Nutritional Optimization of Muffins using Wheat, Bean and Quinoa Flour: A Strategy for Enhancing Protein Quality and Healthy Benefits

Nutritional Optimization of Muffins using Wheat, Bean and Quinoa Flour: A Strategy for Enhancing Protein Quality and Healthy Benefits | 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 Nutritional Optimization of Muffins using Wheat, Bean and Quinoa Flour: A Strategy for Enhancing Protein Quality and Healthy Benefits María Natalia Bassett, María Elina Acuña, Lucrecia Corral, Elvecia Carmen Moreno, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5364412/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The objective of the present study was to elaborate muffins optimizing nutritional quality using protein complementation, with regionally produced underutilized ingredients. The theoretical protein quality of various mixtures was assessed using the "digestible essential amino acid score" (DIAAS) method, using the FAO standard requirements as a reference protein. A muffin was subsequently designed, formulated, and produced using the optimal ratio of wheat, bean, and quinoa flour mixture, and compared to a control made with 100% wheat flour. The proteins, fats, dietary fibre, ashes, and moisture were determined using AOAC methods. Height, texture, and colour were evaluated using a Vernier calliper, a TA-XT 2 plus Texturometer, and a Colour Quest XE spectrophotometer. Antioxidant activity was determined using the DPPH radical decolouration method, and total phenolic compounds were quantified using the Folin-Ciocalteu method. The sensory characteristics and global acceptability were also performed. Results showed that the moisture, protein, fat, fibre content, hardness, colour and the total phenol content (p < 0,05) of muffins are significantly different from each other. The partial replacement of wheat flour with quinoa and bean flours caused a decrease in muffin height while increasing hardness, total phenol content, protein and dietary fibre content. The elaborated muffin showed good sensory acceptance, scoring 6.4 on a 9-point hedonic scale, and achieved an acceptability index of 71%. Formulating this muffin with a good nutritional balance offers an effective way to incorporate beans and quinoa into regular diets, helping to prevent chronic non-communicable and deficiency diseases in vulnerable populations. Muffin Quality Protein White bean Quinoa Flours Functional food Figures Figure 1 Figure 2 1. INTRODUCTION The world population has grown steadily in recent years. The high incidence of obesity and metabolic syndrome in modern society has been aggravated not only by a sedentary lifestyle but also by changes in eating habits. Globally, most markets offer a wide variety of foods and beverages, that combine flavour, comfort and novelty. However, at the same time there is a wide availability and widespread marketing of many of these products, and especially those with a high content of fat, simple sugars, and/or salt. These dietary patterns have an impact on the nutritional status of the Argentine population in general and the NOA population in particular, which have high rates of overweight and obesity and coexist with high rates of malnutrition and low weight, as well as some micronutrient deficits [ 1 ],[ 2 ]. An unhealthy diet is a key modifiable risk factor for non-communicable diseases (NCDs). If not addressed, poor nutrition—along with other risk factors—increases the prevalence of NCDs in populations through mechanisms such as increased blood pressure, higher blood glucose levels, altered blood lipid profiles, and overweight or obesity. Although deaths from NCDs mainly occur in adulthood, the risks associated with unhealthy diets begin in childhood and accumulate throughout life [ 3 , 4 , 5 ]. The Food and Agriculture Organization of the United Nations (FAO) actively promotes the conservation and sustainable use of biodiversity for nutrition and agriculture, as a means of increasing dietary diversity. However, there is currently a strong tendency to reduce the basis of global food security to a few species. This decrease reduces the ability of farmers and ecosystems to adapt to changes and opportunities that arise. From the point of view of research, resolving this situation requires a greater range of species; this involves revaluing and including those that were used by populations and communities in original towns. In the case of the Andean region, the vegetable species that were not taken into account for currently marketed food products are adapted to the agro-ecological conditions of the region, such as quinoa, amaranth, kañiwa, legumes, among others, are used by local farmers and thus contribute to production. sustainability and ecosystem stability. For example, Quinoa is an important source of macronutrients, fibre, vitamins, minerals, and bioactive compounds, which has led to its current revaluation. The ranges of variation of the nutrient content depend both on the varieties and on the agroecological characteristics where it is grown. This grain has a healthy effect beyond its purely nutritional effect due to the bioactive compounds it contains. However, there is limited knowledge of the nutritional values of these species and a lack of strategies for their inclusion in food and nutrition programs. The physiological effects of these whole grains and their derived products are mainly due to the supply of nutrients, the mechanical effect on the gastrointestinal system due to the fibre, and the antioxidant effect of the phenolic compounds [ 6 ]. Also, the legume flours provide protein, fibre and micronutrients while reducing fat consumption. They are the fundamental protein and caloric base of human nutrition, and the main source of energy and essential amino acids provided in a balanced and nutritious way, especially when they are combined with cereals. This combination of cereal and legume proteins enhances the biological value of each and creates an opportunity to develop novel foods with high nutritional and organoleptic quality [ 7 ]. Previous studies show that despite being abundantly produced in the NOA region, these foods are very little consumed by the population [ 8 ]. The study of protein blends of flours from different legumes, with cereals and Andean grains, among others, is determined using the MixProtLUNA.1-2013 computer tool and has allowed its application in various artisanal bread recipes [ 9 ]. Functional food is generally defined as food that provides health benefits beyond basic nutrition, often due to the presence of biologically active components. They contain substances that collaborate in the prevention of diseases and are found in sufficient quantities to achieve the desired objective [ 10 ]. Baked cereal products are generally chosen as ideal material over others owing to their wide consumer acceptance. However, developing a marketable product with potential health benefits requires a thorough investigation of multiple product parameters. Nowadays, many studies are focused on the effort to include ingredients with beneficial effects on human health arising from the need to reformulate preferred but unhealthy foods. Muffins appear as an attractive alternative to include the use of different non-conventional flours such as those obtained from chia, green banana, chickpeas, and byproducts of the industry among others [ 11 ]. For all the above mentioned, the objective of this study was to develop and optimise muffins with enhanced nutritional quality through protein complementation utilising regionally produced ingredients that are underutilized for domestic consumption, and to provide essential nutrients to help prevent chronic and deficiency diseases in vulnerable populations. 2. MATERIALS AND METHODS 2.1. Assessment and selection theoretical of the protein quality of muffins formulations The variability of the protein quality of wheat flour and its mixtures with legume and quinoa flour was studied. Protein quality was quantified based on the amount and profile of indispensable amino acids (IAAs), as well as the actual ileal digestibility of protein IAAs using the “Digestible Indispensable Amino Acid Score” (DIAAS). The value for each aai in the diet was calculated and the lowest value is designated as the DIAAS. For this, the computer tool MixProtLUNA.1-2013 created by the working group [ 12 ] was used. Its database was elaborated from a bibliographic compilation of the composition of IAAs of cereals, Andean grains and legumes of different origins. Some digestibility values were determined "in vivo" by assays with white rats and others from the literature. The FAO/WHO/UNU (2013) pattern was used as a reference [ 13 ]. The calculation of the DIAAS of the different samples was carried out using the following Eq. (1): DIAAS %= 100 x lowest value [(mg of digestible dietary indispensable amino acid in 1 g of the dietary protein)/ (mg of the same dietary indispensable amino acid in 1 g of the reference protein)] (1) 2.2. Ingredients and elaborate muffins proximate analysis The commercials wheat leaving flour, white bean ( Phaseolus vulgaris ), quinoa ( Chenopodium quinoa ) also food grade bakery ingredients, likely, refined powdered sugar, whole milk, sunflower oil, eggs, and vanilla flavour were purchased at local markets in Tucumán, Argentina. White quinoa was previously treated to remove the saponins and dust by washing with water to extract antinutritional factors (saponins) and dried. The integral flours of white beans and quinoa were made by grinding in a blade grinder and sieving them in a No. 45 sieve. Wheat flour with bean and quinoa flours was partially replaced, which resulted in obtaining optimal designed and formulated mixtures. Taking into account the theoretical evaluation of the protein composition obtained, an optimal muffin prototype was developed and compared to the control using 100% wheat flour. The elaborate muffins were kept in a plastic tray and covered with film, maintaining the temperature at 4° C. Mass yield, product yield and specific volume calculations were performed. Muffin weight was determined using a laboratory digital scale (Mettler Toledo PE360 1 +/- 0.001 g), and muffin height was measured with a vernier calliper from the highest point of the muffin to the bottom. The baking loss and baking yield were calculated according to Eq, (2) and (3) respectively, using the batter weight. Baking loss (%) = (batter weight – muffin weight)/batter weight) × 100 (2) Baking yield (%) = (muffin weight/batter weight) × 100 (3) The muffins were also analysed for protein, fibre, fat, ash, and moisture using AOAC (2016). Carbohydrate available content (g/100 g) was calculated by subtracting the contents of moisture, fat, dietary fibre, ash, and protein from 100%. 2.3. Determination of total phenolic compounds (TPC) and antioxidant activity 2.5.1 Determination of TPC Total phenolic compound content was determined by the modified Folin–Ciocalteau method [ 14 ]. The reaction mixture contained 0,5 mL of each extract (0,3 mg/mL), 0,5mL of Folin–Ciocalteau reagent, 0,5 mL of sodium carbonate (10%) and 3,5 mL of distilled water. The reaction mixture was heated at 30ºC for 1 hour in a water bath. Absorbance was measured at 765 nm. A Hitachi U1900 spectrophotometer was utilised for each quantitative measurement performed, based on a standard calibration curve of six points: 20, 100, 200, 300, 400, 500 mg/L of gallic acid in 80% ethanol. TPC was expressed as mg GAE /100g of dry material [ 14 ]. GAE: gallic acid equivalent 2.5.2. Determination of Antioxidant Activity The H-donor activity of extracts was measured by the 1,1-diphenyl-2-picrylhydrazyl (DPPH) method according to Tapia et [ 15 ]. The extracts were assayed at 100 mg/L. 1,5 mL of a freshly prepared DPPH solution (20 mg/l) was added. The reaction mixture was incubated for 30 min at 30ºC [ 16 ]. The quenching of free radicals by sample extracts and compounds was evaluated spectrophotometrically at 517 nm against the absorbance of the DPPH radical [ 17 ]. Quercetin was used as a reference compound (1 mg/l). The percentage of DPPH discoloration was calculated using Eq. (4): Percentage of discoloration (%) = 1 – (Ac / Ab)*100 (4) Where Ac = absorbance of compound/extracts Ab = absorbance of the blank at 515 nm. Values are reported as the mean of three independent determinations. 