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

Abstract

Chinese traditional medicine (CTM) has a longstanding history and plays a crucial role in complementary and alternative medicine. However, ensuring the quality and safety of CTM products has been a persistent concern due to the lack of effective quality control methods. This study addresses this concern by leveraging chemometric models, specifically partial least squares (PLS), support vector machine (SVM), and random forest, in conjunction with near-infrared spectroscopy (NIRS) data. These models are applied to establish a comprehensive quality control framework for ginkgolide production. This framework includes predicting terpenolactones content at three key production stage of ginkgolide product development. The collect extensive NIRS data throughout the ginkgolide production process and develop chemometric models using PLS, SVM, and random forest algorithms. These models are rigorously validated through cross-validation and independent testing to assess their accuracy and precision in predicting chemical content and classifying product stages. The result reveal that the SVM model, when applied to NIRS data, demonstrates outstanding performance in terms of accuracy and precision. It excels in predicting chemical content and effectively classifying the various stages of ginkgolide production.