PLS_Toolbox is a comprehensive chemometrics and multivariate analysis add-on for MATLAB, created to help scientists and engineers turn complex datasets into reliable, interpretable models. It streamlines the full workflow—from data import and preprocessing to model building, validation, visualization, and deployment—while supporting both script-based and graphical, point-and-click use.
Core methods include PCA, PLS and PCR for regression, PLS-DA and SIMCA for classification, and MCR-ALS for mixture resolution. It also offers multiway techniques such as N-PLS and PARAFAC for tensor data. A rich suite of preprocessing tools is built in, including baseline correction, smoothing, derivatives, normalization, autoscaling, SNV, and MSC, along with outlier detection and variable selection options. Robust validation is supported through cross-validation, permutation tests, and diagnostics to assess model stability and prevent overfitting.
Interactive plots and model explorers make it easy to interpret scores, loadings, and residuals, while batch tools enable consistent processing across large datasets. Models can be deployed to standalone environments such as Solo for users who do not run MATLAB.
Common applications span spectroscopy, process analytics, pharmaceuticals, food and agriculture, environmental monitoring, and other fields where high-dimensional sensor or omics data are used for calibration, classification, and exploratory analysis.
PLS_Toolbox - Eigenvector Research is developed by Eigenvector Research. The most popular version of this product among our users is 4.2.
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