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

Abstract EN

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.