Data Fusion of Laser-Induced Breakdown Spectroscopy (LIBS) and RAMAN Spectroscopy: Improving the Accuracy of Ginseng Classification

Abstract

Ginseng, a traditional Chinese herbal medicine, has experienced increasing application. Due to variations in the medicinal value of ginseng based upon its cultivation location, an accurate method is required to identify its origin. Here laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy were employed with machine learning algorithms for the authentication of ginseng. Accurate origin identification was conducted on dried white ginseng. Initially, LIBS and Raman spectra were acquired separately. Regional differences were not observable in the LIBS spectra. Conversely, in the Raman spectra, similarity was observed between the Baishan and Dunhua regions, contributing to reduced accuracy. Subsequently, the LIBS and Raman spectra were fused. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS, Raman, and fused data. The top 20 principal components for these data sets explained variances of 97.17%, 86.51%, and 94.22%, respectively. The reduced-dimension LIBS, Raman, and LIBS-Raman data were utilized as input variables for random forest, extreme gradient boosting, and categorical boosting (CatBoost) classification. The results indicate that the LIBS-Raman data yield the highest accuracy with CatBoost reaching 99%. Compared to the results using LIBS and Raman, there were improvements in accuracy of 11% and 6%. This method holds significance for the rapid and accurate identification of ginseng.