Universal Implementation of Partial Least Squares Regression

This article presents a universal implementation approach for Partial Least Squares Regression (PLSR) and demonstrates its application through the case study of "Gasoline Component Modeling Based on Near-Infrared Spectroscopy Analysis." PLSR is a statistical method for building predictive models that excels in handling datasets with numerous variables while effectively addressing multicollinearity issues. The implementation typically involves key computational steps including data preprocessing, dimensionality reduction through latent variable extraction, and cross-validation for model optimization. In our gasoline component modeling example, we showcase how to apply PLSR to analyze and construct predictive models for gasoline components, highlighting practical implementation aspects such as spectral data normalization, PLS component selection using variance explanation criteria, and model validation through root mean square error calculations. This example provides valuable insights into the application of near-infrared spectroscopy technology in chemical analysis, with particular emphasis on reproducible code implementation strategies for handling spectral data matrices and interpreting variable importance in projection (VIP) scores for feature selection.