Methods: Validation, Preprocessing & Chemometrics

Comprehensive guides for validation strategies, preprocessing techniques, chemometric modeling, and statistical analysis in food spectroscopy.

Quick Navigation

Validation & Scientific Rigor

• Cross-Validation & Leakage — Proper CV implementation
• Model Evaluation — Performance metrics and validation

Preprocessing

• Baseline Correction — Remove baseline drift using ALS, polynomial, and rubberband methods
• Normalization & Smoothing — SNV, MSC, Savitzky-Golay filtering
• Scatter Correction & Cosmic Ray Removal — Handle instrumental artifacts
• Feature Extraction — Peak detection and spectral features

Chemometrics & Machine Learning

• PCA & Dimensionality Reduction — Exploratory analysis and dimension reduction
• Models & Best Practices — PLS-DA, Random Forest, SVM guidance
• Mixture Models — MCR-ALS and mixture quantification
• Model Interpretability — Variable importance and feature selection

Statistics

• ANOVA & MANOVA — Statistical hypothesis testing

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Related Sections

• Tutorials — Step-by-step learning paths
• Workflows — End-to-end analysis patterns
• Theory — Scientific foundations
• API Reference — Python function documentation

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Use this when: You need detailed guides for validation, preprocessing methods, chemometric modeling, or statistical analysis.