A Terahertz Spectroscopy Nondestructive Identification Method for Rubber Based on CS-SVM

Abstract

A method is proposed for rubber identification based on terahertz time-domain spectroscopy (THz-TDS) and support vector machine (SVM). In order to improve the accuracy, the cuckoo search algorithm (CS) is used to optimize the penalty factor C and kernel function parameter g of SVM. The SVM model optimized by the cuckoo search algorithm is abbreviated as CS-SVM. Principal component analysis (PCA) is applied to decrease the dimension of the spectral data. The top ten principal component factors, whose accumulated variance contribution rate reaches 93.93%, are extracted from the original spectra data and then are applied to CS-SVM. The identification rate of testing sets for CS-SVM is 100%, which is significantly higher than 96.67% identification rate of testing sets for PSO-SVM and Grid search. Experimental results show that CS-SVM can accomplish nondestructive identification for different rubber. This method lays a theoretical foundation for the application of terahertz spectroscopy in rubber classification and identification.