Spectral Contrast Features: A Bin-Difference Approach to Interpretable, Parsimonious, and Cross-Instrument NIR Calibration

Abstract

Near-infrared (NIR) spectroscopy with full-spectrum chemometric modeling is widely used in food, agricultural, and pharmaceutical analysis, but calibrations resting on hundreds to thousands of spectral variables are difficult to audit and require full-spectrum instrumentation to deploy. The Spectral Contrast Feature (SCF) framework constructs predictive features as differences between the mean intensities of paired spectral bins, with bin positions, widths, and feature count optimized by a genetic algorithm. SCF-PLSR was evaluated on cocoa bean moisture (n = 72), barley adulteration in roasted coffee (n = 158), wheat grain protein (n = 496), and the IDRC 2002 pharmaceutical tablet shoot-out dataset, against full-spectrum PLSR and four established wavelength-selection methods under repeated evaluation. Using three to seven contrast features in place of 601 to 1559 spectral variables, SCF-PLSR matched or exceeded every comparator on same-instrument prediction. Test-set RMSE fell by 25% for coffee–barley and 15% for wheat protein. On the tablet dataset under second-derivative preprocessing, zero-shot transfer to a second instrument gave RMSE 17% lower than full-spectrum PLSR. Selected features mapped onto established NIR absorption regions, indicating that a calibration built on a few chemically assignable contrasts is both auditable and compatible with targeted, reduced-cost instrumentation.