2.4. Determination of texture and colour Textural characteristics of the muffins were determined at 25° C using a TA.XT2 Texturometer (Stable Micro Systems Ltd., Surrey, UK) containing a maximum 50 kg load cell. Calibrations were carried out with a 5 kg load cell. Data was analysed using the Exponent Software supplied by the team. The muffins (n = 6) were subjected to a 25% deformation through a 2-cycle sequence using a 50-mm diameter spherical probe (Cyl. methacrylate P/50, Stable MicroSystems) at crosshead speed 0.5 mm per second. The samples were analysed after 10 h of cooking. Hardness (N) is the force at maximum deformation calculated by the integrated TA-XT2 software and was recorded in N. The following texture parameters were measured from strain force curves: hardness (N): the maximum force required to compress the sample (maximum force during the first compression cycle); elasticity (dimensionless): the height that the sample recovers during the time that elapses between the end of the first compression and the beginning of the second; cohesiveness (dimensionless): the degree to which the sample can be deformed before failure; A1/A2, A1 is the total energy required for the first compression and A2 the total energy required for the second compression); chewiness (N): the work required to chew a solid sample and bring it to a swallowing steady state (hardness x cohesiveness x elasticity). Colour analyses were performed with a ColorQuest XE spectrophotometer (Hunter Lab, USA) with D65 illuminant, a 0° standard observer and a 2.5 cm port/viewing area. The L* value is the lightness variable, with values ranging from 100 (white) to 0 (black). The a* value measures the colour of the sample, with positive values being redder and negative values being greener. The b* value also measures the colour of the sample, with positive values being more yellow and negative values being bluer. The total colour difference (∆E*) between the control sample and each of the muffins was calculated according to Eq. (5): Colour difference ∆E*=[(∆L*)^2+(∆a*)^2+(∆b*)^2 ]^(1/2) (5) Six samples were measured and the mean and standard deviation of the values were calculated. (n = 6) 2.5. Acceptability global and sensorial analysis Muffins were analysed using a consumer preference test method. They were baked 10 hours before sensory evaluation. Both samples were divided into parts, coded, and supplied with water to each consumer at once in a white plastic plate. The sensory analysis and acceptability were completed and carried out by 98 panellists who had not been trained in the sensory analysis techniques from University Santo Tomas de Aquino, Department of Concepcion, province of Tucuman. The range was between 18 to 60 years old. The consumers were asked to score the different muffin samples based on their appearance, flavour, aroma, texture and overall acceptability using a hedonic scale of nine points (9 score is “I like very much” and 1 score is “I dislike very much”). The acceptability index (AI) was calculated using an acceptability test, calculated according to the following Eq. (6): AI = (A × 100) / B (6) Where A represents the average score given by the consumers and B is the maximum possible score for the product. The research was conducted following the Universal Declaration of Human Rights (1948), the Nuremberg Code (1947), the Declaration of Helsinki (1964 and its amendments), and National Law 25,326, as amended by Law 26,343. Verbal informed consent was obtained from all participants before the anonymous survey, ensuring that their rights and confidentiality were fully protected throughout Google form. Statistical Analysis Analyses were performed using the IBM SPSS Advanced Statistics 23.0 (IBM Software Group, Chicago, IL. USA). The significant difference between the means was evaluated by Tukey’s test (p < 0.05) using analysis of variance (ANOVA). All determinations were made with triplicates, using different batches of each type of muffin samples. 3. RESULTS AND DISCUSSION 3.1. Assessment quality protein theoretical The protein content of the commercial wheat flour used was 10 g/100g and obtained a DIAAS of 47%, while white bean flour with a protein content of 27.3 g/100g obtained a DIAAS of 65%. Therefore, the formulation of mixtures of cereals and legumes allows obtaining an improvement in the amino acid balance and translates into a higher value in the quality of the protein compared to that of each one separately. Theoretical combinations were made from the addition of 10–90% until finding the most suitable of the proteins of each flour component of the mixture and achieving a product of greater nutritional value using the MixProtLUNA computer program. The results are shown in Table 1 where it is observed that the DIAAS ranges from 63 to 85%. Theoretical recipes were formulated with the different percentages and the muffins were made with them. The muffin that resulted with baking best characteristics and that was finally made, was a 25/50/25 combination of bean proteins with wheat and quinoa flours DIAAS: 72. Table 1 Digestible essential amino acid score (DIAAS) in different blends in percentage of wheat flour protein with white bean and quinoa flours protein FOOD/VARIETY/ORIGIN/TYPE White bean flour/ ( P. Vulgaris ) P: 27.3, D: 65; AALim: SAA Wheat flour ( Triticum spp) P:10, D:47; AALim: Lys Quinoa flour ( Chenopodium quinoa ) P:14, D:67; AALim: SAA WF/WBF/QF RATIOS 10/60/30 15/55/30 20/40/40 25/50/25 30/40/30 35/30/35 40/25/35 40/20/40 DIAAS % 63 67 74 72 77 81 84 85 WBF = White bean flour; WF = Wheat flour; QF = Quinoa flour; DIAAS = Digestible Indispensable Amino Acid Score. AALim: limiting amino acids. Lys: lysine; SAA: Sulphur amino acids (Methionine + cysteine) P = Protein g/100g food. Table 2 Proximal composition (g/100g fresh weight) of different types of muffin s Control muffins WF/WBF/QF muffins Commercial muffins° p value Energy (kcal/kJ)) 374 (1574) a 369 (1551) a 409 (1712) b 0.008 Moisture (g) 20.10 ± 0.06b 20.80 ± 0.04 c 15.4 ± 0.1 a 0.000 CHa (g) 53.8 49.0 50.3 Protein (g) 8.8 ± 0.1 a 10.7 ± 0.1 c 6.20 ± 0.2 a 0.000 Lipid (g) 13.8 ± 0.2 a 14.5 ± 0.1 a 20 ± 2 b 0.000 Ash (g) 1.5 ± 0.1 1.50 ± 0.03 ND DF (g) 2.10 ± 0.03 b 3.50 ± 0.06 c 0.8 ± 0.2 a 0.000 IDF (g) 1.30 ± 0,01 2.70 ± 0.05 ND 0.000 SDF (g) 1.10 ± 0,04 0.70 ± 0.06 ND 0.001 Mean ± SD; n = 3. CHa Carbohydrates available calculated by difference (100-Moisture-Protein-Lipids-Dietary fibre-Ashes).DF: dietary fibre; IDF: insoluble dietary fibre; SDF: soluble dietary fibre. °Average of four commercial branches. Values with different letters in the same line are significantly different p < 0.05 (Tukey’s test). 3.2. Materials and elaborate muffins proximate analysis Muffins are an option widely consumed; they could be used as a suitable vehicle to supply good quality nutrients to consumers. The chemical composition of the prepared muffins, compared with a control made using 100% wheat flour and with the average values for commercial muffins, are presented in Table 1 . It can be seen that there were significant differences in all the parameters analysed. Moisture, protein, and fibre content were higher in a muffin elaborated with the addition of non-conventional mixed flours. Meanwhile, the content of fat was higher in commercial muffins. Although the moisture content in the muffins from this study was high, it was lower compared to the results reported by Mihaylova et al. [ 11 ], Yalcin et al. [ 18 ], Gomes et al. [ 19 ] and Kaur et al. [ 20 ] in muffins where wheat flour was replaced with a blend of chia with peach flours, grape seeds, green pea, or apple peel, respectively. The water content is an essential parameter for the shelf life of the muffin, Low moisture content reduces the rate of chemical reactions and inhibits mould growth. In this study, the dietary fibre content of the muffins was higher than that of both the control and commercial varieties. Similar results were reported by Gomes et. al [ 19 ] and Grasso et al. [ 21 ]. Dietary fibre may promote intestinal health by inducing changes, both directly and indirectly, through interactions with the gut microbiota. Additionally, it is widely recognized as an important part of a healthy diet and based on currently scientific published evidence suggest that they physiological effects include shortened intestinal transit time, enhanced colonic fermentation with the production of short-chain fatty acids (SCFAs), promotion of weight loss and satiety, improved mineral absorption, and a protective role in preventing colon cancer [ 22 , 23 ]. In this study, significantly higher insoluble dietary fibre in the enhanced muffin was found. This result was as expected since the whole-grain quinoa and white bean flours utilised provided sources of insoluble fibre. According to Weickert, Pfeiffer [ 24 ] these insoluble cereal fibres from wheat extracts and whole grain products are non-fermentable in vivo and in vitro, and that mainly appears to improve insulin resistance and reduce the risk of developing type 2 diabetes. Perhaps one explanation for the metabolic benefits of insoluble cereal fibres (including alteration of metabolite profiles), stems from their association with increased faecal bulk and, therefore, microbial mass of specific bacteria such as Xylanibacter and Prevotella. Regarding the Argentinian Food Code in Chapter IX: “Farinaceous Foods - cereals, flours and derivatives”, under the title: “Cookies, biscuits and bakery pastries”, which includes articles 760, 760 bis, 762 and 766, they are considered beaten products. A muffin is a classic oil-in water emulsion consisting of a mixture of eggs, water, fat, and sugar, in which the flour particles are dispersed [ 25 ]. This food offers an ideal base for incorporating nutritional and bioactive compounds. However, wheat has limitations, such as poor protein quality due to lysine deficiency. By partially replacing wheat with a blend of beans and quinoa, these nutritional shortcomings can be improved. This is a positive outcome, as non-conventional flours from the food industry, such as apple peel, grape pomace, and pea flours, have recently been shown to enhance the nutritional content of baked goods.A serving size of three muffins (approximately 114 g) provides 22% and 16% of the recommended daily intake (RDI) for protein and fibre, respectively, for an adult with an average body weight of 70 kg and moderate physical activity, based on daily recommendations of 0.8 g protein/kg and 25 g fibre. Therefore, the incorporation of white bean and quinoa flour in muffin can be a good alternative to increase the nutritional value of the product developed. 