摘要
This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces.Nine variables of physico-chemical properties (density,pH,dry matter,ashes,electric conductivity,amino nitrogen,salt,viscosity and total acidity) of 53 soy sauce samples were measured.The measured data was submitted to such pattern recognition as cluster analysis (CA),principal component analysis (PCA),discrimination partial least squares (DPLS),linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands.Two clusters corresponding to the two categories were obtained,and each cluster was divided into three subsets corresponding to three brands by the CA method.The variables for LDA and KNN were selected by the Fisher F-ratio approach.The prediction ability of all classifiers was evaluated by cross-validation.For the three supervised discrimination analyses,LDA and KNN gave 100% predications according to the sample category and brand.
This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces.Nine variables of physico-chemical properties (density,pH,dry matter,ashes,electric conductivity,amino nitrogen,salt,viscosity and total acidity) of 53 soy sauce samples were measured.The measured data was submitted to such pattern recognition as cluster analysis (CA),principal component analysis (PCA),discrimination partial least squares (DPLS),linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands.Two clusters corresponding to the two categories were obtained,and each cluster was divided into three subsets corresponding to three brands by the CA method.The variables for LDA and KNN were selected by the Fisher F-ratio approach.The prediction ability of all classifiers was evaluated by cross-validation.For the three supervised discrimination analyses,LDA and KNN gave 100% predications according to the sample category and brand.
基金
the support from the National Natural Science Foundation of China (NSFC20562009)
the Jiangxi Provincial Natural Science Foundation (JXNSF062004)
the State Key Laboratory of Food Science and Technology of Nanchang University (SKLF-MB-200807 & SKLF-TS-200819)
