Rapid Identification of Nine Easily Confused Mineral Traditional Chinese Medicines Using Raman Spectroscopy Based on Support Vector Machine

Abstract

Mineral traditional Chinese medicines (TCMs) are natural minerals, mineral processing products, and some fossils of animals or animal bones that can be used as medicines. Mineral TCMs are a characteristic part of TCMs and play a unique role in the development of TCMs. Mineral TCMs are usually identified according to their morphological properties such as shape, color, or smell, but it is difficult to separate TCMs that are similar in appearance or smell. In this study, the feasibility of using Raman spectroscopy combined with support vector machine (SVM) for rapid identification of nine easily confused mineral TCMs, i.e., borax, gypsum fibrosum, natrii sulfas exsiccatus, natrii sulfas, alumen, sal ammoniac, quartz, calcite, and yellow croaker otolith, was investigated. Initially, two methods, characteristic intensity data extraction and principal component analysis (PCA), were performed to reduce the dimensionality of spectral data. The identification model was subsequently built by the SVM algorithm. The 3-fold cross validation (3-CV) accuracy of the SVM model established based on extracting characteristic intensity data from spectra pretreated by first derivation was 98.61%, and the prediction accuracies of the training set and validation set were 100%. As for the PCA-SVM model, when the spectra pretreated by vector normalization and the number of principal components (NPC) is 7, the 3-CV accuracy and prediction accuracies all reached 100%. Both models have good performance and strong prediction capacity. These results demonstrate that Raman spectroscopy combined with a powerful SVM algorithm has great potential for providing an effective and accurate identification method for mineral TCMs.