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

Abstract

In this paper, the ginsengs from five ginseng origins are discriminated by using laser-induced breakdown spectrum (LIBS) combined with random forestsupport 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.

Multilayer Perceptron Classification Model Based on Weighted Majority Voting for Classifying Ancient Glass Products

“…Notably, some researchers recently proposed optimized random forest models that were applied to predict the compressive strength of concrete with good outcomes [6] . Zheng et al [7] combined the random Forest-multilayer sensing algorithm with laser-induced breakdown spectroscopy (LIBS) to accurately distinguish ginseng from different origins. Lu [8] integrated feature selection methods and classification algorithms from machine learning into the research on chemical composition analysis and category identification of ancient glass artifacts.…”