Multifactorial characterization of quinoa varieties based on chemical, functional, and morphological parameters

Multifactorial characterization of quinoa varieties based on chemical, functional, and morphological parameters | 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 Multifactorial characterization of quinoa varieties based on chemical, functional, and morphological parameters Francisco Rios, Manuel Oscar Lobo, Norma Cristina Sammán This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7377157/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract This study aims to characterize the chemical, functional, color, and morphological properties of different quinoa ( Chenopodium quinoa Willd) varieties from the Andean region of northern Argentina. The nutritional composition, including dietary fiber, was analyzed using AOAC methods; antioxidant activities were assessed with DPPH, ABTS, and FRAP assays. Functional properties such as water absorption index (WAI), water solubility index (WSI), and swelling power (SP) were also determined. Additionally, digital image analysis via laser confocal microscopy was employed to evaluate morphometric parameters like area, perimeter, Feret’s diameter, circularity, roundness, aspect ratio, and solidity. The results showed significant variability among quinoa samples linked to the variety. The color component a* exhibited the greatest variability (71.54%), followed by WSI (41.58%) and insoluble fiber content (23.50%), indicating heterogeneity in functional and compositional properties. Total polyphenol content ranged from 2.13 to 4.02 mg/g, while total flavonoids ranged from 0.32 to 0.74 mg/g sample. Antioxidant activity varied significantly among samples, with mean IC50 values for DPPH at 13.8 mg/mL, and for ABTS and FRAP at 2.11 and 5.3 mg Trolox equivalents/g sample, respectively. The first two factors (F1 and F2) of principal component analysis (PCA) explained 58% of the total data variability. Variables most influential for differentiation included antioxidants, color, and nutritional parameters. PCA and cluster analyses grouped the samples into three clusters, one of which corresponded to the experimental quinoa varieties. Therefore, these findings suggest that the differentiation of quinoa varieties is influenced by multiple factors. quinoa varieties nutritional composition functional properties morphological properties principal component analysis Figures Figure 1 Figure 2 Introduction Quinoa refers to clean and well-preserved seeds of the genus Chenopodium quinoa Willd [ 1 , 2 ]. It is considered a pseudocereal or even a pseudoseed, as it does not belong to the grass family, although it is consumed and used like cereals. Quinoa is cultivated in the Andean region of Argentina, Chile, Peru, Bolivia, and Ecuador. Since quinoa has high resistance to climatic conditions and soil characteristics [ 3 ] and also good nutritional and functional properties, many countries have introduced it, expanding its use [ 4 , 5 , 6 ]. Quinoa nutritional profile includes high biological value proteins (with a good balance of essential amino acids), starch, dietary fiber, unsaturated lipids, absence of gluten, and notable contents of mineral (Ca, Fe, K, P, Mg, Mn), vitamins (B, C, and E), and bioactive compounds such as isoflavones and polyphenols with antioxidant properties [ 7 , 8 , 9 ]. Quinoa is gluten free, so it is appropriate for celiac patients or those intolerant to gluten [ 10 ]. The physical properties of quinoa, including morphology and size distribution, are essential for the operation of equipment and facilities in the processes of harvesting, cleaning, transportation, classification, separation, drying, storage, and transformation. These properties generally vary depending on the cultivar, and growing region [ 11 ]. The use of modern techniques has facilitated the determination of physical parameters in grains through digital image processing, allowing for the provision of dimensional measurements, morphometric and colorimetric characteristics, among others [ 12 , 13 , 14 ]. Digital image processing also allows obtaining descriptors (such as surface area, perimeter, circularity, diameter, and others) without destroying the sample. These descriptors can possibly be correlated with the varieties and their physical attributes. Furthermore, the geometric dimensions, morphological characteristics, fractal dimensions, and color characteristics of quinoa grains were used to identify their geographical origin. Also, along with morphological and color parameters were used to analyze different grain varieties [ 15 , 16 ]. Knowledge of the nutritional composition of different varieties of quinoa, as well as their physicochemical and functional properties, is important for establishing their potential uses and for understanding their behavior in technological processes such as cooking, baking, gelatinization, and gel formation [ 17 , 18 , 19 ]. Quinoa has diverse functional or technological properties due to its biopolymers (proteins, starch), which vary between varieties (or genotypes). These include: Flour solubility, Water holding capacity, Water imbibing capacity, Solubility, Water absorption power, and Water binding capacity [ 17 , 20 ]. However, the diversity of properties among different varieties, origins, and growing conditions can significantly affect their composition, technological functionality, and physical characteristics [ 11 , 21 ]. This heterogeneity, which includes differences in protein proportion and structure, starch composition and degree of gelatinization, dietary fiber content and type, as well as the presence of minor compounds, leads to variations in processing performance, such as milling, flour production, extrusion, baking, or protein isolation. These differences directly influence product quality attributes, including bread texture, water retention or absorption capacity, solubility, and gel stability [ 11 , 18 , 20 , 22 ]. Therefore, the complete characterization of different quinoa seeds is essential to establish selection criteria for the most suitable raw material, according to the desired final product and its intended food functionality [ 5 ]. This work aims to characterize the chemical, functional, and morphological properties of various quinoa varieties to identify the parameters that determine their differentiation and potential application in the development of functional foods. Material and Methods Samples A total of 13 quinoa ( Chenopodium quinoa Willd) varieties were subjected to analysis. Samples included three commercially available seeds: Real white (Qc1), Black (Qc2), and Red (Qc3), and ten experimental varieties locally grown at INTA (National Institute of Agricultural Technology) in Miraflores, Jujuy, Argentina. All samples were subjected to saponin removal by hand washing. Detailed Methodology for Quality and composition analysis, Functional properties, Morphological characteristics, Color determination, and Statistical analysis are included in Supplementary Material. Results and Discussion Proximate composition Table 1 shows the proximate composition of quinoa seeds, including soluble and insoluble fiber. The analyzed quinoas showed a protein content ranging from 9.81 to 16.35 g/100g on a dry basis. All samples, except Qe1, exceeded the minimum established in national and international regulations for the marketing of quinoa [ 1 , 2 ]. The coefficient of variation for protein (17.89%) indicates considerable variability, possibly associated to varietal differences. The moisture content of all samples did not exceed the maximum limit (13%). The ANOVA determined that ash and moisture content were the parameters with the greatest homogeneity among the