Shared Wavelength Selection for Multicomponent Analysis of Corn by Near-Infrared Spectroscopy

Near-infrared spectroscopy supports rapid quantification of moisture, oil, protein, and starch in corn. Their prediction models may require different spectral inputs, complicating shared-variable selection. We developed the multicomponent shared wavelength selection (MCSWS) algorithm to select one fixed-size subset for all four constituents. It combines a scale-normalized objective with five adaptively fused descriptors. Evaluation used 80 corn samples originally measured at Cargill and distributed by Eigenvector Research, with 700 wavelengths from the m5 instrument. Eight repeated four-way splits assessed held-out prediction (outer cross-validation). Selection and calibration tuning used training-only five-way splits (inner cross-validation). MCSWS retained 38 variables, a 94.6% reduction, and achieved a joint normalized root-mean-square error of prediction (JNRMSEP) of 0.35 ± 0.03. Rounded results were 0.34 ± 0.02 for full-spectrum partial least squares (PLS) regression and competitive adaptive reweighted sampling coupled with PLS (CARS-PLS). Values are means ± sample standard deviations across eight repeat means. After exploratory corn calibration development, the frozen workflow yielded JNRMSEP = 0.049 using six variables on the official casein–glucose–lactate (CGL) test split versus 0.047 for CARS-PLS and 0.016 for full-spectrum PLS. These results characterize an accuracy–dimensionality trade-off. Variable reduction concerns regression inputs after full-spectrum preprocessing, not demonstrated reductions in physical measurements.