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

TL;DR

This paper introduces a model-internal calibration approach for preprocessing in near-infrared spectroscopy, significantly reducing computation time while maintaining prediction accuracy, validated on a large benchmark cohort.

Contribution

It demonstrates that linear operator-adaptive calibration can replace extensive preprocessing screening, streamlining NIR spectroscopy workflows with comparable results.

Findings

Median RMSEP ratios close to 1 compared to traditional methods.

Selected models achieve higher accuracy and much faster runtimes.

Improved balanced accuracy in classification tasks.

Abstract

Preprocessing screening is often the most expensive part of a near-infrared spectroscopy calibration workflow. It works because smoothing, derivatives, detrending and related filters change the spectral directions seen by PLS or Ridge regression, but a full external search repeatedly refits nearly the same linear model. This paper studies the case where that search can be collapsed into one calibration step. For strict linear preprocessing operators, the transformed PLS cross-covariance satisfies (X A^T)^T Y = A X^T Y, and Ridge regression depends on the operator-induced kernel X A^T A X^T. These identities allow a finite operator bank to be screened inside the model while retaining original-wavelength coefficients. Sample-adaptive or fitted corrections such as SNV, MSC, EMSC and ASLS remain fold-local branches, not absorbed into the algebra. The study uses the AOM benchmark cohort: 61 regression rows and 17 classification rows in the manifest. On the main regression denominator (N=32), plain compact-bank AOM-PLS records median RMSEP ratios of 0.991 against PLS-default and 0.990 against PLS-HPO; the selected ASLS-AOM-compact-cv5 branch records 0.985 and 1.002 on the same two references. The plain AOMRidge-global-compact-none baseline records 0.974 against Ridge-default and 0.984 against Ridge-HPO, while the selected AOMRidge-Blender-headline-spxy3 records 0.918 and 0.966. The selected classifier, AOM-PLS-DA-global-simpls-covariance, improves balanced accuracy by 0.159 on N=13 datasets with 12/13 wins. The runtime gap is the practical result: PLS-HPO takes a median total time of 710.81 s per run, whereas the selected AOM-PLS branch takes 1.63 s. Linear operator-adaptive calibration therefore gives comparable prediction quality to exhaustive preprocessing screening, with orders-of-magnitude less fitting time for PLS.