3.3. Determination of total phenolic compounds and antioxidant activity The results for total phenolic content and antioxidant activity are presented in Table 3 . Total phenolic content was higher in the formulated muffins (113.44 mg GAE/100 g dry matter) compared to the control (33.46 mg GAE/100 g dry matter), while antioxidant activity showed no significant difference. Many studies have documented an increase in the concentration of total phenolic compounds probably due to the inclusion of quinoa [ 26 ]. Quinoa contains a variety of polyphenols, including flavonoids like phenolic acids, kaempferol derivatives, rutin, quercetin, and myricetin [ 27 ]. The concentrations of these compounds in our study are comparable to those previously reported. While the most prominent phenolic compounds in beans are anthocyanins and tannins, whose incidence can influence the colour, aroma and nutritional quality of this legume [ 28 ]. Regarding phenolic content levels, our findings indicate that the enhanced muffin exhibits a four-fold increase in Total Phenolic Content (TPC) compared to the control, consistent with the results reported by Barros et al. [ 29 ] in muffins with partial substitution of wheat flour by different classes of bean flour. Table 3 Total phenolic contents and antioxidant activity of muffins Samples TPC (mg GAE/100g sample) Antioxidant activity % scavenging DPPH radical * 100 mg L − 1 Control Muffins 33.5 ± 3 0% WF/WBF/QF muffins 113.4 ± 20 2% Quercetin 92% The data correspond to average values of three analyzes along with the standard deviation of each type of muffin prepared. WBF = White bean flour; HH = Wheat flour; QF = Quinoa flour. GAE: gallic acid equivalent. *The percentage values of discoloration of the DPPH radical were corrected considering quercetin as standard with a value of 100% antioxidant capacity. Polyphenols are known to be sensitive to temperatures above 65°C, suggesting that the muffin-baking process may reduce the availability of free flavonoids. As these compounds primarily contribute to the sample’s DPPH free radical scavenging capacity, this may explain the minimal observed DPPH radical discoloration (2%). In another study, Pawłowska et al. [ 30 ] reported analogous antioxidant results when replacing wheat flour with cocoa in muffins; however, the antioxidant activity of carob was shown to be significantly higher. 3.4. Determination of physical parameters, texture and colour in muffins elaborated The results showed significant differences in the height of the muffin, being lower when quinoa and bean flour were added. The weights of both muffins were similar, with no significant differences in baking loss (4.5 g) and a baking yield of around 96% was obtained (Table 4 ). The evaluation of the textural parameters of the muffins elaborated with bean and quinoa mix flour showed significant differences in hardness and chewiness compared to the control muffin, obtaining softer and smoother crumbs in the control muffin. Lower cohesiveness is associated with increased crumbliness due to greater hardness. Several studies in the scientific literature report that the addition of fibre-rich by-products—such as apple peel, grape seed, or whole oat wheat—to sweet bakery products increases hardness [ 11 , 18 , 20 ]. According to Goswami et al. [ 31 ] chewiness reflects the effort required to chew food and form a bolus before swallowing. In this study, the enhanced muffins required more chewing force compared to the control, a finding consistent with Kaur et al. [ 20 ] who observed similar results in muffins incorporating apple peel powder. Table 4 Physical properties and texture of the prepared muffins. Control Muffins WF/WBF/QF muffins p- value Weight (g) 38.3 ± 0.3 38.6 ± 0.3 Height (mm) 43.0 ± 0.7 38.1 ± 0.5 0.000 Baking loss (%) 4.5 ± 0.8 3.8 ± 0.9 Baking yield (%) 95.5 ± 0.8 96.2 ± 0.9 Hardness (N) 14 ± 1 22 ± 2 0.000 Elasticity 0.9 ± 0.1 0.9 ± 0.1 Cohesiveness 0.8 ± 0.1 0.6 ± 0.1 0.000 Gumminess 10.5 ± 0.8 13.8 ± 0.9 0.001 Chewiness (N) 9.6 ± 0.5 11.9 ± 0.5 0.025 Resilience 0.4 ± 0.1 0.2 ± 0.1 0.000 The data correspond to average values ​​of six analyzes along with the standard deviation from each type of muffin. WBF = White bean flour; WF = Wheat flour; QF = Quinoa flour. Significantly different p < 0.05 (Tukey’s test). Table 5 is shown the colour measured in the muffins made. The results showed significant differences in the four parameters evaluated. The total colour difference (ΔE) of sample pairs leads to foresee whether or not there is a difference in the colour of the muffins that can be perceived by the human eye according to specific thresholds i.e., ΔE < 1 normally invisible difference; 1 < ΔE < 2 very small difference, only obvious to a trained eye; 2 < ΔE < 3.5 medium difference, also obvious to an untrained eye; 3.5 < ΔE 6 a very obvious difference [ 32 ]. The addition of beans and quinoa mix flours produced a small difference in the crumbs but an obvious difference in upper and lower crusts with respect to the control muffin. At same other authors the L* values in the crumbs were higher in muffin made with a blend of non-conventional flours [ 19 , 30 ]. Table 5 Colour of the crusts and crumbs of prepared muffins Upper crust Crumb Lower crust L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE Control muffins 64.4 ± 0.3 12.0 ± 0.3 42.1 ± 0.9 2.1 ± 0.9 75.7 ± 0.1 1.6 ± 0.3 31.3 ± 0.9 2.0 ± 0.6 62.9 ± 0.8 13.9 ± 0.5 40.4 ± 0.2 1.4 ± 0.5 WF/WBF/QF muffins 64.1 ± 0.3 11.7 ± 0.2 40 ± 1 74.4 ± 0.3 3.0 ± 0.1 31.2 ± 0.5 62.1 ± 0.6 14.5 ± 0.4 39.4 ± 0.8 p -value 0.009 0.043 The data correspond to average values ​​of six analyses along with the standard deviation from each type of muffin elaborated. WBF = White bean flour; WF = Wheat flour; QF = Quinoa flour. Significantly different p < 0.05 (Tukey’s test). 3.5. Acceptability global and sensorial analysis Figure 1 is shown in the radar plot form the mean values for each sensory attribute and the overall acceptability on the hedonic scale for the muffins tested. All attributes, except for flavour and aroma, received ratings of 6 or higher. Flavour and aroma received moderate scores of 5.3 and 5.7, respectively, which may reflect the limited familiarity of consumers with legumes and Andean grains, such as quinoa, in their usual diets. Despite saponin being removed to reduce the inherent bitterness of quinoa, participants detected distinctive flavour notes and an aftertaste from the grain. Although differences were observed in the perception of aroma, flavour, and texture descriptors in the enhanced muffin, these did not affect overall consumer acceptance, as reflected by an acceptability index of 71%. Further formulation adjustments could enhance these sensory attributes, improving consumer appeal and facilitating the broader integration of quinoa and bean ingredients into mainstream food markets. In terms of hardness, significant differences were observed in both instrumental measurements and sensory attributes among the muffins tested. Textural properties of food are a group of physical characteristics determined by the structural elements of its components and their interactions [ 33 ]. More broadly, food texture defines the eating experience and influences consumer preferences for food products. Currently, there is no available literature on muffins formulated with a blend of white bean and quinoa flours. However, the results of this study can be compared to similar muffins with enhanced nutritional and sensorial properties reported by Pawłowska et al. [ 30 ] and Barros et al. [ 29 ]. With the development of these enhanced muffins, it was intended to achieve the revaluation of the bean and quinoa varieties, both underutilized regional products. In addition, their nutritional importance and the agro-industrial potential that they present according to their genetic material are highlighted. 4. CONCLUSIONS The muffins produced from low-cost, regionally significant crops exhibited good quality. The blend of bean and quinoa flour serves as a nutritious and functional addition for partial replacement of wheat flour. This formulation offers a high-quality source of protein and fibre with low lipid content, making it potentially beneficial for vulnerable groups, such as schoolchildren to help prevent chronic non-communicable and deficiency diseases. The enhanced muffins also showed a significant increase in total phenolic content. Global acceptability and sensory analysis of the formulated muffin demonstrated a positive consumer response, achieving an overall score of 6.4 on a 9-point hedonic scale and a 71% acceptability index. However, future refinements should prioritise enhancements in flavour and aroma to further increase overall consumer appeal and acceptance. Additionally, it can be concluded that this formulation represents a valuable food alternative that promotes the cultivation of ancestral crops while supporting the regional economy. Declarations COMPLIANCE WITH ETHICAL STANDARDS Human and Animal Rights This article does not contain any studies with human or animal subjects. Conflict of Interest The authors declare that they have no conflict of interest. Author Contribution Author Contributions: Conceptualization, M.N.B., A.M.R., A.C.G.M. and M.L.T; methodology, M.N.B., M.E.A., LC., E.C.M. and. N.N.F.; Software, M.N.B.; validation, M.N.B., A.M.R., A.C.G.M. and M.L.T.; investigation, M.N.B.; resources, M.N.B., A.M.R., A.C.G.M. and M.L.T.; Writing—original draft preparation, M.N.B.; writing—review and editing, M.N.B., A.C.G.M., N.N.F. and M.L.T.; visualization, M.N.B. and M.L.T.; supervision, M.N.B., A.M.R., A.C.G.M. and M.L.T.; funding acquisition, A.M.R., A.C.G.M. and M.L.T. All authors have read and agreed to the published version of the manuscript. Acknowledgement The authors wish to express their gratitude to the staff of the Centro Interdisciplinario de Investigaciones en Tecnologías y Desarrollo Social para el NOA (CIITED) for their invaluable assistance with the texture and colour analysis. References Bassett MN, Romaguera D, Giménez M, Lobo MO, Samman NC (2014) Prevalencia y determinantes de la doble carga de malnutrición en hogares en La Puna y Quebrada de Humahuaca, Jujuy, Argentina. Nutrición hospitalaria 29 (2):322-330 https://www.aulamedica.es/nh/pdf/7075.pdf ENNyS2 (2019) Segunda Encuesta Nacional de Nutrición y Salud. (2019). Resumen Ejecutivo. Secretaría de Gobierno de Salud. Ministerio de Salud y Desarrollo Social. Presidencia de la Nación. https://cesni-biblioteca.org/2-encuesta-nacional-de-nutricion-y-salud-ennys-2-resumen-ejecutivo/ Accessed 2 May 2024 Somnuk S, Komindr S, Monkhai S, Poolsawat T, Nakphaichit M, Wanikorn B (2023) Metabolic and inflammatory profiles, gut microbiota and lifestyle factors in overweight and normal weight young thai adults. PLoS One 18 (7):e0288286 https://doi.org/10.1371/journal.pone.0288286. Almonacid-Fierro A, González-Almonacid J (2022) Obesidad Infantil: repercusiones post-pandemia y el factor escuela. Andes pediatrica 93:440-441 https://doi.org/10.34172/hpp.2022.29. Machado ÍE, Parajára MDC, Guedes LFF, Meireles AL, Menezes MC, Felisbino-Mendes MS et al. (2022) Burden of non-communicable diseases attributable to dietary risks in Brazil, 1990-2019: an analysis of the Global Burden of Disease Study 2019. Rev Soc Bras Med Trop 55 (suppl 1):e0282. https://doi.org/10.1590/0037-8682-0282-2021 García-Villanova B, E.J. GH, Verardo V (2020) Compuestos Bioactivos de Los Granos de Cereales En Papel De Los Cereales De Grano Entero En La Salud. Granada, España Mir NA, Riar CS, Singh S (2018) Nutritional constituents of pseudo cereals and their potential use in food systems: A review. Trends in Food Science & Technology 75:170-180 https://doi.org/10.1016/j.tifs.2018.03.016 Bassett MN, Giménez MA, Romaguera D, Sammán N (2013) Estado nutricional e ingesta alimentaria de poblaciones de regiones de altura del Noroeste Argentino. Archivos latinoamericanos de nutrición 63 (2):114-125 Bassett MN, Espejo LM, Rossi AM, Sammán NC (2019) Evaluación de la calidad proteica de harinas de legumbres con software MixProtLUNA. Aplicación en panes reducidos en sodio. INVESTIGACIONES EN LAS FACULTADES DE INGENIERÍA DEL NOA (5):171-174 Temple NJ (2022) A rational definition for functional foods: A perspective. Frontiers in nutrition 9:957516 https://doi.org/10.3389/fnut.2022.957516 Mihaylova D, Popova A, Goranova Z, Doykina P, Goranov B (2023) Characterization of Muffins Reformulated with Chia and Lyophilized Peach Powder in Terms of Some Technological and Sensory Aspects §. Food Technology and Biotechnology 61 (3):273-282 https://doi.org/10.17113/ftb.61.03.23.7843 Bassett MNE, L.M.; Sammán, N.C. (2015) Estudio de la calidad de diferentes mezclas de harinas proteicas aplicando el software MixProtLUNA. . In: In Proceedings of the V Congreso Internacional de Ciencia y Tecnología de los Alimentos.. Investigaciones y avances en nutrición y salud: trabajos completos del V CICYTAC 2014. Córdoba, Argentina, pp 3–10. Leser S (2013) The 2013 FAO report on dietary protein quality evaluation in human nutrition: Recommendations and implications. Nutrition Bulletin 38 (4):421-428 https://doi.org/10.1111/nbu.12063 Tawaha K, Alali FQ, Gharaibeh M, Mohammad M, El-Elimat T (2007) Antioxidant activity and total phenolic content of selected Jordanian plant species. Food chemistry 104 (4):1372-1378 https://doi.org/10.1016/j.foodchem.2007.01.064 Tapia A, Rodriguez J, Theoduloz C, Lopez S, Feresin GE, Schmeda-Hirschmann G (2004) Free radical scavengers and antioxidants from Baccharis grisebachii. Journal of Ethnopharmacology 95 (2-3):155-161 https://doi.org/10.1016/j.jep.2004.06.035 Kumazawa S, Hamasaka T, Nakayama T (2004) Antioxidant activity of propolis of various geographic origins. Food chemistry 84 (3):329-339 https://doi.org/10.1016/S0308-8146(03)00216-4 Schmeda-Hirschmann G, Rodriguez J, Theoduloz C, Astudillo S, Feresin G, Tapia A (2003) Free-radical scavengers and antioxidants from Peumus boldus Mol.