title: Prediction of peanut protein solubility based on the evaluation model established by supervised principal component regression.
abstract: 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, 37.5kDa, 23.5kDa, 15.5kDa, 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 15.5kDa) 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.
authors.affiliation: Department of Food and Biological Engineering, Beijing Vocational College of Agriculture, Beijing 102442, China.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Comprehensive Key Laboratory of Agricultural Products Processing and Quality Control, Ministry of Agriculture, Beijing 100193, China.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Liu
authors.affiliation: Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Comprehensive Key Laboratory of Agricultural Products Processing and Quality Control, Ministry of Agriculture, Beijing 100193, China.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Comprehensive Key Laboratory of Agricultural Products Processing and Quality Control, Ministry of Agriculture, Beijing 100193, China. Electronic address: wangqiang06@caas.cn.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Food and Biological Engineering, Beijing Vocational College of Agriculture, Beijing 102442, China.
authors.forename: Shurong
authors.initials: S
authors.lastname: Li
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Qizhai
authors.initials: Q
authors.lastname: Li
mesh_terms: D010367:Arachis / Q000737:chemistry*; D008962:Models, Theoretical; D010940:Plant Proteins / Q000737:chemistry*; D025341:Principal Component Analysis; D012044:Regression Analysis; D012995:Solubility
chemical_list: D010940:Plant Proteins
keywords: Evaluation model; Peanut protein solubility; Prediction; Supervised principal component analysis