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

Abstract

Background: 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. Objective: We describe a new approach that applies artificial neural networks (ANN) directly to raw high-performance thin-layer chromatography HPTLC images. Methods: 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. Results: 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. Conclusions: HPTLC fingerprinting, combined with advanced image processing and proper chemometric algorithms, is a simple, efficient, and accurate method for the analysis of TCMs. Highlights: HPTLC fingerprints of four TCM crude drugs derived from Citrus 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.

Effects of different drying methods on the chemical constituents of Lilium lancifolium Thunb. based on UHPLC‐MS analysis and antidepressant activity of the main chemical component regaloside A

“…Most of the Chinese medicinal materials are from medicinal plants, while the components of the same kind of herbs in different drying methods and growing environ-ments have obvious differences [42]. The establishment of the fingerprint of the Chinese medicinal materials can comprehensively reflect the common components of different batches in different drying methods, growing environment, and then describe and evaluate the quality of Chinese medicinal materials as a whole [43,44]. In this study, eight common components of 29 batches of Baihe from different origins were obtained based on UHPLC fingerprint technology.…”

Development of chromatographic technologies for the quality control of Traditional Chinese Medicine in the Chinese Pharmacopoeia

“…To control the comprehensive quality of TCM, some new strategies have been proposed to find out the chemical differences both in raw materials and final products by modern chromatographic techniques [ 19 ]. With the development of modern science and technology, fingerprinting, HPLC tandem mass spectrometry (HPLC-MS/MS), and other new chromatographic technologies have been applied to the analysis of CMMs and their preparations, thereby providing new research prospects and ideas.…”

Near-Infrared Spectroscopy Combined With Support Vector Machine Model to Realize Quality Control of Ginkgolide Production

“…Consequently, it has found applications in the quality control of various natural products, including herbal medicines. Traditional analytical methods such as thin-layer chromatography (TLC) [7,8], gas chromatography (GC) [9,10], high-performance liquid chromatography (HPLC) [11,12], gas chromatography-mass spectrometry (GC-MS) [13,14], and DNA molecular marker techniques [15,16] have also been extensively employed for quality control in traditional Chinese medicine (TCM). For instance, DNA sequencing has been utilized to discern Cordyceps from its counterfeits at the molecular level [17].…”