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.

涉及热过氧化氢 (H ₂ O ₂) 解决方案通常用于棉织物制造。漂白过程的目的是去除棉花的颜色，在进行染色或形状匹配之前获得永久的白色。通常，棉花上白度的视觉等级是根据白度指数 (WI) 来衡量的。然而，发现与实验研究相比，关于棉织物 WI 的化学预测模型的研究很少。使用预测模型的分析可以预测结果，从而做出更明智的棉花质量保证和控制决策。迄今为止，在纺织领域应用最小二乘支持向量回归（LSSVR）模型的研究有限。因此，本研究旨在开发一个多输出 LSSVR (MLSSVR) 模型，使用漂白过程变量和从两个不同案例研究中获得的结果来预测棉花的 WI。MLSSVR 模型的预测精度通过均方根误差 (RMSE)、平均绝对误差 (MAE) 和决定系数 (R²）。将所得结果与偏最小二乘回归、预测模糊模型、局部加权偏最小二乘回归、局部加权核偏最小二乘回归等其他回归模型进行了比较。我们的研究结果表明，所提出的 MLSSVR 模型在预测 WI 方面比其他模型表现更好，因为它显示出显着较低的 RMSE 和 MAE 值。此外，它提供了最高的 R ²值，高达 0.9999。