samples (Table 1 ). Moisture content only showed significant differences (p < 0.05) between Qc1 and Qe9. Similarly, ash content only showed significant differences (p < 0.05) between samples Qc1 = Qe10 compared to Qe5. The coefficient of variation confirms the low variability of the moisture (< 4%) and ash (< 9%) parameters. Table 1 Proximate composition of quinoa samples (* g/100 g db) Samples Moisture Protein* Ash* Fat* IDF* SDF* Qc1 11.84 ± 0.03 b 15.14 ± 1.06 cd 3.20 ± 0.03 a 2.42 ± 0.10 a 9.00 ± 0.80 cde 3.80 ± 0.26 abc Qc2 11.34 ± 0.01 ab 16.35 ± 0.77 d 3.70 ± 0.16 ab 4.20 ± 0.18b c 14.21 ± 0.49 h 3.79 ± 0.25 abc Qc3 11.24 ± 0.01 ab 12.29 ± 0.95 abcd 3.31 ± 0.20 ab 4.14 ± 0.31 b 12.91 ± 0.13 g 3.45 ± 0.16 ab Qe1 10.49 ± 0.18 ab 9.81 ± 0.65 ab 3.35 ± 0.24 ab 5.35 ± 0.11 cd 7.41 ± 0.35 ab 3.84 ± 0.28 abc Qe2 10.91 ± 0.55 ab 13.33 ± 1.09 abcd 3.72 ± 0.27 ab 4.07 ± 0.25 bc 11.91 ± 0.37 g 3.41 ± 0.24 a Qe3 11.26 ± 0.67 ab 10.60 ± 0.92 ab 3.59 ± 0.15 ab 6.51 ± 0.51 de 9.53 ± 0.80 def 4.07 ± 0.34 c Qe4 11.24 ± 0.54 ab 10.41 ± 0.92 ab 3.46 ± 0.23 ab 6.80 ± 0.07 e 8.08 ± 0.52 abc 3.59 ± 0.05 abc Qe5 10.62 ± 0.07 ab 11.54 ± 0.25 abc 3.86 ± 0.30 b 5.16 ± 0.14 bcd 9.78 ± 0.56 ef 4.02 ± 0.36 bc Qe6 10.77 ± 0.34 ab 14.80 ± 1.17 bcd 3.32 ± 0.26 ab 5.50 ± 0.28 de 7.67 ± 0.07 ab 3.97 ± 0.17 abc Qe7 11.00 ± 0.39 ab 10.28 ± 0.86 ab 4.12 ± 0.12 ab 6.09 ± 0.19 de 7.56 ± 0.51 ab 3.82 ± 0.35 abc Qe8 10.79 ± 0.41 ab 10.70 ± 0.68 abc 3.67 ± 0.16 ab 6.13 ± 0.44 de 8.48 ± 0.47 bcd 4.01 ± 0.12 bc Qe9 10.24 ± 0.42 a 12.45 ± 0.48 abcd 3.66 ± 0.08 ab 5.97 ± 0.29 de 10.28 ± 0.58 f 5.00 ± 0.38 d Qe10 10.65 ± 0.34 ab 10.02 ± 0.89 a 3.01 ± 0.22 a 5.11 ± 0.36 bcd 7.21 ± 0.28 a 3.74 ± 0.24 abc M (CV) 10.95 (3.90) 12.13 (17.89) 3.54 (8.43) 5.19 (23.49) 9.54 (23.51) 3.88 (10.13) AFC 13 (Maximun) 10 (Minimum) 3.5 (Maximun) Mean values ± Standard Deviation. IDF: Insoluble Dietary Fiber; SDF: Soluble Dietary Fiber. db: dry basis Different superscript letters within rows indicate significant differences (p < 0.05). ACF: Limit established by the Argentine Food Code. M (CV): Mean (Coefficient of Variation) Most experimental samples had a lipid content greater than 5%, with this parameter was one of the most variable, reflecting significant differences (p < 0.05) in the quinoa samples associated with their varieties. In all analyzed samples, insoluble fiber values were higher than soluble fiber values. Likewise, the variability in insoluble fiber content (23.51%) is greater than that of soluble fiber (10.13%). Similarly, Steffolani et al. [ 22 ] showed greater dispersion in soluble dietary fiber fraction than in the insoluble one, possibly due to the variety factor, or differences in the degree of maturity or processing. The results confirm that quinoa has high nutritional value, with significant variability among varieties, consistent with findings reported by other authors [ 11 , 21 ]. Moreover, the combination of quinoa's high protein, lipid, and dietary fiber content demonstrates its potential as a functional ingredient for food development. Functional Properties The hydration properties of the quinoa samples are shown in Table 2 . Both WAI and SP agree that sample Qe2 presents the minimum of the variation range, 4.53 and 4.95 respectively. As both parameters have a low coefficient of variability (< 10%), there are significant differences (p < 0.05) between the samples at the extremes of the variation range. The relationship of WAI and SP with gel formation is consistent with previous studies, indicating that starch gelatinization and protein denaturation contribute to water absorption and swelling capacity [ 20 , 23 ]. Lower WAI and SP values were determined in quinoa Real and Jericó L51 varieties (Colombia) at 30°C [ 20 , 23 ]. The solubility index (WSI) ranged from 3.01 to 9.81, with significant differences (p 40%) among the hydration properties evaluated. The highest WSI values correspond to Qe5 and Qe9 (9.04 and 9.81, respectively) and would be related to a higher content of soluble compounds, such as low molecular weight peptides or soluble polysaccharides [ 20 , 23 , 24 ]. Phenolic compounds and Antioxidant activity The polyphenol and flavonoid contents of quinoa grain extracts were measured, and their antioxidant and anti-radical activities were assessed using FRAP, ABTS, and DPPH assays (Supplementary Information). The total polyphenol content showed high variability in quinoa, with an average of 3.01 ± 0.52 mg/ g. Significant differences were observed between varieties (p < 0.05). The values obtained are comparable to those reported by Tang et al. [ 25 ], who also found differences in the content of phenolic compounds between quinoa varieties. The total flavonoid content in quinoa samples ranged from 0.32 to 0.74 mg/g sample, indicating considerable variability among samples. This variability is consistent with the findings reported by [ 25 , 26 ]. The pigmented samples exhibited higher polyphenol content and antioxidant activity than the lighter-colored varieties. Sample Qc2 showed the highest polyphenol content (4.02 mg/g) and greater anti-radical activity in the in vitro assays. Conversely, sample Qe10 had the lowest polyphenol levels and activity against DPPH and ABTS radicals. The results align with studies reporting greater antioxidant capacity in quinoa varieties with intense colors, such as red-violet or yellow, mainly due to the presence of betacyanins, betaxanthins, and other phenolic pigments [ 25 , 26 ]. Although samples with higher polyphenol contents generally correlated with antioxidant activity in the DPPH, ABTS, and FRAP assays, discrepancies were noted, particularly regarding flavonoid content, suggesting the involvement of other metabolites such as betalains, as indicated by [ 24 ]. Therefore, the quinoa varieties analyzed possess antioxidant potential that could make them promising ingredients in developing foods with enhanced antioxidant properties. Morphological Analysis Table 2 shows the morphological and shape descriptors for quinoa seeds, obtained with a laser confocal microscope. The seed area showed the greatest variability (13.72%) among shape descriptors. The ANOVA showed significant differences (p < 0.05) between varieties, ranging from 3.12 mm² (Qe5) to 5.21 mm² (Qc3). Similar results have been reported in studies on the morphological diversity of quinoa seeds [ 6 , 13 ]. In the case of perimeter, variations between 6.84 mm (Qe5) and 8.87 mm (Qc3) are consistent with the differences in area. The descriptors Feret Diameter (FD) and Minimum Feret Diameter (MFD) were highly correlated with area and perimeter, as expected from their underlying geometric relationships. These findings confirm that seed size is highly dependent on variety, in agreement with previous reports on morphological diversity in quinoa [ 6 , 13 , 26 ]. The shape descriptor parameters exhibited low variability. The aspect ratio (AR) and roundness (R) parameters have coefficients of variation less than 1.5% and solidity (S) < 0.37%. Despite the low variability of the shape parameters, significant differences (p < 0.05) were observed among the samples. There is a geometric association between AR and R, as samples with greater elongation are indicative of less roundness [ 16 ]. The solidity of all samples showed high and constant values (0.96), reflecting seeds with well-defined and compact shapes, as reported in quinoa quality studies for the industry [ 8 , 19 ]. Quinoa Qe5 was the only one that slightly exceeded this value (0.97), possibly due to lower edge roughness or a smoother surface, which increases the fit between the convex and actual areas. These results are consistent with those proposed by [ 15 ], who linked the seeds’ external texture with their geographical