. (2019). Packagings for the transportation of persimmon and their effects on sensory characteristics. \u003cem\u003ePesquisa Agropecu\u0026aacute;ria Brasileira\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 1\u0026ndash;10. https://doi.org/0.1590/S1678-3921.pab2019.v54.00082.\u003c/li\u003e\n\u003cli\u003eMatsuo, T., \u0026amp; Itoo, S. (1982). A model experimente for de-astringency in persimmon fruit with high carbon dioxide treatment: in vitro gelation of kaki-tannin by reacting with acetaldehyde. \u003cem\u003eAgricultural and Biological Chemistry\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 683\u0026ndash;689. https://doi.org/10.1080/00021369.1982.10865131\u003c/li\u003e\n\u003cli\u003eMatsuo, Tomoaki, Shinohara, J. I., \u0026amp; Ito, S. (1976). An improvement on removing astringency in persimmon fruits by carbon dioxide gas. \u003cem\u003eAgricultural and Biological Chemistry\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1), 215\u0026ndash;217. https://doi.org/10.1080/00021369.1976.10862021\u003c/li\u003e\n\u003cli\u003eMilczarek, R. R., Liang, P. S., Wong, T., Augustine, M. P., Smith, J. L., Woods, R. D., et al. (2019). Nondestructive determination of the astringency of pollination-variant persimmons (Diospyros kaki) using near-infrared (NIR) spectroscopy and nuclear magnetic resonance (NMR) relaxometry. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e, 50\u0026ndash;57. https://doi.org/10.1016/j.postharvbio.2018.11.006\u003c/li\u003e\n\u003cli\u003eMilczarek, R. R., Woods, R. D., LaFond, S. I., Breksa, A. P., Preece, J. E., Smith, J. L., et al. (2018). Synthesis of descriptive sensory attributes and hedonic rankings of dried persimmon (Diospyros kaki sp.). \u003cem\u003eFood Science and Nutrition\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 124\u0026ndash;136. https://doi.org/10.1002/fsn3.537\u003c/li\u003e\n\u003cli\u003eMin, T., Fang, F., Ge, H., Shi, Y. N., Luo, Z. R., Yao, Y. C., et al. (2014). Two novel anoxia-induced ethylene response factors that interact with promoters of deastringency-related genes from persimmon. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 1\u0026ndash;8. https://doi.org/10.1371/journal.pone.0097043\u003c/li\u003e\n\u003cli\u003eMishra, P., Roger, J.-M., Jouan-Rimbaud-Bouveresse, D., Biancolillo, A., Marini, F., Nordon, A., \u0026amp; Rutledge, D. N. (2021). Recent trends in multi-block data analysis in chemometrics for multi-source data integration. \u003cem\u003eTrends in Analytical Chemistry\u003c/em\u003e, \u003cem\u003e137\u003c/em\u003e(116206). https://doi.org/10.1016/j.trac.2021.116206\u003c/li\u003e\n\u003cli\u003eMonteiro, M. F., Edagi, F. K., Majado, M., Sasaki, F. C., Saavedra, J., \u0026amp; Kluge, R. A. (2014). Remo\u0026ccedil;\u0026atilde;o da adstring\u0026ecirc;ncia de caqui \u0026lsquo;Giombo\u0026rsquo; Com etanol em Doses e tempos diferentes, \u003cem\u003e15\u003c/em\u003e, 160\u0026ndash;167. https://www.redalyc.org/articulo.oa?id=81333269006\u003c/li\u003e\n\u003cli\u003eMoskowitz, H. R. (1983). \u003cem\u003eProduct testing and sensory evaluation of foods: Marketing and R \u0026amp; D approaches\u003c/em\u003e. Westport: Food and Nutrition Press.\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller, M., \u0026amp; Munn\u0026eacute;-Bosch, S. (2015). Ethylene Response Factors: A Key Regulatory Hub in Hormone and Stress Signaling. \u003cem\u003ePlant Physiology\u003c/em\u003e, \u003cem\u003e169\u003c/em\u003e(1), 32\u0026ndash;41. https://doi.org/10.1104/pp.15.00677\u003c/li\u003e\n\u003cli\u003eMunera, S, Blasco, J., Cubero, S., Besada, C., Salvador, A., Talens, P., \u0026amp; Aleixos, N. (2016). Maturity assessment of \u0026ldquo;Rojo Brillante\u0026rdquo; persimmon by Hyperspectral Imaging. In \u003cem\u003eCIGR - International Commission of Agricultural and Biosystems Engineering\u003c/em\u003e (pp. 26\u0026ndash;29). Aarhus.