Spectroscopy quantitative analysis cotton content of blend fabrics

Abstract

Purpose

The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics.

Design/methodology/approach

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r _p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively.

Findings

The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics.

Originality/value

It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

Citation

Sun, X., Zhou, M. and Sun, Y. (2016), "Spectroscopy quantitative analysis cotton content of blend fabrics", International Journal of Clothing Science and Technology, Vol. 28 No. 1, pp. 65-76. https://doi.org/10.1108/IJCST-07-2015-0076