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

Abstract

The textile bleaching process that involves hot hydrogen peroxide (H 2 O 2 ) 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 (R 2 ). 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 R 2 values which are up to 0.9999.

Optimization and prediction of the cotton fabric dyeing process using Taguchi design-integrated machine learning approach

“…According to Satrio et al 47 , negative R 2 values indicate a large gap between the observed and predicted values; in this case, the fuzzy technique in this investigation yielded extremely dissimilar results. This is because the training data creates rules and criteria that form the basis of the fuzzy method's membership function, fuzzy logic operators, and if–then statements 19 . An assortment of fuzzy rules, a database detailing the membership functions used by the fuzzy rules, and a reasoning mechanism outlining the inference path upon the rules to adopt projected data are the three parts of this framework 48 .…”

“…The LSSVR model is a nonlinear prediction method based on support vector machine theory (SVM). When it comes to decreasing the computational burden associated with reducing the number of viable classes, LSSVR delivers a more efficient response than the SVM by applying a separate set of linear equations in dual space 19 , 20 . In recent years, academics from a broad range of disciplines have devoted increasing amounts of attention and interest throughout the course of many years.…”

Environmentally Friendly Bleaching Process of the Cellulose Fibres Materials Using Ozone and Hydrogen Peroxide in the Gas Phase

“…The above WI CIE index is limited to the value 40 < WI CIE < 5Y − 280. The investigations on the predictive modelling of the cotton fabric’s whiteness index, in COT-NaOH-H 2 O 2(as) for various reagent concentrations and bleaching conditions, were carried out recently [ 82 ].…”

Effect of polishing on milling, nutritional properties, and optimization of polishing process parameters for kodo and browntop millets: A response surface methodology approach

“…Further, to validate the optimization, experimental runs were performed at suggested optimum conditions. The actual values of responses were compared against predicted responses by a method prescribed by Yeo and Lau (2021) with little modification (Equation (7)): …”