Quantitative analysis of textile delusterant based on terahertz spectral and data fusion strategies

Abstract

Titanium dioxide is a delusterant and an important component in the manufacturing of polyester fiber. For the need of fast, accurate and nondestructive detection of matting agents in textiles, a quantitative analysis method based on terahertz absorption spectroscopy and derivative spectroscopy, combined with chemometrics and data fusion strategy is proposed. This experiment was used two spectra for fusion. The terahertz absorption spectra were obtained in the band of 0.2–1.9 THz by optical parameter extraction. The derivative spectrum was derived from the first-order derivative of the absorption spectrum. Partial least squares (PLS) and data fusion were used to construct a prediction model for titanium dioxide concentration in polyester fiber. Low-level data fusion was the direct combination of two spectral data; The successive projections algorithm (SPA) and Monte Carlo uninformative variable elimination (MCUVE) were employed by mid-level data fusion for feature selection, after which the feature variables were fused; multiple linear regression was used for fusion by high-level data fusion. The prediction accuracy of the high-level data fusion model is higher than that of other models, which the correlation coefficient of cross-validation (Rcv) and correlation coefficient of prediction (Rp) are 0.9229 and 0.9227. The mean relative error (MRE) is 0.2654. The results show that terahertz spectroscopy combined with chemometric methods and high-level data fusion strategies can achieve rapid, accurate and non-destructive detection of titanium dioxide in polyester fiber, which can lay the theoretical foundation for terahertz spectroscopy detection methods for textile additives.