Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

2026-05-13 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Physical Sciences & Chemistry, Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Expert, extended

Summary

A new framework called operator-adaptive calibration has been introduced for Near-Infrared Spectroscopy (NIRS), which integrates linear preprocessing selection directly into the calibration model. This approach addresses the computational cost, statistical instability on small calibration sets, and auditability issues associated with traditional external preprocessing pipeline searches. The framework instantiates for Partial Least Squares (PLS) and Ridge regression, representing candidate spectral treatments as linear operators. Nonlinear corrections like SNV, MSC, and ASLS are handled as fold-local branches to prevent data leakage. Evaluated on over 50 NIRS datasets, compact operator-adaptive PLS with ASLS branch preprocessing achieved a median RMSEP/PLS ratio of 0.960 with 42 wins on 57 datasets, while a deployable AOM-Ridge selector improved over tuned Ridge by a median of 2.22% with 35 wins on 52 datasets. This method reduces dependence on extensive hyperparameter optimization, produces traceable operator choices, and maintains interpretable coefficients.

Key takeaway

For analytical chemists and machine learning engineers developing NIRS calibration models, adopting operator-adaptive calibration can significantly streamline your workflow. This approach reduces the need for computationally expensive external preprocessing searches, offering faster model development and more auditable, interpretable results. Consider integrating AOM-PLS or AOM-Ridge, especially when working with heterogeneous datasets or when model transparency is critical for deployment and regulatory compliance.

Key insights

Embedding preprocessing selection within NIRS calibration models enhances robustness, interpretability, and efficiency.

Principles

• Preprocessing selection is model selection.
• Compact operator banks improve bias-variance compromise.
• Linear operators integrate into model algebra.

Method

Operator-adaptive calibration embeds linear preprocessing as model components. For PLS, it uses covariance identities; for Ridge, operator-adaptive kernels. Nonlinear corrections are handled as fold-local branches to avoid leakage.

In practice

• Use nirs4all for AOM-PLS implementation.
• Start with compact operator banks for stability.
• Apply ASLS as a branch for baseline correction.

Topics

• Near-infrared Spectroscopy
• Operator-adaptive Calibration
• Partial Least Squares
• Ridge Regression
• Spectral Preprocessing

Code references

• GBeurier/nirs4all
• GBeurier/nirs4all-webapp

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.