Principal component regression (PCR), partial least squares regression (PLSR), and locally weighted partial least squares (LW-PLS) models are supervised learning methods in which a labeled dataset is used to train the model. The split-sample validation is normally used to train these models where a dataset is split into training and testing datasets to develop and evaluate the model. However, a limited study is done to evaluate the prediction performance of PCR, PLSR, and LW-PLS models at the different data splitting ratios. Hence, to address this research gap, this submitted work is conducted to investigate the predictive performance of the abovementioned regression models at the different split sample ratios for the data. Meanwhile, this study also serves to determine the optimal splitting ratios for PCR, PLSR, and LW-PLS models via a simple data splitting method where a minimum of 50% of the entire dataset is allocated to train the model. The optimal split is determined by evaluating the root mean squared error, coefficient of determination, and error of approximation (E_a) for five case studies. For PCR, PLSR, and LW-PLS models, LW-PLS performed better in most of the case studies since it copes better with the nonlinear data. Among these best models in each case study, it was found that the split-sample ratios of above 70% of training data had allowed major improvements in terms of predictive performance as compared to their base scenarios which have the largest E_a values.

Given the carcinogenic properties of formaldehyde-based chemicals, an alternative method for resin-finishing cotton textiles is urgently needed. Therefore, the primary objective of this study is to introduce a sustainable resin-finishing process for cotton fabric via an industrial procedure. For this purpose, Bluesign® approved a formaldehyde-free Knittex RCT® resin was used, and the process parameters were designed and optimized according to the Taguchi L₂₇ method. XRD analysis confirmed the crosslinking formation between resin and neighboring molecules of cotton fabric, as no change in the cellulose crystallization phase. Several machine learning models were built in a sequence to predict the crease recovery angle (CRA), tearing strength (TE) and whiteness index (WI). Assessment of modelling was evaluated through the use of various metrics such as root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Results were compared to those from other regression models, such as principal component regression (PCR), partial least squares regression (PLSR), and fuzzy modelling. Based on the results of our research, the LSSVR model predicted the CRA, TE, and WI with substantially more accuracy than other models, as shown by the fact that its RMSE and MAE values were significantly lower. In addition, it offered the greatest possible R² values, reaching up to 0.9627.