origin and post-harvest processes. Escribano et al. [ 26 ] have shown that the shape and size of quinoa seeds can be used as criteria to differentiate between varieties. Other authors agree that despite differences in variety, the morphological parameters of the grains remain within certain ranges of variation [ 14 , 15 ]. Color Parameters Figure 1 shows the color measurements of quinoa samples in the CIELab scale. The L*, a*, and b* values show significant differences (p < 0.05) between samples. Table 2 Functional and morphometric parameters of quinoa samples Samples WAI WSI SP A P C FD MFD AR R S Qc1 6.07 ± 0.08 cd 3.83 ± 0.28 ab 6.32 ± 0.07 efg 4.82 ± 0.48 cde 8.35 ± 0.45 cd 0.87 ± 0.02 e 2.67 ± 0.14 f 2.43 ± 0.14 cde 1.08 ± 0.03 bcde 0.93 ± 0.02 abc 0.963 ± 0.006 c Qc2 4.53 ± 0.11ª 8.64 ± 0.30 e 4.95 ± 0.11ª 4.89 ± 0.48 de 8.70 ± 0.56 de 0.81 ± 0.04 b 2.73 ± 0.15 f 2.44 ± 0.13 cde 1.06 ± 0.02 ab 0.95 ± 0.02 cd 0.954 ± 0.008ª Qc3 5.78 ± 0.23 cd 6.97 ± 0.12 d 6.21 ± 0.24 defg 5.21 ± 0.49 e 8.87 ± 0.59 de 0.83 ± 0.05 bc 2.76 ± 013 f 2.52 ± 0.13 e 1.06 ± 0.03 abce 0.94 ± 0.02 bcd 0.961 ± 0.007 abc Qe1 5.37 ± 0.23 abc 3.22 ± 0.12 ab 5.55 ± 0.25 bc 4.76 ± 0.47 cd 8.41 ± 0.44 cd 0.85 ± 0.03 cd 2.66 ± 0.13 de 2.40 ± 0.13 cd 1.09 ± 0.03 cde 0.92 ± 0.02 ab 0.957 ± 0.005 ab Qe2 5.98 ± 0.41 cd 8.89 ± 0.77 e 6.56 ± 0.50 fg 4.99 ± 0.48 de 8.56 ± 0.42 cd 0.85 ± 0.03 cde 2.69 ± 0.14 f 2.48 ± 0.13 de 1.05 ± 0.04 ab 0.95 ± 0.03 cd 0.960 ± 0.008 abc Qe3 5.86 ± 0.11 cd 4.21 ± 0.09 ab 6.12 ± 0.11 def 4.65 ± 0.45 bcd 8.22 ± 0.42 bcd 0cde.87 ± 0.02 cde 2.61 ± ± 0.13 cde 2.38 ± 0.14 bcde 1.06 ± 0.03 abcde 0.94 ± 0.03 abcd 0.961 ± 0.006 abc Qe4 5.76 ± 0.33 cd 4.95 ± 0.36 c 6.06 ± 0.37 cdef 4.41 ± 0.43 bc 8.01 ± 0.42 bc 0.86 ± 0.02 cde 2.54 ± 0.12 cd 2.32 ± 0.11 bc 1.07 ± 0.04 abce 0.94 ± 0.02 abcd 0.960 ± 0.006 abc Qe5 4.73 ± 0.30 ab 9.04 ± 0.70 e 5.20 ± 0.36 ab 3.12 ± 0.31ª 6.84 ± 0.59ª 0.77 ± 0.04ª 2.10 ± 0.19ª 1.94 ± 0.18 bc 1.06 ± 0.03 abcde 0.94 ± 0.04 abcd 0.967 ± 0.027 bc Qe6 5.72 ± 0.08 cd 4.66 ± 0.38 c 6.00 ± 0.10 cde 4.81 ± 0.48 cde 8.42 ± 0.43 cd 0.85 ± 0.03 cde 2.69 ± 0.14 f 2.39 ± 0.13ª 1.10 ± 0.02 d 0.91 ± 0.03ª 0.957 ± 0.009 abc Qe7 5.43 ± 0.09 bc 4.72 ± 0.16 c 5.70 ± 0.10 bcd 4.96 ± 0.49 de 8.47 ± 0.48 cde 0.87 ± 0.02 de 2.69 ± 0.14 f 2.44 ± 0.13 cd 1.08 ± 0.05 bcde 0.93 ± 0.03 abc 0.964 ± 0.006 bc Qe8 6.39 ± 0.29 cd 4.69 ± 0.01ª 6.39 ± 0.30 g 4.23 ± 0.40 b 7.81 ± 0.48 b 0.87 ± 0.03 e 2.48 ± 0.12 c 2.26 ± 0.11 cde 1.07 ± 0.04 bc 0.94 ± 0.02 ab 0.963 ± 0.007 c Qe9 5.66 ± 0.05 cd 9.81 ± 0.23 e 6.28 ± 0.07 efg 3.49 ± 0.35ª 7.08 ± 0.35ª 0.87 ± 0.03 e 2.27 ± 0.11 b 2.08 ± 0.11 b 1.05 ± 0.02ª 0.96 ± 0.02 d 0.959 ± 0.007 abc Qe10 5.63 ± 0.08 e 3.01 ± 0.19 c 5.81 ± 0.10 cde 5.11 ± 0.51 de 8.62 ± 0.47 de 0.86 ± 0.02 cde 2.76 ± 0.14 f 2.47 ± 0.15ª 1.10 ± 0.05 de 0.91 ± 0.04 bc 0.963 ± 0.005 bc M (CV) 5.61 (9.12) 5.90 (41.18) 5.93 (8.03) 4.57 (13.72) 8.18 (7.46) 0.85 (3.55) 2.59 (7.67) 2.35 (7.16) 1.07 (1.52) 0.93 (1.49) 0.96 (0.37) Different superscript letters in rows indicate significant difference (p < 0.05). WAI: Water Absorption Index, WSI: Water Solubility Index, SP: Swelling Power, A: Area (mm2), P: Perimeter (mm), Circularity, FD: Feret Diameter (mm), MFD: Minimum Feret Diameter (mm), AR: Aspect Ratio, R: Roundness, S: Solidity. M (CV): Mean (Coefficient of Variation) The lightness parameter (L*) showed the greatest range of variation, with values between 68.06 (Qc2) and 90.45 (Qe4), indicating a wide range of variation in light-colored quinoas. The a* parameter, associated with the red (positive) - green (negative) axis, shows the greatest variability between samples; it is even the most variable of all the properties analyzed. Sample Qc3 presents the highest a* component that corresponds to the red region. Different letters above symbols indicate significant differences (p < 0.05) The b* parameter, yellow (positive)-blue (negative) axis, also showed significant variability (p < 0.05) between samples, with average values of 14.05 ± 2.96, with Qe5 having the highest value in the yellow region, which differs significantly (p < 0.05) from the other samples. The presence of colors in the a* and b* (red-yellow) region could be due to the presence of natural pigments, such as phenolic compounds, anthocyanin, betalains, or other compounds [ 26 ]. The results are consistent with those reported by Tang et al. [ 26 ], who observed that quinoa varieties with high levels of total phenols and anthocyanins tend to have higher a* values. Together, the three color parameters analyzed allowed the differentiation of varieties. Furthermore, as a rapid and non-destructive technique, colorimetry offers an objective and reproducible criterion for varietal discrimination in quinoa. Also, Jamanca-Gonzales et al. [ 16 ] propose colorimetry as a tool for varietal characterization of Andean grains. Similarly, Pedrali et al. [ 11 ] demonstrated that differences between varieties have a greater impact on the color profile than geographical factors, reinforcing the importance of colorimetry as a varietal descriptor. Principal Components and Cluster Analysis Principal components analysis (PCA) was used as an exploratory method to examine the relationship between the studied variables of different quinoa varieties, Fig. 2 . PCA required five principal components to explain 85.3% of the total data variability, indicating a low correlation between the variables. Therefore, a high degree of independent variation is observed among the variables analyzed. Variables with high correlation (r > 0.9) were used as supplementary variables, so they do not influence the construction of the principal components of the PCA. The variables that contribute most to Factor1 (F) are the antioxidants (total polyphenols, ABTS, FRAP), color (a*, L), and nutritional (protein, insoluble dietary fiber) parameters. On the F1 positive axis, the samples with the highest protein content, phenolic compounds, and antioxidant capacity (ABTS/FRAP). Conversely, the F1 negative axis is characterized by samples with higher lightness (L*), which are associated with lower antioxidant activity. Therefore, the F2 factor mainly distinguishes samples based on their morphological parameters and soluble fiber content. A negative correlation was found between insoluble fiber and L* (r = − 0.782), indicating that samples with higher insoluble fiber content tend to be darker. Insoluble fiber content also shows a positive correlation with polyphenol content (r = 0.62). Other studies [ 16 , 26 ] have linked greater presence of phenolic compounds with darker seed color (lower L* values) and with a higher insoluble fiber content. In Fig. 2 b, the sample score displays clear dispersion in the F1-F2 plane, as revealed by principal component analysis, indicating significant differences among samples in terms of physicochemical, color, antioxidant activity, and morphological features. The dendrogram confirms the formation of three clusters, although some overlap occurs between experimental and commercial samples. Most experimental samples cluster near the center of the F1-F2 plane, mainly grouped by morphological parameters. On the positive side of the F1-F2 plane, commercial samples clearly separated from the experimental samples located on the negative of F2 axis. Other authors [ 11 , 21 ] have used both PCA and cluster analysis to group varieties based on physical and chemical characteristics. The variables used in the analysis allow for differentiation among the samples, with contributions from morphological, color and composition and antioxidant parameters. Conclusion The results confirm variation among the analyzed quinoa varieties in terms of nutritional composition, functional properties, and morphometric parameters. This observed