PLS1 and RBF-PLS Algorithms Optimized for NIR Sample Processing

In this article, the author presents specialized code implementations for PLS1 and RBF-PLS algorithms specifically adapted for NIR sample analysis. Both algorithms serve as powerful tools for data prediction and modeling, particularly in spectral analysis applications such as Near-Infrared (NIR) spectroscopy. PLS1 (Partial Least Squares 1) represents a linear regression approach commonly employed for multivariate linear regression tasks. The implementation typically involves preprocessing steps like mean-centering and variance scaling, followed by iterative calculation of latent variables that maximize covariance between predictor and response matrices. Key functions would include data normalization, component extraction, and regression coefficient computation. RBF-PLS (Radial Basis Function-Partial Least Squares) combines PLS methodology with radial basis functions to handle nonlinear relationships in spectral data. The implementation generally involves transforming input features using Gaussian kernel functions before applying PLS regression. Critical implementation aspects include kernel parameter optimization (such as sigma value selection) and distance metric calculations between data points. These advanced algorithms significantly enhance model accuracy by capturing complex patterns in spectral data and contribute to better understanding of sample characteristics through dimensionality reduction and feature extraction capabilities. The code implementations likely include cross-validation routines, model performance metrics (like RMSE and R²), and visualization tools for interpreting spectral relationships.