MATLAB Implementation of PLS_TOOLBOX

PLS_TOOLBOX is a MATLAB toolkit originally developed by international researchers, specifically designed for implementing Partial Least Squares (PLS) algorithms. This toolkit is particularly renowned in the field of chemometrics and is widely applied in scenarios such as spectral analysis, process monitoring, and multivariate data analysis. The strength of this toolbox is demonstrated in several key aspects: First, it provides a complete implementation of PLS regression, supporting multiple preprocessing methods and variable selection functions through configurable parameters. The core algorithm handles matrix decomposition using the NIPALS (Nonlinear Iterative Partial Least Squares) method, which efficiently computes latent variables while managing multicollinearity. Second, the toolbox includes comprehensive visualization capabilities, enabling users to intuitively understand model performance and data characteristics through plots like score plots, loading plots, and VIP (Variable Importance in Projection) charts. For researchers working with high-dimensional data, PLS_TOOLBOX is particularly valuable as it effectively addresses multicollinearity issues among independent variables. The algorithms within the toolbox are highly optimized, maintaining computational efficiency even when processing large-scale datasets through efficient memory management and matrix operations. Additionally, it supports multiple PLS variant algorithms including PLS1, PLS2, and kernel PLS, catering to specific requirements across different application scenarios. Originally developed for chemical analysis, the toolbox's applications have expanded to various industries including pharmaceuticals, food science, and environmental monitoring. Its classical status is reflected not only in functional completeness but also in its algorithm implementations that have been validated through numerous real-world cases, ensuring high reliability. The toolbox incorporates cross-validation methods for model validation, with built-in functions for k-fold and leave-one-out validation to ensure robust generalization capabilities of statistical models. Key functions like plsregress handle the core regression calculations, while supporting utilities facilitate data preprocessing including mean-centering, auto-scaling, and SNV (Standard Normal Variate) transformation.