MATLAB Implementation of PLS_TOOLBOX

The article introduces a highly classic tool - the MATLAB implementation of PLS_TOOLBOX. This toolbox, widely recognized as developed by international researchers, offers powerful capabilities for multivariate data analysis. It can be applied to numerous applications including data analysis, pattern recognition, and machine learning domains. The toolbox implements key PLS algorithms through efficient MATLAB functions, providing capabilities for dimensionality reduction, regression modeling, and cross-validation. Implementation typically involves matrix operations for PLS component extraction and statistical calculations for model validation. Using this toolbox can significantly enhance researchers' efficiency and accuracy in multivariate analysis, while contributing to advancements in related disciplines. The PLS_TOOLBOX includes essential functions for data preprocessing, model building, and result visualization, making it particularly valuable for chemometrics and multivariate calibration tasks. Overall, PLS_TOOLBOX is an extremely useful analytical tool that warrants thorough understanding and mastery by researchers working with multivariate data.