Prediction of peanut protein solubility based on the evaluation model established by supervised principal component regression

文献类型： 外文期刊

第一作者： Wang, Li

作者： Wang, Li;Li, Shurong;Liu, Hongzhi;Liu, Li;Wang, Qiang;Li, Qizhai

作者机构：

关键词： Prediction;Peanut protein solubility;Supervised principal component analysis;Evaluation model

期刊名称：FOOD CHEMISTRY （ 影响因子：7.514； 五年影响因子：7.516 ）

ISSN： 0308-8146

年卷期： 2017 年 218 卷

页码：

收录情况： SCI

摘要： Supervised principal component regression (SPCR) analysis was adopted to establish the evaluation model of peanut protein solubility. Sixty-six peanut varieties were analysed in the present study. Results showed there was intimate correlation between protein solubility and other indexes. At 0.05 level, these 11 indexes, namely crude fat, crude protein, total sugar, cystine, arginine, conarachin I, 37.5 kDa, 23.5 kDa, 15.5 kDa, protein extraction rate, and kernel ratio, were correlated with protein solubility and were extracted to for establishing the SPCR model. At 0.01 level, a simper model was built between the four indexes (crude protein, cystine, conarachin I, and 15.5 kDa) and protein solubility. Verification results showed that the coefficients between theoretical and experimental values were 0.815 (p < 0.05) and 0.699 (p < 0.01), respectively, which indicated both models can forecast the protein solubility effectively. The application of models was more convenient and efficient than traditional determination method. (C) 2016 Elsevier Ltd. All rights reserved.

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