Discrimination of ginseng origin by using laser‐induced breakdown spectrum and machine learning algorithms

In this paper, the ginsengs from five ginseng origins are discriminated by using laser- induced breakdown spectrum (LIBS) combined with random forest- support vector machine (RF- SVM) and random forest- multilayer perception (RF- MLP) machine learning algorithms. The raw LIBS of ginseng is pretreated by using the wavelet threshold method, denoise the background information and normalazation to improve the signal- to- background ratio and the experimental reliability. The RF algorithm is used to select 10 characteristic spectral lines as the input vectors of the MLP and the SVM models to identify the ginseng orgin. The experimental results show that the discrimination accuracy rates of RF- MLP and RF- SVM models are 99.75% and 99.5%, respectively. The disrimination accuracy of ginseng origin used in the RF-MLP machine algorithm model is slightly higher than that of the RF- SVM model, and then calculated the speed of the RF- MLP model is faster than the RF- SVM model. The results show that LIBS combined with machine algorithms are both promising rapid discrimination methods for ginseng origin.