Shi Ting, Zhu MengTing, Chen Yi, Yan XiaoLi, Chen Qian, Wu XiaoLin, Lin Jiangnan, Xie Mingyong
State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China.
State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China.
Food Chem. 2018 Mar 1;242:308-315. doi: 10.1016/j.foodchem.2017.09.061. Epub 2017 Sep 14.
Proton nuclear magnetic resonance (H NMR) and chemometrics were employed to detect the adulteration of camellia oil (CAO) with 3 different cheap vegetable oils. With the intensity of 15 selected H NMR signals as input variables, principal component analysis (PCA) showed good group clustering results for pure and nonpure CAO, but unsatisfied identification accuracy for the adulterated oil types, indicating relatively small difference among those oils. Whereas these difference could be revealed by orthogonal projection to latent structures discriminant analysis (OPLS-DA), with identification accuracy higher than 90%. Partial least squares (PLS) was further applied for the prediction of adulteration level in CAO. With less than 6 variables screened out by variable importance in the projection (VIP) scores as potential key markers, the developed PLS models showed better accuracy. The prediction results for 10 hold-out samples also confirmed that this method was accurate and fast for the detection of CAO adulteration.
采用质子核磁共振（(^1H) NMR）和化学计量学方法检测山茶油（CAO）中掺入3种不同廉价植物油的情况。以15个选定的(^1H) NMR信号强度作为输入变量，主成分分析（PCA）对纯CAO和非纯CAO显示出良好的聚类结果，但对掺假油类型的识别准确率不高，表明这些油之间的差异相对较小。而这些差异可以通过正交投影到潜在结构判别分析（OPLS-DA）揭示出来，其识别准确率高于90%。进一步应用偏最小二乘法（PLS）预测CAO中的掺假水平。通过投影变量重要性（VIP）得分筛选出少于6个变量作为潜在关键标志物，所建立的PLS模型显示出更好的准确性。对10个保留样本的预测结果也证实了该方法用于检测CAO掺假准确且快速。