\u003c/li\u003e\n\u003cli\u003eMunera, Sandra, Aleixos, N., Besada, C., G\u0026oacute;mez-Sanchis, J., Salvador, A., Cubero, S., et al. (2019). Discrimination of astringent and deastringed hard \u0026lsquo;Rojo Brillante\u0026rsquo; persimmon fruit using a sensory threshold by means of hyperspectral imaging. \u003cem\u003eJournal of Food Engineering\u003c/em\u003e, \u003cem\u003e263\u003c/em\u003e(January), 173\u0026ndash;180. https://doi.org/10.1016/j.jfoodeng.2019.06.008\u003c/li\u003e\n\u003cli\u003eMunera, Sandra, Besada, C., Blasco, J., Cubero, S., Salvador, A., Talens, P., \u0026amp; Aleixos, N. (2017). Astringency assessment of persimmon by hyperspectral imaging. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e, 35\u0026ndash;41. https://doi.org/10.1016/j.postharvbio.2016.11.006\u003c/li\u003e\n\u003cli\u003eMu\u0026ntilde;oz, V. R. de S. (2002). \u003cem\u003eDestaniza\u0026ccedil;\u0026atilde;o do caqui (Diospyrus kaki L .) \u0026ldquo;Rama Forte.\u0026rdquo;\u003c/em\u003e Universidade Estadual de Campinas - Faculdade de Engenharia Agr\u0026iacute;cola.\u003c/li\u003e\n\u003cli\u003eNakano, R., Ogura, E., Kubo, Y., \u0026amp; Inaba, A. (2003). Ethylene Biosynthesis in Detached Young Persimmon Fruit Is Initiated in Calyx and Modulated by Water Loss from the Fruit. \u003cem\u003ePlant Physiology\u003c/em\u003e, \u003cem\u003e131\u003c/em\u003e(1), 276\u0026ndash;286. https://doi.org/10.1104/pp.010462\u003c/li\u003e\n\u003cli\u003eNelson, D. L., \u0026amp; Cox, M. M. (2014). \u003cem\u003ePrinc\u0026iacute;pios de Bioqu\u0026iacute;mica de Lehninger\u003c/em\u003e (6th ed.). Porto Alegre: Artmed.\u003c/li\u003e\n\u003cli\u003eNicol\u0026auml;i, B. M., Defraeye, T., De Ketelaere, B., Herremans, E., Hertog, M. L. A. T. M., Saeys, W., et al. (2014). Nondestructive measurement of fruit and vegetable quality. \u003cem\u003eAnnual Review of Food Science and Technology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 285\u0026ndash;312. https://doi.org/10.1146/annurev-food-030713-092410\u003c/li\u003e\n\u003cli\u003eNovillo, P., Gil, R., Besada, C., \u0026amp; Salvador, A. (2015). Astringency removal of \u0026ldquo;Rojo Brillante\u0026rdquo; persimmon by combining CO2 and ethanol application. \u003cem\u003eActa Horticulturae\u003c/em\u003e, \u003cem\u003e1079\u003c/em\u003e, 599\u0026ndash;604. https://doi.org/10.17660/ActaHortic.2015.1079.81\u003c/li\u003e\n\u003cli\u003eNovillo, P., Salvador, A., Magalhaes, T., \u0026amp; Besada, C. (2014). Deastringency treatment with CO2 induces oxidative stress in persimmon fruit. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e, 16\u0026ndash;22. https://doi.org/10.1016/j.postharvbio.2013.12.019\u003c/li\u003e\n\u003cli\u003ePrograma Brasileiro para moderniza\u0026ccedil;\u0026atilde;o da Horticultura. (2009). \u003cem\u003eNormas de classifica\u0026ccedil;\u0026atilde;o: caqui\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eProhort. (2016). Panorama do caqui comercializado no ETSP-Ceagesp. S\u0026atilde;o Paulo. http://www.hortibrasil.org.br/2016-06-03-10-48/decidindo-o-futuro-do-caqui.html?showall=\u0026amp;start=05. Accessed 26 November 2023\u003c/li\u003e\n\u003cli\u003eProhort. (2018). Programa Brasileiro de Moderniza\u0026ccedil;\u0026atilde;o do Mercado de Hortigranjeiro. \u003cem\u003eCEAGESP\u003c/em\u003e. http://www.ceasa.gov.br/. Accessed 26 June 2023\u003c/li\u003e\n\u003cli\u003eSaeys, W., Mouazen, A. M., \u0026amp; Ramon, H. (2005). Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy. \u003cem\u003eBiosystems Engineering\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(4), 393\u0026ndash;402. https://doi.org/10.1016/j.biosystemseng.2005.05.001\u003c/li\u003e\n\u003cli\u003eSalvador, A., Arnal, L., Besada, C., Larrea, V., Hernando, I., Perez-Munuera, I., \u0026amp; P\u0026eacute;rez-Munuera, I. (2008). Reduced effectiveness of the treatment for removing astringency in persimmon fruit when stored at 15 \u0026deg;C: physiological and microstructural study. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(3), 340\u0026ndash;347. https://doi.org/10.1016/j.postharvbio.2008.01.015\u003c/li\u003e\n\u003cli\u003eSalvador, A., Arnal, L., Besada, C., Larrea, V., Quiles, A., \u0026amp; P\u0026eacute;rez-Munuera, I. (2007). Physiological and structural changes during ripening and deastringency treatment of persimmon fruit cv. \u0026lsquo;Rojo Brillante.\u0026rsquo; \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 181\u0026ndash;188. https://doi.org/10.1016/j.postharvbio.2007.05.003\u003c/li\u003e\n\u003cli\u003eSanch\u0026iacute;s, E., Mateos, M., \u0026amp; P\u0026eacute;rez-Gago, M. B. (2016). Physicochemical, sensory, and nutritional quality of fresh-cut \u0026ldquo;Rojo Brillante\u0026rdquo; persimmon affected by maturity stage and antibrowning agents. \u003cem\u003eFood Science and Technology International\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(7), 1\u0026ndash;13. https://doi.org/10.1177/1082013216629262\u003c/li\u003e\n\u003cli\u003eSato, A., \u0026amp; Yamada, M. (2016). Persimmon breeding in Japan for pollination-constant non-astringent (PCNA) type with marker-assisted selection. \u003cem\u003eBreeding Science\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(1), 60\u0026ndash;68. https://doi.org/0.1270/jsbbs.66.60\u003c/li\u003e\n\u003cli\u003eSiddiq, R., Auras, R., Siddiq, M., Dolan, K. D., \u0026amp; Harte, B. (2020). Effect of modified atmosphere packaging (MAP) and NatureSeal\u0026reg; treatment on the physico-chemical, microbiological, and sensory quality of fresh-cut d\u0026rsquo;Anjou pears. \u003cem\u003eFood Packaging and Shelf Life\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 1\u0026ndash;6. https://doi.org/https://doi.org/10.1016/j.fpsl.2019.100454\u003c/li\u003e\n\u003cli\u003eStavang, J. A., Freitag, S., Foito, A., Verral, S., Heide, O. M., Stewart, D., \u0026amp; S\u0026oslash;nsteby, A. (2015). Raspberry fruit quality changes during ripening and storage as assessed by colour, sensory evaluation and chemical analyses. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, \u003cem\u003e195\u003c/em\u003e(12), 216\u0026ndash;225. https://doi.org/https://doi.org/10.1016/j.scienta.2015.08.045\u003c/li\u003e\n\u003cli\u003eSucupira, N. R., AlvesFilho, E. G., Silva, L. M. A., Brito, E. S. de, Wurlitzer, N. J., \u0026amp; Sousa, P. H. M. (2017). NMR spectroscopy and chemometrics to evaluate different processing of coconut water. \u003cem\u003eFood Chemistry\u003c/em\u003e, \u003cem\u003e2016\u003c/em\u003e, 2017\u0026ndash;224. https://doi.org/10.1016/j.foodchem.2016.08.035\u003c/li\u003e\n\u003cli\u003eTaira, S. (1996). Astringency in persimmon. In H. F. Linkskens \u0026amp; J. F. Jackson (Eds.), \u003cem\u003eModern methods of plant analysis\u003c/em\u003e (Vol. 18, pp. 97\u0026ndash;110). Berlin: Springer. https://doi.org/10.1007/978-3-642-79660-9_6\u003c/li\u003e\n\u003cli\u003eTerra, F. de A. M., Edagi, F. K., Sasaki, F. F. C., Frassetto Filho, M. E., Silva, M. M. da, Giro, B., et al. (2014). Aplica\u0026ccedil;\u0026atilde;o do 1-metilciclopropeno e sua influ\u0026ecirc;ncia no processo de remo\u0026ccedil;\u0026atilde;o da adstring\u0026ecirc;ncia com etanol em caqui \u0026ldquo;Giombo\u0026rdquo; refrigerado. \u003cem\u003eCi\u0026ecirc;ncia Rural\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(2), 210\u0026ndash;216. https://doi.org/10.1590/s0103-84782014000200003\u003c/li\u003e\n\u003cli\u003eTessmer, M. A., Appezzato-da-Gl\u0026oacute;ria, B., \u0026amp; Kluge, R. A. (2018). Astringency reduction using ethanol-associated to the storing under refrigeration at 5\u003csup\u003eo\u003c/sup\u003eC promotes physiological and structural alterations in \u0026lsquo;Giombo\u0026rsquo; persimmons. \u003cem\u003eComunicata Scientiae\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 49-456. https://doi.org/https://doi.org/10.14295/cs.v9i3.2014\u003c/li\u003e\n\u003cli\u003eTessmer, M. A., Appezzato-da-Gl\u0026oacute;ria, B., \u0026amp; Kluge, R. A. (2019). Evaluation of storage temperatures to astringency \u0026lsquo;Giombo\u0026rsquo; persimmon: Storage at 1 \u0026deg;C combined with 1-MCP is recommended to alleviate chilling injury. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, \u003cem\u003e257\u003c/em\u003e, 1\u0026ndash;7. https://doi.org/https://doi.org/10.1016/j.scienta.2019.108675\u003c/li\u003e\n\u003cli\u003eTessmer, M. A., Besada, C., Hernando, I., Appezzato-da-Gl\u0026oacute;ria, B., Quiles, A., \u0026amp; Salvador, A. (2016). Microstructural changes while persimmon fruits mature and ripen. Comparison between astringent and non-astringent cultivars. \u003cem\u003ePostharvest Biology and Technology\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e, 52\u0026ndash;60. https://doi.org/10.1016/j.postharvbio.2016.05.014\u003c/li\u003e\n\u003cli\u003eTziotzios, G., Pantazi, X. E., Paraskevas, C., Tsitsopoulos, C., Valasiadis, D., Nasiopoulou, E., et al. (2024). Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits. \u003cem\u003eHorticulturae\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 251. https://doi.org/10.3390/horticulturae10030251\u003c/li\u003e\n\u003cli\u003eVitti, D. C. (2009). \u003cem\u003eDestaniza\u0026ccedil;\u0026atilde;o e armazenamento refrigerado de caqui \u0026lsquo; Rama Forte \u0026rsquo; em fun\u0026ccedil;\u0026atilde;o da \u0026eacute;poca de colheita\u003c/em\u003e (Tese). Universidade de S\u0026atilde;o Paulo - Escola Superior de Agricultura \u0026ldquo; Luiz de Queiroz .\u0026rdquo;\u003c/li\u003e\n\u003cli\u003eVoragen, G. J., Pilnik, W., Thibault, J. F., Axelos, M. A. V., \u0026amp; Renard, C. M. G. C. (1995). Pectins. In Stephen A. M. (Ed.), \u003cem\u003eFood polysaccharides and their applications\u003c/em\u003e. New York: Marcel Dekker Inc.\u003c/li\u003e\n\u003cli\u003eWilliams, P., Manley, M., \u0026amp; Antoniszyn, J. (2019). \u003cem\u003eNear infrared technology: getting the best out of light\u003c/em\u003e. African Sun Media.\u003c/li\u003e\n\u003cli\u003eXie, D. Y., \u0026amp; Dixon, R. A. (2005). Proanthocyanidin biosynthesis - still more questionsthan answers? \u003cem\u003ePhytochemistry\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e, 2127\u0026ndash;2144. https://doi.org/10.1016/j.phytochem.2005.01.008\u003c/li\u003e\n\u003cli\u003eXu, J. C., Ding, J. G., Gan, J. P., Mo, R. L., Xu, L. Q., Zhang, Q. L., \u0026amp; Luo, Z. R. (2017). ALDH2 genes are negatively correlated with natural deastringency in Chinese PCNA persimmon (Diospyros kaki Thunb.). \u003cem\u003eTree Genetics and Genomes\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(122), 1\u0026ndash;9. https://doi.org/10.1007/s11295-017-1207-z\u003c/li\u003e\n\u003cli\u003eYamada, M., Taira, S., Ohtsuki, M., Sato, A., Iwanami, H., Yakushiji, H., et al. (2002). Varietal differences in the ease of astringency removal by carbon dioxide gas and ethanol vapor treatments among Oriental astringent persimmons of Japanese and Chinese origin. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(1\u0026ndash;2), 63\u0026ndash;72. https://doi.org/10.1016/S0304-4238(01)00367-3\u003c/li\u003e\n\u003cli\u003eZhang, P., Wu, Q., Wang, Y., Huang, Y., Xie, M., \u0026amp; Fan, L. (2024). Rapid Detection of Tannin Content in Wine Grapes Using Hyperspectral Technology. \u003cem\u003eLife\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 416. https://doi.org/10.3390/life14030416\u003c/li\u003e\n\u003cli\u003eZhu, W., Khalifa, I., Wang, R., \u0026amp; Li, C. (2020). Persimmon highly galloylated‐tannins in vitro mitigated \u0026alpha;‐amylase and \u0026alpha;‐glucosidase via statically binding with their catalytic‐closed sides and altering their secondary structure elements. \u003cem\u003eJournal of Foof Biochemistry\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(7), 1\u0026ndash;12. https://doi.org/10.1111/jfbc.13234\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Diospyros kaki L., mathematical modeling, proanthocyanidins, astringency.","lastPublishedDoi":"10.21203/rs.3.rs-4217960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4217960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCorrelations between quality attributes determined by destructive and non-destructive analysis methods are being investigated to enable quantification and prediction of internal quality characteristics without the need for destructive techniques. Our study correlated sensory and physicochemical attributes of 'Rama Forte' persimmons treated for astringency removal with 70 % CO\u003csub\u003e2\u003c/sub\u003e for 18 hours or 1.70 mL Kg\u003csup\u003e-1\u003c/sup\u003e ethanol for 6 hours, to establish predictive models for destructive analytical methods based on non-destructive ones. Physicochemical and sensory analyses were carried out daily. Principal Component Analysis (PCA), Partial Least Squares Discriminant (PLS-DA) and regression analysis by Partial Least Squares (PLS) were applied to obtain prediction models. Two models based on fruit translucency (non-destructive) were obtained for persimmons treated with CO\u003csub\u003e2\u003c/sub\u003e, one for flesh firmness, and the other for color index prediction. A model based on sensory astringency (destructive) was developed to predict the astringency index for ethanol treatment. The models show a reliable fit, particularly in predicting flesh firmness by using the translucency of \u0026nbsp;'Rama Forte' fruit treated with CO\u003csub\u003e2\u003c/sub\u003e. \u0026nbsp;Using the translucency scale and the prediction model, it is possible to establish the maximum period for logistic steps to reduce losses and waste in the persimmon chain. The low correlation between sensory astringency and proanthocyanidin content points to possible other compounds in the perception of astringency. Identifying these compounds will enable advances in the development of predictive models for quality attributes and shelf life of astringent persimmons.\u003c/p\u003e","manuscriptTitle":"Predictive modeling and correlation between the sensory and physicochemical attributes in ‘Rama Forte’ astringent persimmon fruit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 16:19:52","doi":"10.21203/rs.3.rs-4217960/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":"4086f76b-a664-47ea-b097-469a1a248160","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-19T13:47:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 16:19:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4217960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4217960","identity":"rs-4217960","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}