diversity, mainly related to differences among varieties, allows for the differentiation of quinoa seeds through a multifactorial approach. Parameters such as the WSI, insoluble fiber content, and the a* color component showed high variability among the samples. In contrast, morphological parameters like solidity and AR remain consistent across varieties. Digital image analysis proved to be an effective tool for the morphological characterization of different quinoa varieties. This method enabled quick assessment of quinoa seeds’ surface morphology without the need for specific staining or sample destruction. Therefore, digital image analysis could be a fundamental tool for quinoa quality control. The diversity observed in quinoa samples suggests that varietal characterization could be a valuable method for developing food products tailored to specific nutritional, functional, or technological needs. Furthermore, the results allow for identifying the quinoa varieties most suitable for specific formulations or technological processes, such as gel and dough formation or the development of functional foods with targeted nutritional requirements, based on their compositional, functional, and color properties. Declarations Ethical Approval: No applicable Conflict of Interest: No conflicts of interest to declare. Funding: No specific funds to declare Author Contribution Study conception, design, material preparation, data collection, and analysis: all authors. Writing of the first draft: F.R. Revision and critical review: M.L. and N.S. All authors have read and approved the final manuscript. 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J Food Compos Anal 84:103290. https://doi.org/10.1016/j.jfca.2019.103290 Patole S, Cheng L, Yang Z (2022) Impact of incorporations of various polysaccharides on rheological and microstructural characteristics of heat-induced quinoa protein isolate gels. Food Biophys 17:314–323. https://doi.org/10.1007/s11483-022-09702-2 Muñoz-Pabon KS, Roa-Acosta DF, Hoyos-Concha JL, Bravo-Gómez JE, Ortiz-Gómez V (2022) Quinoa snack production at an industrial level: effect of extrusion and baking on digestibility, bioactive, rheological, and physical properties. Foods 11:3383. https://doi.org/10.3390/foods11213383 Roa-Acosta DF, Bravo-Gómez JE, García-Parra MA, Rodríguez-Herrera R, Solanilla-Duque JF (2020) Hyper-protein quinoa flour (Chenopodium quinoa Willd): monitoring and study of structural and rheological properties. LWT 121:108952. https://doi.org/10.1016/j.lwt.2019.108952 Granado-Rodríguez S, Vilariño-Rodríguez S, Maestro-Gaitán I, Matías J, Rodríguez MJ, Calvo P et al (2021) Genotype-dependent variation of nutritional quality-related traits in quinoa seeds. Plants 10:2128. https://doi.org/10.3390/plants10102128 Steffolani ME, Repo-Carrasco-Valencia R, Pérez GT, Condezo-Hoyos L (2020) Physicochemical and functional properties of isolated starch and their correlation with flour from the Andean Peruvian quinoa varieties. Int J Biol Macromol 147:997–1007. https://doi.org/10.1016/j.ijbiomac.2020.01.0 Contreras-Jiménez B, Torres-Vargas OL, Rodríguez-García ME (2019) Physicochemical characterization of quinoa (Chenopodium quinoa) flour and isolated starch. Food Chem 298:124982. https://doi.org/10.1016/j.foodchem.2019.12498 Chang J, Zhao Y, Xu J (2025) Conformational, physiochemical, and functional properties of quinoa protein isolate influenced by thermal treatment. J Food Sci 90:e70051. https://doi.org/10.1111/1750-3841.17005 Tang Y, Li X, Zhang B, Chen PX, Liu R, Tsao R (2015) Characterisation of phenolics, betanins and antioxidant activities in seeds of three Chenopodium quinoa Willd genotypes. Food Chem 166:380–388. https://doi.org/10.1016/j.foodchem.2014.06.018 Escribano J, Cabanes J, Jiménez-Atiénzar M, Ibañez-Tremolada M, Gómez-Pando LR, García-Carmona F, Gandía-Herrero F (2017) Characterization of betalains, saponins and antioxidant power in differently colored quinoa (Chenopodium quinoa) varieties. Food Chem 234:285–294. https://doi.org/10.1016/j.foodchem.2017.05.016 Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialMultifactorialcharacterizationofquinoavarieties.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 20 Aug, 2025 Editor assigned by journal 18 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 14 Aug, 2025 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. 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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-7377157","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507030169,"identity":"35ddeb8b-5079-400c-9d05-d214d546e256","order_by":0,"name":"Francisco Rios","email":"","orcid":"","institution":"Universidad Nacional de Jujuy. Centro Interdisciplinario de Investigaciones en Tecnología y Desarrollo social para el NOA (CIITED-CONCIET-UNJu)","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Rios","suffix":""},{"id":507030170,"identity":"1c0e0ca5-a0f2-462b-9497-8f4b92c2fd41","order_by":1,"name":"Manuel Oscar Lobo","email":"","orcid":"","institution":"Universidad Nacional de Jujuy. Centro Interdisciplinario de Investigaciones en Tecnología y Desarrollo social para el NOA (CIITED-CONCIET-UNJu)","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"Oscar","lastName":"Lobo","suffix":""},{"id":507030171,"identity":"6ffda8b9-70bf-4758-ad9d-952ce3bd1920","order_by":2,"name":"Norma Cristina Sammán","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYJADxgdAgoePFC3MBiAtbKRoYZMAk4SUmbP3Pt3w449dtG772WOVX3PsZNgYmB8+uoFHi2XPcbObvW3JudvO5KXdlt2WDHQYm7FxDh4tBjfS2G7wNjDnbjuQY3ZbchszUAsPmzQhLTf//KnP3Xb+jVmx5LZ64rTc5mE7nLvtRo4Z48dth4nQcuYY223ZtuNALW+MpRm3HedhYybkl+NtbDff/KkGOizH8OPPbdX2/OzNDx/j04ICmHnAJLHKQYDxBymqR8EoGAWjYMQAAJrMSj1ch68wAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Nacional de Jujuy. Centro Interdisciplinario de Investigaciones en Tecnología y Desarrollo social para el NOA (CIITED-CONCIET-UNJu)","correspondingAuthor":true,"prefix":"","firstName":"Norma","middleName":"Cristina","lastName":"Sammán","suffix":""}],"badges":[],"createdAt":"2025-08-14 23:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7377157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7377157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90540641,"identity":"3fdfda1d-ac0a-484d-a955-9909b2cd81a0","added_by":"auto","created_at":"2025-09-03 23:52:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134871,"visible":true,"origin":"","legend":"\u003cp\u003eColor parameters in the CIE Lab* scale. Symbols: ∆ a*, ○ b*, □ L*.\u003c/p\u003e\n\u003cp\u003eDifferent letters above symbols indicate significant differences (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377157/v1/7ddcaca1186b9051bf3fe761.jpg"},{"id":90540648,"identity":"ad56f775-a8af-4393-91e5-69f6b38f5e56","added_by":"auto","created_at":"2025-09-03 23:52:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":498540,"visible":true,"origin":"","legend":"\u003cp\u003eThe biplots present the results of principal component analysis and clustering. a) Loading plot, (b) Scores plot, c) Dendrogram classification of quinoa.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7377157/v1/6acd70e6785060bbeb33b55e.jpeg"},{"id":90542911,"identity":"c7143263-7c8a-40ac-a20b-95a52fd3adc1","added_by":"auto","created_at":"2025-09-04 00:08:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7377157/v1/0821891e-ad92-485c-990c-8bc7602c0b39.pdf"},{"id":90542125,"identity":"ce10c3f4-6c4a-4b9c-aa41-9313c034f2ae","added_by":"auto","created_at":"2025-09-04 00:00:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":349359,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialMultifactorialcharacterizationofquinoavarieties.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7377157/v1/a2ba4782306da3e3a6af1400.