Prediction of Wheat Quality Using GlutoPeak Combined with Principal Component Analysis

被引：0
|
作者：
Jiang L. [1 ]
Li X. [1 ]
Cao Y. [1 ]
Ma X. [1 ]
Wang M. [1 ]
Hao J. [1 ]
Zhang D. [1 ]
Ji H. [1 ]
机构：
[1] Institute of Wheat Research, Shanxi Agricultural University, Linfen
来源：
Shipin Kexue/Food Science | 2022年 / 43卷 / 14期
关键词：
Comprehensive evaluation; GlutoPeak test; Principal component analysis; Quality analysis; Wheat;
D O I：
10.7506/spkx1002-6630-20210929-349
中图分类号：
学科分类号：
摘要：
The aim of this study was to verify the feasibility of applying GlutoPeak test (GPT) for rapid evaluation of wheat quality and establish a comprehensive evaluation method for wheat quality traits based on GPT.Conventional methods and GPT were used to determine 33 quality characteristics of 81 wheat varieties, and principal component analysis (PCA)was used to evaluate their comprehensive quality. The orthogonal partial least squares (OPLS) method was used to verify the comprehensive quality evaluation model and its relationship with the characteristic indices measured by GlutoPeak, and analyze the correlation between them and the variable importance in the projection (VIP) values. The results showed that there were extensive genetic variations in the 33 quality traits among wheat varieties, among which the coefficients of variation of starting energy, stabilization energy, stabilization time, stretching area and maximum stretching resistance were all above 60%.PCA showed that the cumulative contribution rate of the six principal components extracted was as high as 84.221%, which could comprehensively reflect wheat quality information. The function for comprehensive evaluation of wheat quality was established as follows: Y = 0.283F1 + 0.214F2 + 0.183F3 + 0.130F4 + 0.122F5 + 0.066 3F6, where the correlation coefficients (R2) between AM and PM and the comprehensive quality evaluation score were 0.706 1 and 0.739 2, respectively. Correlation analysis among 18 indicators that significantly affected the comprehensive evaluation score of wheat quality showed that there were significant or extremely significant correlations between the GPT indicators and wheat grain quality attributes and dough rheological properties. This study confirmed the feasibility of using GPT to quickly evaluate wheat quality, which can provide a basis for an accurate understanding of wheat quality, the establishment of wheat quality evaluation criteria and wheat quality control. © 2022, China Food Publishing Company. All right reserved.
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页码：85 / 92
页数：7
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