| Evaluation method of grain uniformity of quinoa |
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| DOI：10.7606/j.issn.1000-7601.2026.03.02 |
| Key Words: quinoa grain uniformity morphological trait principal component analysis membership function analysis regression analysis |
| Author Name | Affiliation | | LAI Yunping | Institute of Coarse Cereal, Chengdu Agricultural College, Chengdu, Sichuan 611130, China | | LIU Siyi | Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu, Sichuan 610106, China | | WANG Zhu | Institute of Coarse Cereal, Chengdu Agricultural College, Chengdu, Sichuan 611130, China | | JIANG Yun | Biotechnology and Nuclear Technology Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, Sichuan 610066, China | | WEI Xianya | Institute of Coarse Cereal, Chengdu Agricultural College, Chengdu, Sichuan 611130, China | | YUAN Jin’e | Institute of Coarse Cereal, Chengdu Agricultural College, Chengdu, Sichuan 611130, China | | XU Xia | Institute of Coarse Cereal, Chengdu Agricultural College, Chengdu, Sichuan 611130, China | | ZOU Liang | Institute of Coarse Cereal, Chengdu Agricultural College, Chengdu, Sichuan 611130, China
Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu, Sichuan 610106, China |
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| Abstract: |
| The objective of this study was to establish a comprehensive evaluation and screening model for the purpose of identifying suitable identification indexes of quinoa grain uniformity. A total of 93 quinoas were used as experimental materials to determine 9 morphological traits, including 1 000-grain weight (X1), grain area (X2), grain perimeter (X3), grain length\|width ratio (X4), grain length (X5), grain width (X6), diameter (X7), roundness (X8), and density factor (X9). Combined with multivariate statistical analysis methods such as correlation analysis, cluster analysis, principal component analysis, membership function method, and stepwise regression analysis, the comprehensive evaluation of quinoa grain uniformity was carried out. The results showed that the coefficient of variation for each trait ranged from 1.41% to 13.79%. The quinoa materials were grouped into six distinct categories based on their characteristics. The principal component analysis resulted in the simplification of nine traits into three independent principal components, with a cumulative contribution rate of 99.08%. The comprehensive evaluation values of grain uniformity were calculated from the membership function analysis. The 93 accessions were thus classified into four uniformity types, comprising 10 ultra\|high uniformity materials, 48 high uniformity materials, 32 medium uniformity materials, and 3 low uniformity materials. An optimal linear regression equation was established based on stepwise regression analysis between the predicted value of grain uniformity (VP) and the grain morphological trait index: VP=-5.647+ 0.334X3 +3.727X8+0.737X9 (R2=0.997). In accordance with the stipulated formula, the indicators selected for the identification and evaluation of indices for grain uniformity in quinoa include the grain perimeter, roundness, and density factor. The findings provide a theoretical basis for breeding quinoa varieties with high uniformity. |
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