The PLS (Partial Least Squares) Toolbox in MATLAB!
Process Monitoring: Implementing on-line models for real-time quality control in chemical manufacturing. matlab pls toolbox
and Cluster Analysis to identify patterns and outliers in unsupervised datasets. Advanced Regression & Classification The PLS (Partial Least Squares) Toolbox in MATLAB
No software is without shortcomings. Critics of the PLS Toolbox point to: embed in GUIs
The PLS Toolbox emerged during a pivotal era in analytical chemistry. In the 1980s and early 1990s, techniques like Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopy were gaining traction for rapid, non-destructive analysis. These techniques produced hundreds or thousands of wavelengths per sample, creating data matrices where the number of variables (p) often far exceeded the number of samples (n). Traditional regression methods like Multiple Linear Regression (MLR) failed due to collinearity, while Principal Component Regression (PCR) could ignore the response variable (e.g., concentration of an analyte) during the decomposition step.
Advanced Analysis and Visualization
🔁 Integrates seamlessly with MATLAB’s environment — automate models, embed in GUIs, or deploy as standalone tools.