(" Boldo"). Free radical research 37 (4):447-452 https://doi.org/10.1080/1071576031000090000 Yalcin E, Ozdal T, Gok I (2022) Investigation of textural, functional, and sensory properties of muffins prepared by adding grape seeds to various flours. Journal of Food Processing and Preservation 46 (5):e15316 https://doi.org/10.1111/jfpp.15316 Gomes DdS, Rosa LS, Cordoba LdP, Fiorda-Mello F, Spier MR, Waszczynskyj N (2021) Development of muffins with green pea flour and their physical and sensory evaluation and essential amino acid content. Ciência Rural 52 (7):e20200693 https://doi.org/10.1590/0103-8478cr20200693 Kaur M, Kaur M, Kaur H (2022) Apple peel as a source of dietary fiber and antioxidants: Effect on batter rheology and nutritional composition, textural and sensory quality attributes of muffins. Journal of Food Measurement and Characterization 16 (3):2411-2421 https://link.springer.com/article/10.1007/s11694-022-01329-x Grasso S, Pintado T, Pérez-Jiménez J, Ruiz-Capillas C, Herrero AM (2021) Characterisation of muffins with upcycled sunflower flour. Foods 10 (2):426 https://doi.org/10.3390/foods10020426 Abreu A, Milke-García M, Argüello-Arévalo G, Calderón-de la Barca A, Carmona-Sánchez R, Consuelo-Sánchez A et al. (2021) Fibra dietaria y microbiota, revisión narrativa de un grupo de expertos de la Asociación Mexicana de Gastroenterología. Revista de Gastroenterología de México 86 (3):287-304 https://doi.org/10.1016/j.rgmx.2021.02.004 Barber TM, Kabisch S, Pfeiffer AF, Weickert MO (2020) The health benefits of dietary fibre. Nutrients 12 (10):3209 https://doi.org/10.1093/jn/nxx008 Weickert MO, Pfeiffer AF (2018) Impact of dietary fiber consumption on insulin resistance and the prevention of type 2 diabetes. The Journal of nutrition 148 (1):7-12 https://doi.org/10.1093/jn/nxx008 CAA Código Alimentario Argentino. Capítulo IX. Artículos: 643 al 766 - Alimentos Farináceos - Cereales, Harinas y Derivados. https://www.argentina.gob.ar/sites/default/files/anmat_capitulo_ix_harinas.pdf . Accessed 20 September 2024 Bhathal SK, Kaur N, Gill J (2017) Effect of processing on the nutritional composition of quinoa (Chenopodium quinoa Willd). Agric Res J 54 (1):90-93 https://doi.org/10.5958/2395-146X.2017.00015.1 Hussain MI, Farooq M, Syed QA, Ishaq A, Al-Ghamdi AA, Hatamleh AA (2021) Botany, Nutritional Value, Phytochemical Composition and Biological Activities of Quinoa. Plants (Basel) 10 (11). https://doi.org/10.3390/plants10112258 Barrueto-Gonzalez NB (2008) Biodisponibilidade de minerais das fontes leguminosas. Revista Simbiologias 1 (1) https://doi.org/10.32905/1983-3253/2008.01.01p174 Barros LFTd, Escobar TD, Ribeiro PFdA, Kaminski TA (2018) Muffins adicionados de farinha de feijão de diferentes classes. Brazilian Journal of Food Technology 21:e2017081 https://doi.org/10.1590/1981-6723.08117 Pawłowska K, Kuligowski M, Jasińska-Kuligowska I, Kidoń M, Siger A, Rudzińska M et al. (2018) Effect of replacing cocoa powder by carob powder in the muffins on sensory and physicochemical properties. Plant Foods for Human Nutrition 73:196-202 https://doi.org/10.1007/s11130-018-0675-0 Goswami D, Gupta R, Mridula D, Sharma M, Tyagi S (2015) Barnyard millet based muffins: Physical, textural and sensory properties. LWT-Food Science and Technology 64 (1):374-380 https://doi.org/10.1016/j.lwt.2015.05.060 Ashe T (2014) Color management & quality output: Working with color from camera to display to print. Routledge. https://doi.org/10.4324/9780240821368 Yang X, Li A, Li X, Sun L, Guo Y (2020) An overview of classifications, properties of food polysaccharides and their links to applications in improving food textures. Trends in Food Science & Technology 102:1-15 https://doi.org/10.1016/j.tifs.2020.05.020 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Nov, 2024 Editor assigned by journal 05 Nov, 2024 Submission checks completed at journal 03 Nov, 2024 First submitted to journal 30 Oct, 2024 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. 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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-5364412","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374455797,"identity":"8692b198-519c-435b-9bf0-8736dc326337","order_by":0,"name":"María Natalia Bassett","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJACZijN+ABI8PARowGihY2Z2QCkhY0ULWwSYJqQBnP288ekC2rq7Pnl+49Vfs2xk2FjYH746AYeLZY9yWzSM44dTpzZxsx2W3ZbMtBhbMbGOXi0GBwAauFhO5BgcAyoRXIbM1ALD5s0Xi3nHwO1/KuztwdqKZbcVk+ElhtAW3jbmBk3AL3P+HHbYWK0PDa2ntl3OHHGsWRjacZtx3mAWgn45Xziw9sF34Ah1nzw4cef26rt+dmbHz7GpwUIWCRgLGYeMIlfOVjJBxiL8Qdh1aNgFIyCUTACAQCxSz9tsrVKJwAAAABJRU5ErkJggg==","orcid":"","institution":"Instituto Superior de Investigaciones Biológicas. (INSIBIO), CONICET. Universidad Nacional de Tucumán","correspondingAuthor":true,"prefix":"","firstName":"María","middleName":"Natalia","lastName":"Bassett","suffix":""},{"id":374455798,"identity":"93d685ab-dae4-4b70-a2c5-e8ee94377995","order_by":1,"name":"María Elina Acuña","email":"","orcid":"","institution":"Instituto Superior de Investigaciones Biológicas. (INSIBIO), CONICET. Universidad Nacional de Tucumán","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Elina","lastName":"Acuña","suffix":""},{"id":374455799,"identity":"1954606a-ba48-4c41-a88c-95de895fc094","order_by":2,"name":"Lucrecia Corral","email":"","orcid":"","institution":"Facultad de Ciencias Exactas y Tecnología (FACET). UNT","correspondingAuthor":false,"prefix":"","firstName":"Lucrecia","middleName":"","lastName":"Corral","suffix":""},{"id":374455800,"identity":"91414055-7e59-42f8-baec-a6d5f764594f","order_by":3,"name":"Elvecia Carmen Moreno","email":"","orcid":"","institution":"Facultad de Ciencias Exactas y Tecnología (FACET). UNT","correspondingAuthor":false,"prefix":"","firstName":"Elvecia","middleName":"Carmen","lastName":"Moreno","suffix":""},{"id":374455801,"identity":"f51cc4b2-6fdd-45bb-b09d-aea6efccbc5e","order_by":4,"name":"Noelia Natalia Fernández","email":"","orcid":"","institution":"Facultad de Ciencias Exactas y Tecnología (FACET). UNT","correspondingAuthor":false,"prefix":"","firstName":"Noelia","middleName":"Natalia","lastName":"Fernández","suffix":""},{"id":374455802,"identity":"139b2275-92ef-4742-a543-d8566eb38a34","order_by":5,"name":"Analia Mabel Rossi","email":"","orcid":"","institution":"Facultad de Bioquímica, Química y Farmacia. UNT","correspondingAuthor":false,"prefix":"","firstName":"Analia","middleName":"Mabel","lastName":"Rossi","suffix":""},{"id":374455803,"identity":"f24a1bd3-f239-444e-888e-81c10175742a","order_by":6,"name":"Ana Clelia Gómez Marigliano","email":"","orcid":"","institution":"Universidad Nacional de Tucumán. INFINOA – CONICET Tucumán","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Clelia Gómez","lastName":"Marigliano","suffix":""},{"id":374455804,"identity":"6013411c-d823-42c8-aa19-79d8ef11034e","order_by":7,"name":"María Laura Tereschuk","email":"","orcid":"","institution":"Facultad de Ciencias Exactas y Tecnología (FACET). UNT","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Laura","lastName":"Tereschuk","suffix":""}],"badges":[],"createdAt":"2024-10-31 03:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5364412/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5364412/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69006009,"identity":"d7dc77df-0e82-4f96-9a8a-ff54ba61200a","added_by":"auto","created_at":"2024-11-14 12:44:47","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148695,"visible":true,"origin":"","legend":"\u003cp\u003eAppearance of white bean and quinoa seeds, flours and muffins elaborated.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5364412/v1/90a0cf9bebd6f644316903e0.jpeg"},{"id":69006010,"identity":"be702a45-e682-40fc-9d19-6ee3c9b5fb2e","added_by":"auto","created_at":"2024-11-14 12:44:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142470,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Acceptability and Sensorial Analysis in Muffins elaborated.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5364412/v1/6d792acad2afe7e86bdaabf7.png"},{"id":69006285,"identity":"1fef373f-09aa-4dc8-93f7-93f2a0798360","added_by":"auto","created_at":"2024-11-14 12:52:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1025464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5364412/v1/c6d0d895-1f85-41a3-b803-f826a87407ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional Optimization of Muffins using Wheat, Bean and Quinoa Flour: A Strategy for Enhancing Protein Quality and Healthy Benefits","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe world population has grown steadily in recent years. The high incidence of obesity and metabolic syndrome in modern society has been aggravated not only by a sedentary lifestyle but also by changes in eating habits. Globally, most markets offer a wide variety of foods and beverages, that combine flavour, comfort and novelty. However, at the same time there is a wide availability and widespread marketing of many of these products, and especially those with a high content of fat, simple sugars, and/or salt. These dietary patterns have an impact on the nutritional status of the Argentine population in general and the NOA population in particular, which have high rates of overweight and obesity and coexist with high rates of malnutrition and low weight, as well as some micronutrient deficits [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. An unhealthy diet is a key modifiable risk factor for non-communicable diseases (NCDs). If not addressed, poor nutrition\u0026mdash;along with other risk factors\u0026mdash;increases the prevalence of NCDs in populations through mechanisms such as increased blood pressure, higher blood glucose levels, altered blood lipid profiles, and overweight or obesity. Although deaths from NCDs mainly occur in adulthood, the risks associated with unhealthy diets begin in childhood and accumulate throughout life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Food and Agriculture Organization of the United Nations (FAO) actively promotes the conservation and sustainable use of biodiversity for nutrition and agriculture, as a means of increasing dietary diversity. However, there is currently a strong tendency to reduce the basis of global food security to a few species. This decrease reduces the ability of farmers and ecosystems to adapt to changes and opportunities that arise. From the point of view of research, resolving this situation requires a greater range of species; this involves revaluing and including those that were used by populations and communities in original towns. In the case of the Andean region, the vegetable species that were not taken into account for currently marketed food products are adapted to the agro-ecological conditions of the