Yang Jun, Yin Chunling, Miao Xu, Meng Xiangru, Liu Zhimin, Hu Leqian
Engineering Technology Research Center for Grain & Oil Food, State Administration of Grain, Henan University of Technology, Zhengzhou 450001, Henan Province, PR China; College of Chemistry, Chemical and Environmental Engineering, Henan University of Technology, Zhengzhou 450001, Henan Province, PR China.
College of Chemistry, Chemical and Environmental Engineering, Henan University of Technology, Zhengzhou 450001, Henan Province, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Mar 5;248:119251. doi: 10.1016/j.saa.2020.119251. Epub 2020 Nov 28.
Fraud in the global food and related products supply chain is becoming increasingly common due to the huge profits associated with this type of criminal activity and yet strategies to detect fraudulent adulteration are still far from robust. Herbal medicines such as Radix Astragali suffer adulteration by the addition of less expensive materials with the objective to increase yield and consequently the profit margin. In this paper, diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) was used to detect the presence of Jin Quegen in Radix Astragali. 900 fake samples of Radix Astragali produced by 6 different regions were constructed at the levels of 2%, 5%, 10%, 30% and 50% (w/w). DRIFTS data were analyzed using unsupervised classification method such as principal component analysis (PCA), and supervised classification method such as linear discrimination analysis (LDA), K-nearest neighbor (K-NN), linear discrimination analysis combining K-nearest neighbor (LDA-KNN) and partial least squares discriminant analysis (PLS-DA). The results of PCA showed that it was feasible to detect the adulteration of Radix Astragali by the combination of drift technique and chemometrics. PLS-DA obtained the best classification results in all four supervised methods with mean-centralization as the data preprocessing method, the prediction accuracy of PLS-DA model for the six groups of sample ranged from 95.00% to 98.33%. At the same time, LDA-KNN also achieved good classification results, and its correct prediction rate were also between 86.67% and 100.0%. The prediction results confirmed that the combination of DRIFTS technology and chemometrics can distinguish the amount of adulteration present in Radix Astragali. Additionally, the innovative strategy designed can be used to test the fraud of various forms of herbal medicine in other products.
由于全球食品及相关产品供应链中的欺诈行为伴随着这类犯罪活动带来的巨额利润，其现象日益普遍，然而检测欺诈性掺假的策略仍远不够完善。诸如黄芪等草药会被添加价格较低的材料进行掺假，目的是提高产量从而增加利润率。本文采用漫反射中红外傅里叶变换光谱法（DRIFTS）检测黄芪中锦鸡儿根的存在情况。构建了6个不同地区生产的900份伪造黄芪样品，掺假水平分别为2%、5%、10%、30%和50%（w/w）。使用无监督分类方法如主成分分析（PCA），以及监督分类方法如线性判别分析（LDA）、K近邻算法（K-NN）、线性判别分析结合K近邻算法（LDA-KNN）和偏最小二乘判别分析（PLS-DA）对DRIFTS数据进行分析。PCA结果表明，结合漂移技术和化学计量学检测黄芪掺假是可行的。在所有四种监督方法中，以均值中心化作为数据预处理方法时，PLS-DA获得了最佳分类结果，PLS-DA模型对六组样品的预测准确率在95.00%至98.33%之间。同时，LDA-KNN也取得了良好的分类结果，其正确预测率也在86.67%至100.0%之间。预测结果证实，DRIFTS技术与化学计量学相结合能够区分黄芪中的掺假量。此外，所设计的创新策略可用于检测其他产品中各种形式草药的欺诈行为。
