Quantitative analysis of multicomponent mixtures of rubber and additives based on terahertz time-domain spectroscopy and Krawtchouk moments

Abstract

This paper presents a quantitative analytical method based on terahertz time-domain spectroscopy and chemometrics, which was used to detect the content of diphenyl-p-phenylenediamine in a five-component mixture comprising tire rubber and additives. We separately extracted the features of the spectra by using principal component analysis and Krawtchouk moments, and then used partial least squares regression and support vector regression to develop quantitative analysis models. The experimental results show that support vector regression outperforms partial least squares regression in the quantitative analysis of multicomponent mixtures, and the Krawtchouk moments method effectively enhances the prediction accuracy of the model. The Krawtchouk moment combined with support vector regression model shows excellent predictive ability. The correlation coefficient and root mean square error of Krawtchouk moment combined with support vector regression model for the calibration set were 0.9749 and 1.7256%, respectively, and for the prediction set were 0.9669 and 1.9777%, respectively. The results demonstrate that terahertz time-domain spectroscopy combined with Krawtchouk moments and support vector regression is an effective method for determining the antioxidant content in rubber and additive compounds.