region, such as quinoa, amaranth, ka\u0026ntilde;iwa, legumes, among others, are used by local farmers and thus contribute to production. sustainability and ecosystem stability. For example, Quinoa is an important source of macronutrients, fibre, vitamins, minerals, and bioactive compounds, which has led to its current revaluation. The ranges of variation of the nutrient content depend both on the varieties and on the agroecological characteristics where it is grown. This grain has a healthy effect beyond its purely nutritional effect due to the bioactive compounds it contains. However, there is limited knowledge of the nutritional values of these species and a lack of strategies for their inclusion in food and nutrition programs. The physiological effects of these whole grains and their derived products are mainly due to the supply of nutrients, the mechanical effect on the gastrointestinal system due to the fibre, and the antioxidant effect of the phenolic compounds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Also, the legume flours provide protein, fibre and micronutrients while reducing fat consumption. They are the fundamental protein and caloric base of human nutrition, and the main source of energy and essential amino acids provided in a balanced and nutritious way, especially when they are combined with cereals. This combination of cereal and legume proteins enhances the biological value of each and creates an opportunity to develop novel foods with high nutritional and organoleptic quality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies show that despite being abundantly produced in the NOA region, these foods are very little consumed by the population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The study of protein blends of flours from different legumes, with cereals and Andean grains, among others, is determined using the MixProtLUNA.1-2013 computer tool and has allowed its application in various artisanal bread recipes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFunctional food is generally defined as food that provides health benefits beyond basic nutrition, often due to the presence of biologically active components. They contain substances that collaborate in the prevention of diseases and are found in sufficient quantities to achieve the desired objective [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBaked cereal products are generally chosen as ideal material over others owing to their wide consumer acceptance. However, developing a marketable product with potential health benefits requires a thorough investigation of multiple product parameters. Nowadays, many studies are focused on the effort to include ingredients with beneficial effects on human health arising from the need to reformulate preferred but unhealthy foods. Muffins appear as an attractive alternative to include the use of different non-conventional flours such as those obtained from chia, green banana, chickpeas, and byproducts of the industry among others [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor all the above mentioned, the objective of this study was to develop and optimise muffins with enhanced nutritional quality through protein complementation utilising regionally produced ingredients that are underutilized for domestic consumption, and to provide essential nutrients to help prevent chronic and deficiency diseases in vulnerable populations.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Assessment and selection theoretical of the protein quality of muffins formulations\u003c/h2\u003e \u003cp\u003eThe variability of the protein quality of wheat flour and its mixtures with legume and quinoa flour was studied. Protein quality was quantified based on the amount and profile of indispensable amino acids (IAAs), as well as the actual ileal digestibility of protein IAAs using the \u0026ldquo;Digestible Indispensable Amino Acid Score\u0026rdquo; (DIAAS). The value for each aai in the diet was calculated and the lowest value is designated as the DIAAS. For this, the computer tool MixProtLUNA.1-2013 created by the working group [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] was used. Its database was elaborated from a bibliographic compilation of the composition of IAAs of cereals, Andean grains and legumes of different origins. Some digestibility values were determined \"in vivo\" by assays with white rats and others from the literature. The FAO/WHO/UNU (2013) pattern was used as a reference [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The calculation of the DIAAS of the different samples was carried out using the following Eq.\u0026nbsp;(1):\u003c/p\u003e \u003cp\u003eDIAAS %= 100 x lowest value [(mg of digestible dietary indispensable amino acid in 1 g of the dietary protein)/ (mg of the same dietary indispensable amino acid in 1 g of the reference protein)] (1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ingredients and elaborate muffins proximate analysis\u003c/h2\u003e \u003cp\u003eThe commercials wheat leaving flour, white bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e), quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e) also food grade bakery ingredients, likely, refined powdered sugar, whole milk, sunflower oil, eggs, and vanilla flavour were purchased at local markets in Tucum\u0026aacute;n, Argentina. White quinoa was previously treated to remove the saponins and dust by washing with water to extract antinutritional factors (saponins) and dried. The integral flours of white beans and quinoa were made by grinding in a blade grinder and sieving them in a No. 45 sieve. Wheat flour with bean and quinoa flours was partially replaced, which resulted in obtaining optimal designed and formulated mixtures. Taking into account the theoretical evaluation of the protein composition obtained, an optimal muffin prototype was developed and compared to the control using 100% wheat flour.\u003c/p\u003e \u003cp\u003eThe elaborate muffins were kept in a plastic tray and covered with film, maintaining the temperature at 4\u0026deg; C. Mass yield, product yield and specific volume calculations were performed. Muffin weight was determined using a laboratory digital scale (Mettler Toledo PE360 1 +/- 0.001 g), and muffin height was measured with a vernier calliper from the highest point of the muffin to the bottom. The baking loss and baking yield were calculated according to Eq, (2) and (3) respectively, using the batter weight.\u003c/p\u003e \u003cp\u003eBaking loss (%) = (batter weight \u0026ndash; muffin weight)/batter weight) \u0026times; 100 (2)\u003c/p\u003e \u003cp\u003eBaking yield (%) = (muffin weight/batter weight) \u0026times; 100 (3)\u003c/p\u003e \u003cp\u003eThe muffins were also analysed for protein, fibre, fat, ash, and moisture using AOAC (2016). Carbohydrate available content (g/100 g) was calculated by subtracting the contents of moisture, fat, dietary fibre, ash, and protein from 100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Determination of total phenolic compounds (TPC) and antioxidant activity\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Determination of TPC\u003c/h2\u003e \u003cp\u003eTotal phenolic compound content was determined by the modified Folin\u0026ndash;Ciocalteau method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The reaction mixture contained 0,5 mL of each extract (0,3 mg/mL), 0,5mL of Folin\u0026ndash;Ciocalteau reagent, 0,5 mL of sodium carbonate (10%) and 3,5 mL of distilled water. The reaction mixture was heated at 30\u0026ordm;C for 1 hour in a water bath. Absorbance was measured at 765 nm. A Hitachi U1900 spectrophotometer was utilised for each quantitative measurement performed, based on a standard calibration curve of six points: 20, 100, 200, 300, 400, 500 mg/L of gallic acid in 80% ethanol. TPC was expressed as mg GAE /100g of dry material [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. GAE: gallic acid equivalent\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Determination of Antioxidant Activity\u003c/h2\u003e \u003cp\u003eThe H-donor activity of extracts was measured by the 1,1-diphenyl-2-picrylhydrazyl (DPPH) method according to Tapia et [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The extracts were assayed at 100 mg/L. 1,5 mL of a freshly prepared DPPH solution (20 mg/l) was added. The reaction mixture was incubated for 30 min at 30\u0026ordm;C [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The quenching of free radicals by sample extracts and compounds was evaluated spectrophotometrically at 517 nm against the absorbance of the DPPH radical [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Quercetin was used as a reference compound (1 mg/l). The percentage of DPPH discoloration was calculated using Eq.\u0026nbsp;(4):\u003c/p\u003e \u003cp\u003ePercentage of discoloration (%)\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (Ac / Ab)*100 (4)\u003c/p\u003e \u003cp\u003eWhere Ac\u0026thinsp;=\u0026thinsp;absorbance of compound/extracts\u003c/p\u003e \u003cp\u003eAb\u0026thinsp;=\u0026thinsp;absorbance of the blank at 515 nm. Values are reported as the mean of three independent determinations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Determination of texture and colour\u003c/h2\u003e \u003cp\u003eTextural characteristics of the muffins were determined at 25\u0026deg; C using a TA.XT2 Texturometer (Stable Micro Systems Ltd., Surrey, UK) containing a maximum 50 kg load cell. Calibrations were carried out with a 5 kg load cell. Data was analysed using the Exponent Software supplied by the team. The muffins (n\u0026thinsp;=\u0026thinsp;6) were subjected to a 25% deformation through a 2-cycle sequence using a 50-mm diameter spherical probe (Cyl. methacrylate P/50, Stable MicroSystems) at crosshead speed 0.5 mm per second. The samples were analysed after 10 h of cooking. Hardness (N) is the force at maximum deformation calculated by the integrated TA-XT2 software and was recorded in N. The following texture parameters were measured from strain force curves: hardness (N): the maximum force required to compress the sample (maximum force during the first compression cycle); elasticity (dimensionless): the height that the sample recovers during the time that elapses between the end of the first compression and the beginning of the second; cohesiveness (dimensionless): the degree to which the sample can be deformed before failure; A1/A2, A1 is the total energy required for the first compression and A2 the total energy required for the second compression); chewiness (N): the work required to chew a solid sample and bring it to a swallowing steady state (hardness x cohesiveness x elasticity).\u003c/p\u003e \u003cp\u003eColour analyses were performed with a ColorQuest XE spectrophotometer (Hunter Lab, USA) with D65 illuminant, a 0\u0026deg; standard observer and a 2.5 cm port/viewing area. The L* value is the lightness variable, with values ranging from 100 (white) to 0 (black). The a* value measures the colour of the sample, with positive values being redder and negative values being greener. The b* value also measures the colour of the sample, with positive values being more yellow and negative values being bluer. The total colour difference (∆E*) between the control sample and each of the muffins was calculated according to Eq.