Validation of chemometric models - a tutorial.

Area of Science:

• Chemometrics
• Data Science
• Statistical Modeling

Background:

• Random dataset splitting is common but requires careful application.
• Systematic stratification can bias validation metrics (e.g., RMSE, R²).
• Understanding validation levels (repeatability, reproducibility, variation) is essential.

Purpose of the Study:

• To provide a comprehensive guide to numerical and conceptual validation.
• To highlight potential pitfalls in common validation procedures.
• To emphasize the importance of model robustness for future predictions.

Main Methods:

• Discussing the careful application of random calibration/test set splitting.
• Illustrating validation across different levels: repeatability, reproducibility, and material/instrument variation.
• Examining model robustness and its impact on predicting future samples.

Main Results:

• Demonstrating how validation strategies affect figures of merit (RMSE, R²) and model dimensionality.
• Showing the critical importance of robust models for reliable future predictions.
• Highlighting the need for consensus on significant variables across methods and literature.

Conclusions:

• Validation requires careful consideration of data structure and potential biases.
• Robust model validation is key for accurate predictions and reliable scientific conclusions.
• Cross-validation with literature and chemical knowledge ensures model generalizability.