Tabular foundation models for robust calibration of near-infrared chemical sensing data

Near-infrared spectroscopy is increasingly used as a rapid, non-destructive chemical sensing technology for the analysis of food, pharmaceutical, biological, and environmental samples. However, the practical deployment of NIR sensors still depends on calibration models able to handle high-dimensional, collinear spectra, limited sample sizes, preprocessing dependence, spectral outliers, and extrapolation beyond the calibration domain. Here, we evaluate whether tabular foundation models can provide a new calibration strategy for NIR chemical sensing. We benchmark TabPFN on 66 NIR datasets covering 54 regression and 12 classification tasks, and compare direct inference on raw spectra with preprocessing-optimized inference against PLS/PLS-DA, Ridge, Catboost, and one-dimensional convolutional neural networks. The study uses a unified validation framework in which preprocessing and model selection are performed exclusively on calibration data before external test evaluation. In regression, preprocessing-optimized TabPFN achieves the best overall average rank and significantly outperforms PLS, CatBoost, TabPFN on raw spectra, and CNN-1D, while remaining statistically comparable to Ridge. In classification, TabPFN applied directly to raw spectra provides the best average rank, with performance close to the optimized variant. Robustness analyses show that TabPFN provides strong average predictive performance but that its advantage decreases on spectral outliers and extrapolated samples, where classical chemometric models remain competitive. These results suggest that tabular foundation models can complement established chemometric workflows for NIR chemical sensing, especially in small- to medium-sized calibration settings, while highlighting the need for spectroscopy-specific priors and uncertainty-aware deployment strategies.

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

Near-infrared spectroscopy (NIRS) is rapid and non-destructive, but reliable calibration still depends heavily on spectral preprocessing. In routine practice, preprocessing is often selected by large external pipeline searches that are costly, unstable on small calibration sets, and difficult to audit. We introduce operator-adaptive calibration, a framework that moves linear preprocessing selection inside the calibration model. Candidate treatments are encoded as linear spectral operators, while nonlinear or sample-adaptive corrections such as SNV, MSC, and ASLS are handled as fold-local branches to prevent leakage. We instantiate the framework for PLS and Ridge regression. For PLS, covariance identities enable fast NIPALS and SIMPLS variants while preserving original-wavelength coefficients. For Ridge, operator-adaptive kernels yield a dual formulation with recoverable original-space coefficients. The approach was evaluated on more than 50 heterogeneous NIRS datasets against conventional PLS, Ridge, CatBoost, and CNN baselines under documented search budgets. 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 2.22% with 35 wins on 52 datasets. The proposed models reduce dependence on large preprocessing-HPO campaigns, produce traceable operator choices, retain interpretable coefficients, and fit in seconds for compact AOM-PLS. Operator-adaptive calibration therefore offers a practical route to faster, more robust, and more auditable NIRS method development.