\u0026nbsp;(5):\u003c/p\u003e \u003cp\u003eColour difference ∆E*=[(∆L*)^2+(∆a*)^2+(∆b*)^2 ]^(1/2) (5)\u003c/p\u003e \u003cp\u003eSix samples were measured and the mean and standard deviation of the values were calculated. (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Acceptability global and sensorial analysis\u003c/h2\u003e \u003cp\u003eMuffins were analysed using a consumer preference test method. They were baked 10 hours before sensory evaluation. Both samples were divided into parts, coded, and supplied with water to each consumer at once in a white plastic plate. The sensory analysis and acceptability were completed and carried out by 98 panellists who had not been trained in the sensory analysis techniques from University Santo Tomas de Aquino, Department of Concepcion, province of Tucuman. The range was between 18 to 60 years old. The consumers were asked to score the different muffin samples based on their appearance, flavour, aroma, texture and overall acceptability using a hedonic scale of nine points (9 score is \u0026ldquo;I like very much\u0026rdquo; and 1 score is \u0026ldquo;I dislike very much\u0026rdquo;). The acceptability index (AI) was calculated using an acceptability test, calculated according to the following Eq.\u0026nbsp;(6):\u003c/p\u003e \u003cp\u003eAI = (A \u0026times; 100) / B (6)\u003c/p\u003e \u003cp\u003eWhere A represents the average score given by the consumers and B is the maximum possible score for the product.\u003c/p\u003e \u003cp\u003eThe research was conducted following the Universal Declaration of Human Rights (1948), the Nuremberg Code (1947), the Declaration of Helsinki (1964 and its amendments), and National Law 25,326, as amended by Law 26,343. Verbal informed consent was obtained from all participants before the anonymous survey, ensuring that their rights and confidentiality were fully protected throughout Google form.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalyses were performed using the IBM SPSS Advanced Statistics 23.0 (IBM Software Group, Chicago, IL. USA). The significant difference between the means was evaluated by Tukey\u0026rsquo;s test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using analysis of variance (ANOVA). All determinations were made with triplicates, using different batches of each type of muffin samples.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Assessment quality protein theoretical\u003c/h2\u003e \u003cp\u003eThe protein content of the commercial wheat flour used was 10 g/100g and obtained a DIAAS of 47%, while white bean flour with a protein content of 27.3 g/100g obtained a DIAAS of 65%. Therefore, the formulation of mixtures of cereals and legumes allows obtaining an improvement in the amino acid balance and translates into a higher value in the quality of the protein compared to that of each one separately. Theoretical combinations were made from the addition of 10\u0026ndash;90% until finding the most suitable of the proteins of each flour component of the mixture and achieving a product of greater nutritional value using the MixProtLUNA computer program. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e where it is observed that the DIAAS ranges from 63 to 85%. Theoretical recipes were formulated with the different percentages and the muffins were made with them. The muffin that resulted with baking best characteristics and that was finally made, was a 25/50/25 combination of bean proteins with wheat and quinoa flours DIAAS: 72.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigestible essential amino acid score (DIAAS) in different blends in percentage of wheat flour protein with white bean and quinoa flours protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eFOOD/VARIETY/ORIGIN/TYPE\u003c/p\u003e \u003cp\u003eWhite bean flour/ (\u003cem\u003eP. Vulgaris\u003c/em\u003e) P: 27.3, D: 65; AALim: SAA\u003c/p\u003e \u003cp\u003eWheat flour (\u003cem\u003eTriticum\u003c/em\u003e spp) P:10, D:47; AALim: Lys\u003c/p\u003e \u003cp\u003eQuinoa flour (\u003cem\u003eChenopodium quinoa\u003c/em\u003e) P:14, D:67; AALim: SAA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eWF/WBF/QF RATIOS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10/60/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/55/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/40/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25/50/25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30/40/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35/30/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40/25/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40/20/40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDIAAS %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eWBF\u0026thinsp;=\u0026thinsp;White bean flour; WF\u0026thinsp;=\u0026thinsp;Wheat flour; QF\u0026thinsp;=\u0026thinsp;Quinoa flour; DIAAS\u0026thinsp;=\u0026thinsp;Digestible Indispensable Amino Acid Score. AALim: limiting amino acids. Lys: lysine; SAA: Sulphur amino acids (Methionine\u0026thinsp;+\u0026thinsp;cysteine) P\u0026thinsp;=\u0026thinsp;Protein g/100g food.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProximal composition (g/100g fresh weight) of different types of muffin\u003cb\u003es\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl muffins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWF/WBF/QF muffins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommercial muffins\u0026deg;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal/kJ))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e374 (1574) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e369 (1551) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e409 (1712) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHa (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDF (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; n\u0026thinsp;=\u0026thinsp;3. CHa Carbohydrates available calculated by difference (100-Moisture-Protein-Lipids-Dietary fibre-Ashes).DF: dietary fibre; IDF: insoluble dietary fibre; SDF: soluble dietary fibre. \u0026deg;Average of four commercial branches. Values with different letters in the same line are significantly different p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Tukey\u0026rsquo;s test).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Materials and elaborate muffins proximate analysis\u003c/h2\u003e \u003cp\u003eMuffins are an option widely consumed; they could be used as a suitable vehicle to supply good quality nutrients to consumers. The chemical composition of the prepared muffins, compared with a control made using 100% wheat flour and with the average values for commercial muffins, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It can be seen that there were significant differences in all the parameters analysed. Moisture, protein, and fibre content were higher in a muffin elaborated with the addition of non-conventional mixed flours. Meanwhile, the content of fat was higher in commercial muffins. Although the moisture content in the muffins from this study was high, it was lower compared to the results reported by Mihaylova et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Yalcin et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Gomes et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Kaur et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in muffins where wheat flour was replaced with a blend of chia with peach flours, grape seeds, green pea, or apple peel, respectively. The water content is an essential parameter for the shelf life of the muffin, Low moisture content reduces the rate of chemical reactions and inhibits mould growth. In this study, the dietary fibre content of the muffins was higher than that of both the control and commercial varieties. Similar results were reported by Gomes et. al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Grasso et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Dietary fibre may promote intestinal health by inducing changes, both directly and indirectly, through interactions with the gut microbiota. Additionally, it is widely recognized as an important part of a healthy diet and based on currently scientific published evidence suggest that they physiological effects include shortened intestinal transit time, enhanced colonic fermentation with the production of short-chain fatty acids (SCFAs), promotion of weight loss and satiety, improved mineral absorption, and a protective role in preventing colon cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, significantly higher insoluble dietary fibre in the enhanced muffin was found. This result was as expected since the whole-grain quinoa and white bean flours utilised provided sources of insoluble fibre. According to Weickert, Pfeiffer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] these insoluble cereal fibres from wheat extracts and whole grain products are non-fermentable in vivo and in vitro, and that mainly appears to improve insulin resistance and reduce the risk of developing type 2 diabetes. Perhaps one explanation for the metabolic benefits of insoluble cereal fibres (including alteration of metabolite profiles), stems from their association with increased faecal bulk and, therefore, microbial mass of specific bacteria such as \u003cem\u003eXylanibacter\u003c/em\u003e and \u003cem\u003ePrevotella.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eRegarding the Argentinian Food Code in Chapter IX: \u0026ldquo;Farinaceous Foods - cereals, flours and derivatives\u0026rdquo;, under the title: \u0026ldquo;Cookies, biscuits and bakery pastries\u0026rdquo;, which includes articles 760, 760 bis, 762 and 766, they are considered beaten products. A muffin is a classic oil-in water emulsion consisting of a mixture of eggs, water, fat, and sugar, in which the flour particles are dispersed [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This food offers an ideal base for incorporating nutritional and bioactive compounds. However, wheat has limitations, such as poor protein quality due to lysine deficiency. By partially replacing wheat with a blend of beans and quinoa, these nutritional shortcomings can be improved. This is a positive outcome, as non-conventional flours from the food industry, such as apple peel, grape pomace, and pea flours, have recently been shown to enhance the nutritional content of baked goods.A serving size of three muffins (approximately 114 g) provides 22% and 16% of the recommended daily intake (RDI) for protein and fibre, respectively, for an adult with an average body weight of 70 kg and moderate physical activity, based on daily recommendations of 0.8 g protein/kg and 25 g fibre. Therefore, the incorporation of white bean and quinoa flour in muffin can be a good alternative to increase the nutritional value of the product developed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Determination of total phenolic compounds and antioxidant activity\u003c/h2\u003e \u003cp\u003eThe results for total phenolic content and antioxidant activity are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Total phenolic content was higher in the formulated muffins (113.44 mg GAE/100 g dry matter) compared to the control (33.46 mg GAE/100 g dry matter), while antioxidant activity showed no significant difference. Many studies have documented an increase in the concentration of total phenolic compounds probably due to the inclusion of quinoa [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Quinoa contains a variety of polyphenols, including flavonoids like phenolic acids, kaempferol derivatives, rutin, quercetin, and myricetin [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The concentrations of these compounds in our study are comparable to those previously reported. While the most prominent phenolic compounds in beans are anthocyanins and tannins, whose incidence can influence the colour, aroma and nutritional quality of this legume [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Regarding phenolic content levels, our findings indicate that the enhanced muffin exhibits a four-fold increase in Total Phenolic Content (TPC) compared to the control, consistent with the results reported by Barros et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] in muffins with partial substitution of wheat flour by different classes of bean flour.