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multifactorial characterization of quinoa varieties based on chemical, functional, and morphological parameters","fulltext":[{"header":"Introduction","content":"\u003cp\u003eQuinoa refers to clean and well-preserved seeds of the genus \u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is considered a pseudocereal or even a pseudoseed, as it does not belong to the grass family, although it is consumed and used like cereals. Quinoa is cultivated in the Andean region of Argentina, Chile, Peru, Bolivia, and Ecuador. Since quinoa has high resistance to climatic conditions and soil characteristics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and also good nutritional and functional properties, many countries have introduced it, expanding its use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Quinoa nutritional profile includes high biological value proteins (with a good balance of essential amino acids), starch, dietary fiber, unsaturated lipids, absence of gluten, and notable contents of mineral (Ca, Fe, K, P, Mg, Mn), vitamins (B, C, and E), and bioactive compounds such as isoflavones and polyphenols with antioxidant properties [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Quinoa is gluten free, so it is appropriate for celiac patients or those intolerant to gluten [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe physical properties of quinoa, including morphology and size distribution, are essential for the operation of equipment and facilities in the processes of harvesting, cleaning, transportation, classification, separation, drying, storage, and transformation. These properties generally vary depending on the cultivar, and growing region [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The use of modern techniques has facilitated the determination of physical parameters in grains through digital image processing, allowing for the provision of dimensional measurements, morphometric and colorimetric characteristics, among others [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Digital image processing also allows obtaining descriptors (such as surface area, perimeter, circularity, diameter, and others) without destroying the sample. These descriptors can possibly be correlated with the varieties and their physical attributes. Furthermore, the geometric dimensions, morphological characteristics, fractal dimensions, and color characteristics of quinoa grains were used to identify their geographical origin. Also, along with morphological and color parameters were used to analyze different grain varieties [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKnowledge of the nutritional composition of different varieties of quinoa, as well as their physicochemical and functional properties, is important for establishing their potential uses and for understanding their behavior in technological processes such as cooking, baking, gelatinization, and gel formation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Quinoa has diverse functional or technological properties due to its biopolymers (proteins, starch), which vary between varieties (or genotypes). These include: Flour solubility, Water holding capacity, Water imbibing capacity, Solubility, Water absorption power, and Water binding capacity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the diversity of properties among different varieties, origins, and growing conditions can significantly affect their composition, technological functionality, and physical characteristics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis heterogeneity, which includes differences in protein proportion and structure, starch composition and degree of gelatinization, dietary fiber content and type, as well as the presence of minor compounds, leads to variations in processing performance, such as milling, flour production, extrusion, baking, or protein isolation. These differences directly influence product quality attributes, including bread texture, water retention or absorption capacity, solubility, and gel stability [\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, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, the complete characterization of different quinoa seeds is essential to establish selection criteria for the most suitable raw material, according to the desired final product and its intended food functionality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis work aims to characterize the chemical, functional, and morphological properties of various quinoa varieties to identify the parameters that determine their differentiation and potential application in the development of functional foods.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSamples\u003c/h2\u003e\u003cp\u003eA total of 13 quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd) varieties were subjected to analysis. Samples included three commercially available seeds: Real white (Qc1), Black (Qc2), and Red (Qc3), and ten experimental varieties locally grown at INTA (National Institute of Agricultural Technology) in Miraflores, Jujuy, Argentina. All samples were subjected to saponin removal by hand washing.\u003c/p\u003e\u003cp\u003eDetailed Methodology for Quality and composition analysis, Functional properties, Morphological characteristics, Color determination, and Statistical analysis are included in \u003cb\u003eSupplementary Material.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eProximate composition\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the proximate composition of quinoa seeds, including soluble and insoluble fiber. The analyzed quinoas showed a protein content ranging from 9.81 to 16.35 g/100g on a dry basis. All samples, except Qe1, exceeded the minimum established in national and international regulations for the marketing of quinoa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The coefficient of variation for protein (17.89%) indicates considerable variability, possibly associated to varietal differences.\u003c/p\u003e\u003cp\u003eThe moisture content of all samples did not exceed the maximum limit (13%). The ANOVA determined that ash and moisture content were the parameters with the greatest homogeneity among the samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moisture content only showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between Qc1 and Qe9. Similarly, ash content only showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between samples Qc1\u0026thinsp;=\u0026thinsp;Qe10 compared to Qe5. The coefficient of variation confirms the low variability of the moisture (\u0026lt;\u0026thinsp;4%) and ash (\u0026lt;\u0026thinsp;9%) parameters.