食品科学 ›› 2022, Vol. 43 ›› Issue (14): 85-92.doi: 10.7506/spkx1002-6630-20210929-349

• 食品化学 • 上一篇    

基于主成分分析的面筋聚集仪预测小麦品质

姜兰芳,李晓丽,曹勇,马小飞,王敏,郝建宇,张定一,姬虎太   

  1. (山西农业大学小麦研究所,山西 临汾 041000)
  • 发布日期:2022-07-28
  • 基金资助:
    山西农业大学(省农科院)优秀青年基金项目(YCX2020YQ40;YCX2020YQ52); 山西省现代农业产业技术体系建设项目(2020-02);国家现代农业产业技术体系建设专项(CARS-03)

Prediction of Wheat Quality Using GlutoPeak Combined with Principal Component Analysis

JIANG Lanfang, LI Xiaoli, CAO Yong, MA Xiaofei, WANG Min, HAO Jianyu, ZHANG Dingyi, JI Hutai   

  1. (Institute of Wheat Research, Shanxi Agricultural University, Linfen 041000, China)
  • Published:2022-07-28

摘要: 旨在验证面筋聚集仪测定快速评价小麦品质的可行性,建立基于聚集仪测试的小麦品质性状综合评价方法。采用常规品质测定法和面筋聚集仪法测定81 份不同小麦品种中33 个品质特性指标,利用主成分分析法对其进行综合品质评价。运用正交偏最小二乘法验证综合品质评价模型及其与面筋聚集仪测定指标的关系,分析其变量投影重要性及相关性。结果表明,33 个品质性状在品种间都存在广泛的遗传变异,其中启动能量、稳定能量、稳定时间、拉伸面积、最大拉伸阻力的变异系数均在60%以上。主成分分析结果表明,提取的6 个主成分累计贡献率高达84.221%,可综合反映小麦品质信息,构建小麦综合品质评价函数:Y=0.283F1+0.214F2+0.183F3+0.130F4+0.122F5+0.066 3F6,其中峰前值和峰后值与综合品质评价得分的R2分别为0.706 1和0.739 2。18 个显著影响小麦综合品质的指标间的相关性分析表明,面筋聚集仪测定指标与籽粒品质、面团的流变学特性之间呈显著或极显著相关性。该研究验证了使用面筋聚集仪快速评价小麦品质的可行性,为准确、快速了解小麦品质、品质评价标准建立及品质调控提供参考和依据。

关键词: 小麦;面筋聚集仪;品质分析;主成分分析;综合评价

Abstract: 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.

Key words: wheat; GlutoPeak test; quality analysis; principal component analysis; comprehensive evaluation

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