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal phenolic contents and antioxidant activity of muffins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTPC (mg GAE/100g sample)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAntioxidant activity\u003c/p\u003e \u003cp\u003e% scavenging DPPH radical *\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl Muffins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWF/WBF/QF muffins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThe data correspond to average values of three analyzes along with the standard deviation of each type of muffin prepared. WBF\u0026thinsp;=\u0026thinsp;White bean flour; HH\u0026thinsp;=\u0026thinsp;Wheat flour; QF\u0026thinsp;=\u0026thinsp;Quinoa flour. GAE: gallic acid equivalent. *The percentage values of discoloration of the DPPH radical were corrected considering quercetin as standard with a value of 100% antioxidant capacity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePolyphenols are known to be sensitive to temperatures above 65\u0026deg;C, suggesting that the muffin-baking process may reduce the availability of free flavonoids. As these compounds primarily contribute to the sample\u0026rsquo;s DPPH free radical scavenging capacity, this may explain the minimal observed DPPH radical discoloration (2%). In another study, Pawłowska et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] reported analogous antioxidant results when replacing wheat flour with cocoa in muffins; however, the antioxidant activity of carob was shown to be significantly higher.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Determination of physical parameters, texture and colour in muffins elaborated\u003c/h2\u003e \u003cp\u003eThe results showed significant differences in the height of the muffin, being lower when quinoa and bean flour were added. The weights of both muffins were similar, with no significant differences in baking loss (4.5 g) and a baking yield of around 96% was obtained (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The evaluation of the textural parameters of the muffins elaborated with bean and quinoa mix flour showed significant differences in hardness and chewiness compared to the control muffin, obtaining softer and smoother crumbs in the control muffin. Lower cohesiveness is associated with increased crumbliness due to greater hardness. Several studies in the scientific literature report that the addition of fibre-rich by-products\u0026mdash;such as apple peel, grape seed, or whole oat wheat\u0026mdash;to sweet bakery products increases hardness [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. According to Goswami et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] chewiness reflects the effort required to chew food and form a bolus before swallowing. In this study, the enhanced muffins required more chewing force compared to the control, a finding consistent with Kaur et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] who observed similar results in muffins incorporating apple peel powder.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical properties and texture of the prepared muffins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Muffins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWF/WBF/QF muffins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaking loss (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaking yield (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHardness (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohesiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGumminess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChewiness (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe data correspond to average values ​​of six analyzes along with the standard deviation from each type of muffin. WBF\u0026thinsp;=\u0026thinsp;White bean flour; WF\u0026thinsp;=\u0026thinsp;Wheat flour; QF\u0026thinsp;=\u0026thinsp;Quinoa flour. Significantly different p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Tukey\u0026rsquo;s test).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is shown the colour measured in the muffins made. The results showed significant differences in the four parameters evaluated. The total colour difference (ΔE) of sample pairs leads to foresee whether or not there is a difference in the colour of the muffins that can be perceived by the human eye according to specific thresholds i.e., ΔE\u0026thinsp;\u0026lt;\u0026thinsp;1 normally invisible difference; 1\u0026thinsp;\u0026lt;\u0026thinsp;ΔE\u0026thinsp;\u0026lt;\u0026thinsp;2 very small difference, only obvious to a trained eye; 2\u0026thinsp;\u0026lt;\u0026thinsp;ΔE\u0026thinsp;\u0026lt;\u0026thinsp;3.5 medium difference, also obvious to an untrained eye; 3.5\u0026thinsp;\u0026lt;\u0026thinsp;ΔE\u0026thinsp;\u0026lt;\u0026thinsp;5 an obvious difference; ΔE\u0026thinsp;\u0026gt;\u0026thinsp;6 a very obvious difference [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The addition of beans and quinoa mix flours produced a small difference in the crumbs but an obvious difference in upper and lower crusts with respect to the control muffin. At same other authors the L* values in the crumbs were higher in muffin made with a blend of non-conventional flours [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eColour of the crusts and crumbs of prepared muffins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eUpper\u0026nbsp; \u0026nbsp; crust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eCrumb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eLower crust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eb*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eΔE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eL*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ea*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eb*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eΔE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl muffins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6 \u0026plusmn; 0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWF/WBF/QF muffins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0 \u0026plusmn; 0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eThe data correspond to average values ​​of six analyses along with the standard deviation from each type of muffin elaborated. WBF\u0026thinsp;=\u0026thinsp;White bean flour; WF\u0026thinsp;=\u0026thinsp;Wheat flour; QF\u0026thinsp;=\u0026thinsp;Quinoa flour. Significantly different \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Tukey\u0026rsquo;s test).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Acceptability global and sensorial analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is shown in the radar plot form the mean values for each sensory attribute and the overall acceptability on the hedonic scale for the muffins tested. All attributes, except for flavour and aroma, received ratings of 6 or higher. Flavour and aroma received moderate scores of 5.3 and 5.7, respectively, which may reflect the limited familiarity of consumers with legumes and Andean grains, such as quinoa, in their usual diets. Despite saponin being removed to reduce the inherent bitterness of quinoa, participants detected distinctive flavour notes and an aftertaste from the grain. Although differences were observed in the perception of aroma, flavour, and texture descriptors in the enhanced muffin, these did not affect overall consumer acceptance, as reflected by an acceptability index of 71%. Further formulation adjustments could enhance these sensory attributes, improving consumer appeal and facilitating the broader integration of quinoa and bean ingredients into mainstream food markets. In terms of hardness, significant differences were observed in both instrumental measurements and sensory attributes among the muffins tested. Textural properties of food are a group of physical characteristics determined by the structural elements of its components and their interactions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. More broadly, food texture defines the eating experience and influences consumer preferences for food products.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCurrently, there is no available literature on muffins formulated with a blend of white bean and quinoa flours. However, the results of this study can be compared to similar muffins with enhanced nutritional and sensorial properties reported by Pawłowska et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and Barros et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. With the development of these enhanced muffins, it was intended to achieve the revaluation of the bean and quinoa varieties, both underutilized regional products. In addition, their nutritional importance and the agro-industrial potential that they present according to their genetic material are highlighted.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eThe muffins produced from low-cost, regionally significant crops exhibited good quality. The blend of bean and quinoa flour serves as a nutritious and functional addition for partial replacement of wheat flour. This formulation offers a high-quality source of protein and fibre with low lipid content, making it potentially beneficial for vulnerable groups, such as schoolchildren to help prevent chronic non-communicable and deficiency diseases. The enhanced muffins also showed a significant increase in total phenolic content. Global acceptability and sensory analysis of the formulated muffin demonstrated a positive consumer response, achieving an overall score of 6.4 on a 9-point hedonic scale and a 71% acceptability index. However, future refinements should prioritise enhancements in flavour and aroma to further increase overall consumer appeal and acceptance. Additionally, it can be concluded that this formulation represents a valuable food alternative that promotes the cultivation of ancestral crops while supporting the regional economy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCOMPLIANCE WITH ETHICAL STANDARDS\u003c/h2\u003e \u003cp\u003eHuman and Animal Rights This article does not contain any studies with human or animal subjects.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, M.N.B., A.M.R., A.C.G.M. and M.L.T; methodology, M.N.B., M.E.A., LC., E.C.M. and. N.N.F.; Software, M.N.B.; validation, M.N.B., A.M.R., A.C.G.M. and M.L.T.; investigation, M.N.B.; resources, M.N.B., A.M.R., A.C.G.M. and M.L.T.; Writing\u0026mdash;original draft preparation, M.N.B.; writing\u0026mdash;review and editing, M.N.B., A.C.G.M., N.N.F. and M.L.T.; visualization, M.N.B. and M.L.T.; supervision, M.N.B., A.M.R., A.C.G.M. and M.L.T.; funding acquisition, A.M.R., A.C.G.M. and M.L.T. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors wish to express their gratitude to the staff of the Centro Interdisciplinario de Investigaciones en Tecnolog\u0026iacute;as y Desarrollo Social para el NOA (CIITED) for their invaluable assistance with the texture and colour analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBassett MN, Romaguera D, Gim\u0026eacute;nez M, Lobo MO, Samman NC (2014) Prevalencia y determinantes de la doble carga de malnutrici\u0026oacute;n en hogares en La Puna y Quebrada de Humahuaca, Jujuy, Argentina. Nutrici\u0026oacute;n hospitalaria 29 (2):322-330 https://www.aulamedica.es/nh/pdf/7075.pdf\u003c/li\u003e\n\u003cli\u003eENNyS2 (2019) Segunda Encuesta Nacional de Nutrici\u0026oacute;n y Salud. (2019). Resumen Ejecutivo. Secretar\u0026iacute;a de Gobierno de Salud. Ministerio de Salud y Desarrollo Social. Presidencia de la Naci\u0026oacute;n. https://cesni-biblioteca.org/2-encuesta-nacional-de-nutricion-y-salud-ennys-2-resumen-ejecutivo/ Accessed 2 May 2024\u003c/li\u003e\n\u003cli\u003eSomnuk S, Komindr S, Monkhai S, Poolsawat T, Nakphaichit M, Wanikorn B (2023) Metabolic and inflammatory profiles, gut microbiota and lifestyle factors in overweight and normal weight young thai adults. PLoS One 18 (7):e0288286 https://doi.org/10.1371/journal.pone.0288286.