\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\u003eProximate composition of quinoa samples (* g/100 g db)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoisture\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtein*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAsh*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFat*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIDF*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSDF*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQc1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQc2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18b\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c7\"\u003e\u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQe9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQe10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eM (CV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e10.95 (3.90)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e12.13 (17.89)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.54 (8.43)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.19 (23.49)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e9.54 (23.51)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3.88 (10.13)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (Maximun)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (Minimum)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5 (Maximun)\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMean values\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation. IDF: Insoluble Dietary Fiber; SDF: Soluble Dietary Fiber. db: dry basis Different superscript letters within rows indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eACF: Limit established by the Argentine Food Code. M (CV): Mean (Coefficient of Variation)\u003c/p\u003e\u003cp\u003eMost experimental samples had a lipid content greater than 5%, with this parameter was one of the most variable, reflecting significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the quinoa samples associated with their varieties.\u003c/p\u003e\u003cp\u003eIn all analyzed samples, insoluble fiber values were higher than soluble fiber values. Likewise, the variability in insoluble fiber content (23.51%) is greater than that of soluble fiber (10.13%). Similarly, Steffolani et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] showed greater dispersion in soluble dietary fiber fraction than in the insoluble one, possibly due to the variety factor, or differences in the degree of maturity or processing.\u003c/p\u003e\u003cp\u003eThe results confirm that quinoa has high nutritional value, with significant variability among varieties, consistent with findings reported by other authors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, the combination of quinoa's high protein, lipid, and dietary fiber content demonstrates its potential as a functional ingredient for food development.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFunctional Properties\u003c/h3\u003e\n\u003cp\u003eThe hydration properties of the quinoa samples are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Both WAI and SP agree that sample Qe2 presents the minimum of the variation range, 4.53 and 4.95 respectively. As both parameters have a low coefficient of variability (\u0026lt;\u0026thinsp;10%), there are significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the samples at the extremes of the variation range. The relationship of WAI and SP with gel formation is consistent with previous studies, indicating that starch gelatinization and protein denaturation contribute to water absorption and swelling capacity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Lower WAI and SP values were determined in quinoa Real and Jeric\u0026oacute; L51 varieties (Colombia) at 30\u0026deg;C [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe solubility index (WSI) ranged from 3.01 to 9.81, with significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between samples. This parameter showed the greatest variability (\u0026gt;\u0026thinsp;40%) among the hydration properties evaluated. The highest WSI values correspond to Qe5 and Qe9 (9.04 and 9.81, respectively) and would be related to a higher content of soluble compounds, such as low molecular weight peptides or soluble polysaccharides [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePhenolic compounds and Antioxidant activity\u003c/h3\u003e\n\u003cp\u003eThe polyphenol and flavonoid contents of quinoa grain extracts were measured, and their antioxidant and anti-radical activities were assessed using FRAP, ABTS, and DPPH assays (Supplementary Information).\u003c/p\u003e\u003cp\u003eThe total polyphenol content showed high variability in quinoa, with an average of 3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 mg/ g. Significant differences were observed between varieties (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The values obtained are comparable to those reported by Tang et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], who also found differences in the content of phenolic compounds between quinoa varieties. The total flavonoid content in quinoa samples ranged from 0.32 to 0.74 mg/g sample, indicating considerable variability among samples. This variability is consistent with the findings reported by [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The pigmented samples exhibited higher polyphenol content and antioxidant activity than the lighter-colored varieties. Sample Qc2 showed the highest polyphenol content (4.02 mg/g) and greater anti-radical activity in the \u003cem\u003ein vitro\u003c/em\u003e assays. Conversely, sample Qe10 had the lowest polyphenol levels and activity against DPPH and ABTS radicals. The results align with studies reporting greater antioxidant capacity in quinoa varieties with intense colors, such as red-violet or yellow, mainly due to the presence of betacyanins, betaxanthins, and other phenolic pigments [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although samples with higher polyphenol contents generally correlated with antioxidant activity in the DPPH, ABTS, and FRAP assays, discrepancies were noted, particularly regarding flavonoid content, suggesting the involvement of other metabolites such as betalains, as indicated by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, the quinoa varieties analyzed possess antioxidant potential that could make them promising ingredients in developing foods with enhanced antioxidant properties.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMorphological Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the morphological and shape descriptors for quinoa seeds, obtained with a laser confocal microscope.\u003c/p\u003e\u003cp\u003eThe seed area showed the greatest variability (13.72%) among shape descriptors. The ANOVA showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between varieties, ranging from 3.12 mm\u0026sup2; (Qe5) to 5.21 mm\u0026sup2; (Qc3). Similar results have been reported in studies on the morphological diversity of quinoa seeds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the case of perimeter, variations between 6.84 mm (Qe5) and 8.87 mm (Qc3) are consistent with the differences in area.\u003c/p\u003e\u003cp\u003eThe descriptors Feret Diameter (FD) and Minimum Feret Diameter (MFD) were highly correlated with area and perimeter, as expected from their underlying geometric relationships. These findings confirm that seed size is highly dependent on variety, in agreement with previous reports on morphological diversity in quinoa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe shape descriptor parameters exhibited low variability. The aspect ratio (AR) and roundness (R) parameters have coefficients of variation less than 1.5% and solidity (S)\u0026thinsp;\u0026lt;\u0026thinsp;0.37%. Despite the low variability of the shape parameters, significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among the samples.