In the world of high-dimensional data analysis, few challenges are as persistent as the "curse of dimensionality." When you have hundreds or thousands of predictor variables (e.g., spectral wavelengths, sensor outputs) but only a handful of samples, standard regression techniques like Ordinary Least Squares (OLS) fail. Enter Partial Least Squares (PLS) regression—a multivariate workhorse that has become the gold standard in chemometrics, bioinformatics, and process engineering.
Thirdly, the toolbox excels in classification. Through methods like PLS-Discriminant Analysis (PLS-DA) and Support Vector Machines (SVM), users can categorize samples based on their spectral fingerprints. This is vital in fields like pharmaceutical quality control, where one must determine if a sample is genuine or counterfeit, or in food science, to authenticate the origin of olive oil or wine. matlab pls toolbox
Benefits of Using MATLAB PLS Toolbox
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