Adulteration Recognition Between Taoren and Xingren by Hyperspectral Non-destructive Technology with Mixed Metaheuristics RBF-SVM Model

摘要

Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine (RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2% in five-floods cross-validation that increased 4.818% than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7% which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.

关键词

hyperspectral technology / adulteration recognition / machine learning