\u003c/p\u003e\u003cp\u003eThere is a geometric association between AR and R, as samples with greater elongation are indicative of less roundness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The solidity of all samples showed high and constant values (0.96), reflecting seeds with well-defined and compact shapes, as reported in quinoa quality studies for the industry [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Quinoa Qe5 was the only one that slightly exceeded this value (0.97), possibly due to lower edge roughness or a smoother surface, which increases the fit between the convex and actual areas. These results are consistent with those proposed by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], who linked the seeds\u0026rsquo; external texture with their geographical origin and post-harvest processes. Escribano et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] have shown that the shape and size of quinoa seeds can be used as criteria to differentiate between varieties. Other authors agree that despite differences in variety, the morphological parameters of the grains remain within certain ranges of variation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eColor Parameters\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the color measurements of quinoa samples in the CIELab scale. The L*, a*, and b* values show significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between samples.\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\u003eFunctional and morphometric parameters of quinoa samples\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWAI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWSI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMFD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQc1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.963\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQc2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.964\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQe8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.963\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQe9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.959\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQe10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u0026ordf;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.963\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eM (CV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5.61 (9.12)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.90 (41.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e5.93 (8.03)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4.57 (13.72)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e8.18 (7.46)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.85 (3.55)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e2.59 (7.67)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e2.35 (7.16)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1.07 (1.52)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.93 (1.49)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e0.96 (0.37)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDifferent superscript letters in rows indicate significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eWAI: Water Absorption Index, WSI: Water Solubility Index, SP: Swelling Power, A: Area (mm2), P: Perimeter (mm), Circularity, FD: Feret Diameter (mm), MFD: Minimum Feret Diameter (mm), AR: Aspect Ratio, R: Roundness, S: Solidity. M (CV): Mean (Coefficient of Variation)\u003c/p\u003e\u003cp\u003eThe lightness parameter (L*) showed the greatest range of variation, with values between 68.06 (Qc2) and 90.45 (Qe4), indicating a wide range of variation in light-colored quinoas.\u003c/p\u003e\u003cp\u003eThe a* parameter, associated with the red (positive) - green (negative) axis, shows the greatest variability between samples; it is even the most variable of all the properties analyzed. Sample Qc3 presents the highest a* component that corresponds to the red region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDifferent letters above symbols indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003cp\u003eThe b* parameter, yellow (positive)-blue (negative) axis, also showed significant variability (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between samples, with average values of 14.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96, with Qe5 having the highest value in the yellow region, which differs significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the other samples. The presence of colors in the a* and b* (red-yellow) region could be due to the presence of natural pigments, such as phenolic compounds, anthocyanin, betalains, or other compounds [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The results are consistent with those reported by Tang et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], who observed that quinoa varieties with high levels of total phenols and anthocyanins tend to have higher a* values. Together, the three color parameters analyzed allowed the differentiation of varieties. Furthermore, as a rapid and non-destructive technique, colorimetry offers an objective and reproducible criterion for varietal discrimination in quinoa. Also, Jamanca-Gonzales et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] propose colorimetry as a tool for varietal characterization of Andean grains. Similarly, Pedrali et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated that differences between varieties have a greater impact on the color profile than geographical factors, reinforcing the importance of colorimetry as a varietal descriptor.\u003c/p\u003e\n\u003ch3\u003ePrincipal Components and Cluster Analysis\u003c/h3\u003e\n\u003cp\u003ePrincipal components analysis (PCA) was used as an exploratory method to examine the relationship between the studied variables of different quinoa varieties, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. PCA required five principal components to explain 85.3% of the total data variability, indicating a low correlation between the variables. Therefore, a high degree of independent variation is observed among the variables analyzed. Variables with high correlation (r\u0026thinsp;\u0026gt;\u0026thinsp;0.9) were used as supplementary variables, so they do not influence the construction of the principal components of the PCA.\u003c/p\u003e\u003cp\u003eThe variables that contribute most to Factor1 (F) are the antioxidants (total polyphenols, ABTS, FRAP), color (a*, L), and nutritional (protein, insoluble dietary fiber) parameters. On the F1 positive axis, the samples with the highest protein content, phenolic compounds, and antioxidant capacity (ABTS/FRAP). Conversely, the F1 negative axis is characterized by samples with higher lightness (L*), which are associated with lower antioxidant activity.\u003c/p\u003e\u003cp\u003eTherefore, the F2 factor mainly distinguishes samples based on their morphological parameters and soluble fiber content. A negative correlation was found between insoluble fiber and L* (r = \u0026minus;\u0026thinsp;0.782), indicating that samples with higher insoluble fiber content tend to be darker. Insoluble fiber content also shows a positive correlation with polyphenol content (r\u0026thinsp;=\u0026thinsp;0.62). Other studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] have linked greater presence of phenolic compounds with darker seed color (lower L* values) and with a higher insoluble fiber content. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, the sample score displays clear dispersion in the F1-F2 plane, as revealed by principal component analysis, indicating significant differences among samples in terms of physicochemical, color, antioxidant activity, and morphological features.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe dendrogram confirms the formation of three clusters, although some overlap occurs between experimental and commercial samples. Most experimental samples cluster near the center of the F1-F2 plane, mainly grouped by morphological parameters. On the positive side of the F1-F2 plane, commercial samples clearly separated from the experimental samples located on the negative of F2 axis. Other authors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] have used both PCA and cluster analysis to group varieties based on physical and chemical characteristics.