PLS for Spectral Analysis including Data Reading Process

Complete Workflow Analysis of PLSR for Spectral Analysis

Data Reading and Preprocessing Spectral data is typically stored in structured text or specialized formats (such as .csv/.spc). When reading data, attention must be paid to wavelength and absorbance alignment. Common tools include pandas (for tabular data) or specialized spectral library parsers. Raw spectra often exhibit baseline drift and noise, requiring preprocessing using Standard Normal Variate (SNV) or Multiplicative Scatter Correction (MSC) to eliminate scattering interference. In Python implementation, the pandas.read_csv() function handles CSV data loading, while scipy.signal filters can apply SNV normalization through standard scaling operations.

Wavelet Transform Denoising Discrete Wavelet Transform (DWT) decomposes spectral signals into different frequency components. High-frequency coefficients represent noise and can be removed through threshold filtering methods (like hard thresholding), preserving low-frequency characteristic components. Symlet or Daubechies wavelet basis functions are suitable for processing spectral smoothness characteristics, with decomposition levels typically chosen between 3-5 to balance denoising efficiency and information retention. Python's PyWavelets library provides dwt() and idwt() functions for implementing multi-level decomposition and reconstruction with threshold processing.

PCA Dimensionality Reduction and Feature Extraction Principal Component Analysis (PCA) projects high-dimensional spectral data into a lower-dimensional space, eliminating multicollinearity between wavelengths. The number of principal components is determined by cumulative contribution rate (e.g., 95%), while loadings plots help interpret the chemical meaning of each principal component (e.g., a specific component may correspond to particular functional group vibrations). Scikit-learn's PCA() class implements this with fit_transform() method for transformation and explained_variance_ratio_ attribute for contribution analysis.

Key Steps in PLS Modeling Partial Least Squares Regression (PLSR) simultaneously decomposes the spectral matrix X and concentration matrix Y, establishing correlation models through Latent Variables (LVs). The number of LVs must be optimized to avoid overfitting: the first few LVs typically contain valid information, while subsequent components may introduce noise. Variable Importance in Projection (VIP) scores help screen critical wavelengths. The PLSRegression class in scikit-learn provides n_components parameter tuning and VIP calculation through regression coefficients and loading weights.

Cross-Validation Strategies Sample set division using Kennard-Stone or SPXY algorithms ensures uniform distribution between training and test sets. k-fold cross-validation (e.g., 10-fold) evaluates model stability, with RMSECV (Root Mean Square Error of Cross-Validation) and R² metrics determining model performance. External validation sets must remain completely independent of the training process. Scikit-learn's train_test_split() and cross_val_score() functions implement these strategies with stratified sampling options.

Extended Considerations Integrating CNN can automatically extract local spectral features, compensating for wavelength selection dependencies in traditional methods Transfer learning adapts well to spectral data calibration from different instruments Explainable AI techniques (like SHAP values) help interpret the chemical decision logic of PLSR models PyTorch or TensorFlow frameworks can implement 1D-CNN architectures for spectral feature learning, while SHAP library provides model interpretation capabilities for regression outputs.