Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
Building better spectroscopy by choosing preprocessing inside the model
Scientists developed a new way to prepare and analyze spectroscopy data by letting the calibration model itself decide which preprocessing treatments to apply, rather than testing hundreds of combinations beforehand. On 57 datasets, their approach matched or beat traditional methods while using far less computation and producing results that are easier to explain and verify.
Near-infrared spectroscopy is used in manufacturing, pharmaceuticals, and food safety to quickly identify material composition without damage. The usual approach of testing many preprocessing options is slow, unreliable with small datasets, and hard to audit for compliance. This method cuts calibration time to seconds, makes preprocessing choices traceable, and keeps results interpretable — meaning labs can develop reliable tests faster and explain their choices to regulators or customers.