\u003c/p\u003e\u003cp\u003eThe variables used in the analysis allow for differentiation among the samples, with contributions from morphological, color and composition and antioxidant parameters.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results confirm variation among the analyzed quinoa varieties in terms of nutritional composition, functional properties, and morphometric parameters. This observed diversity, mainly related to differences among varieties, allows for the differentiation of quinoa seeds through a multifactorial approach. Parameters such as the WSI, insoluble fiber content, and the a* color component showed high variability among the samples. In contrast, morphological parameters like solidity and AR remain consistent across varieties. Digital image analysis proved to be an effective tool for the morphological characterization of different quinoa varieties. This method enabled quick assessment of quinoa seeds\u0026rsquo; surface morphology without the need for specific staining or sample destruction. Therefore, digital image analysis could be a fundamental tool for quinoa quality control.\u003c/p\u003e\u003cp\u003eThe diversity observed in quinoa samples suggests that varietal characterization could be a valuable method for developing food products tailored to specific nutritional, functional, or technological needs. Furthermore, the results allow for identifying the quinoa varieties most suitable for specific formulations or technological processes, such as gel and dough formation or the development of functional foods with targeted nutritional requirements, based on their compositional, functional, and color properties.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Approval:\u003c/h2\u003e\u003cp\u003eNo applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003cp\u003eNo conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo specific funds to declare\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy conception, design, material preparation, data collection, and analysis: all authors. Writing of the first draft: F.R. Revision and critical review: M.L. and N.S. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge financial support from CONICET-UNJu and the technical assistance of Dr. Marcos Dur\u0026aacute;n (CPA-CONICET), Head of the Microscopy Laboratory.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eNo datasets were generated or analysed during the current study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO/WHO (2019) Codex Alimentarius. Standard for quinoa (CXS 333\u0026ndash;2019). Rome: Food and Agriculture Organization of the United Nations. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/fao-who-codexalimentarius/sh-proxy\u003c/span\u003e\u003cspan address=\"https://www.fao.org/fao-who-codexalimentarius/sh-proxy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Food Chem 166:380\u0026ndash;388. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2014.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2014.06.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEscribano J, Cabanes J, Jim\u0026eacute;nez-Ati\u0026eacute;nzar M, Iba\u0026ntilde;ez-Tremolada M, G\u0026oacute;mez-Pando LR, Garc\u0026iacute;a-Carmona F, Gand\u0026iacute;a-Herrero F (2017) Characterization of betalains, saponins and antioxidant power in differently colored quinoa (Chenopodium quinoa) varieties. Food Chem 234:285\u0026ndash;294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2017.05.016\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2017.05.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"quinoa varieties, nutritional composition, functional properties, morphological properties, principal component analysis","lastPublishedDoi":"10.21203/rs.3.rs-7377157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7377157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to characterize the chemical, functional, color, and morphological properties of different quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd) varieties from the Andean region of northern Argentina. The nutritional composition, including dietary fiber, was analyzed using AOAC methods; antioxidant activities were assessed with DPPH, ABTS, and FRAP assays. Functional properties such as water absorption index (WAI), water solubility index (WSI), and swelling power (SP) were also determined. Additionally, digital image analysis via laser confocal microscopy was employed to evaluate morphometric parameters like area, perimeter, Feret\u0026rsquo;s diameter, circularity, roundness, aspect ratio, and solidity. The results showed significant variability among quinoa samples linked to the variety. The color component a* exhibited the greatest variability (71.54%), followed by WSI (41.58%) and insoluble fiber content (23.50%), indicating heterogeneity in functional and compositional properties. Total polyphenol content ranged from 2.13 to 4.02 mg/g, while total flavonoids ranged from 0.32 to 0.74 mg/g sample. Antioxidant activity varied significantly among samples, with mean IC50 values for DPPH at 13.8 mg/mL, and for ABTS and FRAP at 2.11 and 5.3 mg Trolox equivalents/g sample, respectively. The first two factors (F1 and F2) of principal component analysis (PCA) explained 58% of the total data variability. Variables most influential for differentiation included antioxidants, color, and nutritional parameters. PCA and cluster analyses grouped the samples into three clusters, one of which corresponded to the experimental quinoa varieties. Therefore, these findings suggest that the differentiation of quinoa varieties is influenced by multiple factors.\u003c/p\u003e","manuscriptTitle":"Multifactorial characterization of quinoa varieties based on chemical, functional, and morphological parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 23:52:39","doi":"10.21203/rs.3.rs-7377157/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T18:58:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T18:34:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T18:13:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T15:55:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50807110048447048614028472733458192759","date":"2025-08-22T19:14:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290339343927543555248656254739871391372","date":"2025-08-22T16:36:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181103939819142197367686676277379214288","date":"2025-08-22T16:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255825768209360562371901562218925731600","date":"2025-08-22T12:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67838801069396079660907222798712630729","date":"2025-08-20T17:35:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196952872793561972957897618075506590272","date":"2025-08-20T16:41:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247299565798648957167405859199442511349","date":"2025-08-20T16:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308733417505840513423031065015541664575","date":"2025-08-20T16:22:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T15:41:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-18T18:08:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T12:39:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Foods for Human Nutrition","date":"2025-08-14T23:03:45+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":"c23a44c4-31a9-4e44-8d86-7e845e3009ca","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T19:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 23:52:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7377157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7377157","identity":"rs-7377157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}