College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, People's Republic of China.
Food Chem. 2013 Dec 15;141(4):4026-30. doi: 10.1016/j.foodchem.2013.06.119. Epub 2013 Jul 4.
Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine.
根据矿物元素含量，对中国三个知名酒厂（“龟鹿山庄”、“会稽山”和“塔牌”）的黄酒进行了鉴别。采用电感耦合等离子体质谱法（ICP-MS）测定了 117 个样品中的 19 种常量和微量元素（Na、Mg、Al、K、Ca、Mn、Fe、Cu、Zn、V、Cr、Co、Ni、As、Se、Mo、Cd、Ba 和 Pb）。然后对实验数据进行方差分析（ANOVA）和主成分分析（PCA），以揭示样品之间的显著差异和潜在模式。逐步线性判别分析（LDA）和偏最小二乘判别分析（PLS-DA）用于建立分类模型，预测样本集的正确分类率分别为 100%和 97.4%。这种鉴别归因于不同的原材料（主要是水）和酿造工艺。结果表明，元素组成结合多元分析可作为保护知名酒厂和确保中国黄酒真实性的指纹技术。