\u003c/li\u003e\n\u003cli\u003eAlmonacid-Fierro A, Gonz\u0026aacute;lez-Almonacid J (2022) Obesidad Infantil: repercusiones post-pandemia y el factor escuela. Andes pediatrica 93:440-441 https://doi.org/10.34172/hpp.2022.29.\u003c/li\u003e\n\u003cli\u003eMachado \u0026Iacute;E, Paraj\u0026aacute;ra MDC, Guedes LFF, Meireles AL, Menezes MC, Felisbino-Mendes MS et al. (2022) Burden of non-communicable diseases attributable to dietary risks in Brazil, 1990-2019: an analysis of the Global Burden of Disease Study 2019. Rev Soc Bras Med Trop 55 (suppl 1):e0282. https://doi.org/10.1590/0037-8682-0282-2021\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Villanova B, E.J. GH, Verardo V (2020) Compuestos Bioactivos de Los Granos de Cereales En Papel De Los Cereales De Grano Entero En La Salud. Granada, Espa\u0026ntilde;a\u003c/li\u003e\n\u003cli\u003eMir NA, Riar CS, Singh S (2018) Nutritional constituents of pseudo cereals and their potential use in food systems: A review. Trends in Food Science \u0026amp; Technology 75:170-180 https://doi.org/10.1016/j.tifs.2018.03.016\u003c/li\u003e\n\u003cli\u003eBassett MN, Gim\u0026eacute;nez MA, Romaguera D, Samm\u0026aacute;n N (2013) Estado nutricional e ingesta alimentaria de poblaciones de regiones de altura del Noroeste Argentino. Archivos latinoamericanos de nutrici\u0026oacute;n 63 (2):114-125\u003c/li\u003e\n\u003cli\u003eBassett MN, Espejo LM, Rossi AM, Samm\u0026aacute;n NC (2019) Evaluaci\u0026oacute;n de la calidad proteica de harinas de legumbres con software MixProtLUNA. Aplicaci\u0026oacute;n en panes reducidos en sodio. INVESTIGACIONES EN LAS FACULTADES DE INGENIER\u0026Iacute;A DEL NOA (5):171-174\u003c/li\u003e\n\u003cli\u003eTemple NJ (2022) A rational definition for functional foods: A perspective. Frontiers in nutrition 9:957516 https://doi.org/10.3389/fnut.2022.957516\u003c/li\u003e\n\u003cli\u003eMihaylova D, Popova A, Goranova Z, Doykina P, Goranov B (2023) Characterization of Muffins Reformulated with Chia and Lyophilized Peach Powder in Terms of Some Technological and Sensory Aspects \u0026sect;. Food Technology and Biotechnology 61 (3):273-282 https://doi.org/10.17113/ftb.61.03.23.7843\u003c/li\u003e\n\u003cli\u003eBassett MNE, L.M.; Samm\u0026aacute;n, N.C. (2015) Estudio de la calidad de diferentes mezclas de harinas proteicas aplicando el software MixProtLUNA. . In: In Proceedings of the V Congreso Internacional de Ciencia y Tecnolog\u0026iacute;a de los Alimentos.. Investigaciones y avances en nutrici\u0026oacute;n y salud: trabajos completos del V CICYTAC 2014. C\u0026oacute;rdoba, Argentina, pp 3\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eLeser S (2013) The 2013 FAO report on dietary protein quality evaluation in human nutrition: Recommendations and implications. Nutrition Bulletin 38 (4):421-428 https://doi.org/10.1111/nbu.12063 \u003c/li\u003e\n\u003cli\u003eTawaha K, Alali FQ, Gharaibeh M, Mohammad M, El-Elimat T (2007) Antioxidant activity and total phenolic content of selected Jordanian plant species. Food chemistry 104 (4):1372-1378 https://doi.org/10.1016/j.foodchem.2007.01.064\u003c/li\u003e\n\u003cli\u003eTapia A, Rodriguez J, Theoduloz C, Lopez S, Feresin GE, Schmeda-Hirschmann G (2004) Free radical scavengers and antioxidants from Baccharis grisebachii. Journal of Ethnopharmacology 95 (2-3):155-161 https://doi.org/10.1016/j.jep.2004.06.035\u003c/li\u003e\n\u003cli\u003eKumazawa S, Hamasaka T, Nakayama T (2004) Antioxidant activity of propolis of various geographic origins. Food chemistry 84 (3):329-339 https://doi.org/10.1016/S0308-8146(03)00216-4\u003c/li\u003e\n\u003cli\u003eSchmeda-Hirschmann G, Rodriguez J, Theoduloz C, Astudillo S, Feresin G, Tapia A (2003) Free-radical scavengers and antioxidants from Peumus boldus Mol.(\u0026quot; Boldo\u0026quot;). Free radical research 37 (4):447-452 https://doi.org/10.1080/1071576031000090000\u003c/li\u003e\n\u003cli\u003eYalcin E, Ozdal T, Gok I (2022) Investigation of textural, functional, and sensory properties of muffins prepared by adding grape seeds to various flours. Journal of Food Processing and Preservation 46 (5):e15316 https://doi.org/10.1111/jfpp.15316\u003c/li\u003e\n\u003cli\u003eGomes DdS, Rosa LS, Cordoba LdP, Fiorda-Mello F, Spier MR, Waszczynskyj N (2021) Development of muffins with green pea flour and their physical and sensory evaluation and essential amino acid content. Ci\u0026ecirc;ncia Rural 52 (7):e20200693 https://doi.org/10.1590/0103-8478cr20200693\u003c/li\u003e\n\u003cli\u003eKaur M, Kaur M, Kaur H (2022) Apple peel as a source of dietary fiber and antioxidants: Effect on batter rheology and nutritional composition, textural and sensory quality attributes of muffins. Journal of Food Measurement and Characterization 16 (3):2411-2421 https://link.springer.com/article/10.1007/s11694-022-01329-x\u003c/li\u003e\n\u003cli\u003eGrasso S, Pintado T, P\u0026eacute;rez-Jim\u0026eacute;nez J, Ruiz-Capillas C, Herrero AM (2021) Characterisation of muffins with upcycled sunflower flour. Foods 10 (2):426 https://doi.org/10.3390/foods10020426\u003c/li\u003e\n\u003cli\u003eAbreu A, Milke-Garc\u0026iacute;a M, Arg\u0026uuml;ello-Ar\u0026eacute;valo G, Calder\u0026oacute;n-de la Barca A, Carmona-S\u0026aacute;nchez R, Consuelo-S\u0026aacute;nchez A et al. (2021) Fibra dietaria y microbiota, revisi\u0026oacute;n narrativa de un grupo de expertos de la Asociaci\u0026oacute;n Mexicana de Gastroenterolog\u0026iacute;a. Revista de Gastroenterolog\u0026iacute;a de M\u0026eacute;xico 86 (3):287-304 https://doi.org/10.1016/j.rgmx.2021.02.004\u003c/li\u003e\n\u003cli\u003eBarber TM, Kabisch S, Pfeiffer AF, Weickert MO (2020) The health benefits of dietary fibre. Nutrients 12 (10):3209 https://doi.org/10.1093/jn/nxx008\u003c/li\u003e\n\u003cli\u003eWeickert MO, Pfeiffer AF (2018) Impact of dietary fiber consumption on insulin resistance and the prevention of type 2 diabetes. The Journal of nutrition 148 (1):7-12 https://doi.org/10.1093/jn/nxx008\u003c/li\u003e\n\u003cli\u003eCAA C\u0026oacute;digo Alimentario Argentino. Cap\u0026iacute;tulo IX. Art\u0026iacute;culos: 643 al 766 - Alimentos Farin\u0026aacute;ceos - Cereales, Harinas y Derivados. https://www.argentina.gob.ar/sites/default/files/anmat_capitulo_ix_harinas.pdf . Accessed 20 September 2024\u003c/li\u003e\n\u003cli\u003eBhathal SK, Kaur N, Gill J (2017) Effect of processing on the nutritional composition of quinoa (Chenopodium quinoa Willd). Agric Res J 54 (1):90-93 https://doi.org/10.5958/2395-146X.2017.00015.1\u003c/li\u003e\n\u003cli\u003eHussain MI, Farooq M, Syed QA, Ishaq A, Al-Ghamdi AA, Hatamleh AA (2021) Botany, Nutritional Value, Phytochemical Composition and Biological Activities of Quinoa. Plants (Basel) 10 (11). https://doi.org/10.3390/plants10112258 \u003c/li\u003e\n\u003cli\u003eBarrueto-Gonzalez NB (2008) Biodisponibilidade de minerais das fontes leguminosas. Revista Simbiologias 1 (1) https://doi.org/10.32905/1983-3253/2008.01.01p174\u003c/li\u003e\n\u003cli\u003eBarros LFTd, Escobar TD, Ribeiro PFdA, Kaminski TA (2018) Muffins adicionados de farinha de feij\u0026atilde;o de diferentes classes. Brazilian Journal of Food Technology 21:e2017081 https://doi.org/10.1590/1981-6723.08117\u003c/li\u003e\n\u003cli\u003ePawłowska K, Kuligowski M, Jasińska-Kuligowska I, Kidoń M, Siger A, Rudzińska M et al. (2018) Effect of replacing cocoa powder by carob powder in the muffins on sensory and physicochemical properties. Plant Foods for Human Nutrition 73:196-202 https://doi.org/10.1007/s11130-018-0675-0\u003c/li\u003e\n\u003cli\u003eGoswami D, Gupta R, Mridula D, Sharma M, Tyagi S (2015) Barnyard millet based muffins: Physical, textural and sensory properties. LWT-Food Science and Technology 64 (1):374-380 https://doi.org/10.1016/j.lwt.2015.05.060\u003c/li\u003e\n\u003cli\u003eAshe T (2014) Color management \u0026amp; quality output: Working with color from camera to display to print. Routledge. https://doi.org/10.4324/9780240821368\u003c/li\u003e\n\u003cli\u003eYang X, Li A, Li X, Sun L, Guo Y (2020) An overview of classifications, properties of food polysaccharides and their links to applications in improving food textures. Trends in Food Science \u0026amp; Technology 102:1-15 https://doi.org/10.1016/j.tifs.2020.05.020\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-foods-for-human-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Foods for Human Nutrition](https://www.springer.com/journal/11130)","snPcode":"11130","submissionUrl":"https://submission.nature.com/new-submission/11130/3","title":"Plant Foods for Human Nutrition","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Muffin, Quality, Protein, White bean, Quinoa Flours, Functional food","lastPublishedDoi":"10.21203/rs.3.rs-5364412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5364412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of the present study was to elaborate muffins optimizing nutritional quality using protein complementation, with regionally produced underutilized ingredients. The theoretical protein quality of various mixtures was assessed using the \"digestible essential amino acid score\" (DIAAS) method, using the FAO standard requirements as a reference protein. A muffin was subsequently designed, formulated, and produced using the optimal ratio of wheat, bean, and quinoa flour mixture, and compared to a control made with 100% wheat flour. The proteins, fats, dietary fibre, ashes, and moisture were determined using AOAC methods. Height, texture, and colour were evaluated using a Vernier calliper, a TA-XT 2 plus Texturometer, and a Colour Quest XE spectrophotometer. Antioxidant activity was determined using the DPPH radical decolouration method, and total phenolic compounds were quantified using the Folin-Ciocalteu method. The sensory characteristics and global acceptability were also performed. Results showed that the moisture, protein, fat, fibre content, hardness, colour and the total phenol content (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05) of muffins are significantly different from each other. The partial replacement of wheat flour with quinoa and bean flours caused a decrease in muffin height while increasing hardness, total phenol content, protein and dietary fibre content. The elaborated muffin showed good sensory acceptance, scoring 6.4 on a 9-point hedonic scale, and achieved an acceptability index of 71%. Formulating this muffin with a good nutritional balance offers an effective way to incorporate beans and quinoa into regular diets, helping to prevent chronic non-communicable and deficiency diseases in vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Nutritional Optimization of Muffins using Wheat, Bean and Quinoa Flour: A Strategy for Enhancing Protein Quality and Healthy Benefits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-14 12:44:42","doi":"10.21203/rs.3.rs-5364412/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-17T20:14:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-05T15:31:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-03T21:52:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Foods for Human Nutrition","date":"2024-10-31T03:06:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-foods-for-human-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Foods for Human Nutrition](https://www.springer.com/journal/11130)","snPcode":"11130","submissionUrl":"https://submission.nature.com/new-submission/11130/3","title":"Plant Foods for Human Nutrition","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"82c33461-f167-42e5-abef-cac57a24be6f","owner":[],"postedDate":"November 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-17T16:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-14 12:44:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5364412","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5364412","identity":"rs-5364412","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}