Application of Chemometric Algorithms in the High-Performance Thin-Layer Chromatography Fingerprint of Traditional Chinese Medicines

• Published in: Journal of AOAC International Vol. 102; no. 3; pp. 720 - 725
• Main Authors: Li, Tao, Tian, Runtao, Yu, Xinlan, Sun, Lei, He, Yi, Xie, Peishan, Ma, Shuangcheng
• Format: Journal Article
• Language: English
• Published: England AOAC International 01.05.2019
• Subjects: Algorithms; Artificial neural networks; Asian medicine; Chemometrics; Chromatography; Chromatography, Thin Layer; Citrus; Composition; Discriminant Analysis; Drugs; Drugs, Chinese Herbal - analysis; Image processing; Least-Squares Analysis; Medicine, Chinese Traditional - methods; Methods; Neural Networks (Computer); Principal Component Analysis; Testing; Thin layer chromatography; Traditional Chinese medicine
• ISSN: 1060-3271; 1944-7922
• DOI: 10.5740/jaoacint.18-0306

The use of HPTLC fingerprinting for the analysis of traditional Chinese medicines (TCMs) usually involves several image-processing steps. However, these image-processing steps are time consuming. We describe a new approach that applies artificial neural networks (ANN) directly to raw high-performance thin-layer chromatography HPTLC images. This approach combines image processing and chemometric modeling and was used to classify TCMs [dried tangerine eel (Chen Pi), green tangerine peel (Qing Pi), immature bitter orange fruit, and bitter orange fruit (Zhi Qiao)]. Images of the plates were processed with Chempattern and chemometric analysis including PCA, PLS-DA, and kNN were carried out all by ChemPattern. The ANN model has an accuracy of 100.00% in all training, validation, and test sets, indicating excellent predictive performance and good generalization ability. The k-nearest neighbors (kNN) and partial least-square discriminant analysis (PLS-DA) models have accuracies of 90.91 and 72.73%, respectively, with the independent test set. The kNN model is also accurate, simple, and can be easily interpreted. HPTLC fingerprinting, combined with advanced image processing and proper chemometric algorithms, is a simple, efficient, and accurate method for the analysis of TCMs. HPTLC fingerprints of four TCM crude drugs derived from spp. were compared by using image analysis algorithms. A new approach that applied ANN directly to raw HPTLC fingerprint images was described. Three image analysis algorithms based on kNN, PLS-DA and ANN are compared in the paper. The ANN model shows excellent predictive performance with high accuracy in test sets.