Rana Priya, Liaw Shu-Yi, Lee Meng-Shiou, Sheu Shyang-Chwen
Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan.
Department of Business Management, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan.
Foods. 2021 Nov 19;10(11):2871. doi: 10.3390/foods10112871.
Discrimination of highly valued and non-hepatotoxic species () from hepatotoxic (. , . , and . ) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of species to prevent food fraud.
区分高价值且无肝毒性的物种（）与肝毒性物种（.、.和.）对于防止食品掺假和安全问题至关重要。在本研究中，我们开发了一种利用物理功能特性和化学计量技术区分这四种物种的新方法。通过主成分分析（PCA）和多类判别分析（MDA）对数据进行分析。结果表明，前三个主成分的累积方差贡献率为81.70%。PCA得分图显示不同物种之间有明显的区分。训练集用于构建判别MDA模型，测试集通过该模型进行验证。100%的预测率证明该模型有效且可靠。因此，物理功能特性与化学计量技术相结合构成了一种用于区分物种以防止食品欺诈的实用方法。
