Predicting the whiteness index of cotton fabric with a least squares model

Abstract

The textile bleaching process that involves hot hydrogen peroxide (H2O2) solution is commonly practised in cotton fabric manufacture. The purpose of the bleaching process is to remove color from the cotton, achieving a permanent white before proceeding to dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured based on whiteness index (WI). However, it is found that there is little research on chemical predictive modelling of the cotton fabric’s WI compared to experimental study. Analytics using predictive modelling can forecast the outcomes, leading to better-informed cotton quality assurance and control decisions. Up to date, there is limited study applying least square support vector regression (LSSVR) model in the textile domain. Hence, the present study aims to develop a multi-output LSSVR (MLSSVR) model using bleaching process variables and results obtained from two different case studies to predict the WI of cotton. The predictive accuracy of the MLSSVR model was measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The obtained results were compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression, and locally weighted kernel partial least square regression. Our findings indicate that the proposed MLSSVR model performed better than other models in predicting the WI as it showed significantly lower values of RMSE and MAE. Furthermore, it provided the highest R2 values which are up to 0.9999.