Tabular foundation models for robust calibration of near-infrared chemical sensing data
Researchers have explored the use of tabular foundation models, specifically TabPFN, as a novel calibration strategy for near-infrared (NIR) chemical sensing. In a study involving 66 NIR datasets, TabPFN demonstrated strong performance, particularly in regression tasks where it outperformed several traditional methods. While TabPFN showed promise, its effectiveness diminished with spectral outliers and extrapolated samples, indicating that classical chemometric models remain competitive in these scenarios. The findings suggest that tabular foundation models can enhance existing NIR sensing workflows, especially for smaller datasets, but emphasize the need for spectroscopy-specific considerations and uncertainty awareness. AI
IMPACT Suggests new methods for improving chemical sensing accuracy and robustness, potentially impacting food, pharmaceutical, and environmental analysis.