Adaptive Interval Selection for Near‐Infrared Spectra via Fused Lasso and Backward Interval PLS

ABSTRACT

Near‐infrared (NIR) spectra exhibit strong local collinearity and smooth absorption patterns, requiring variable selection to mitigate overfitting in multivariate calibration. Point‐wise methods yield scattered subsets that do not account for spectral ordering, whereas fixed‐window approaches lack the adaptability to capture the varying widths of physical absorption bands. To address these limitations, we propose Fused‐BiPLS, a two‐stage wavelength‐interval selection framework that decouples spectral segmentation from predictive coefficient estimation. First, a fused lasso penalty is applied to adjacent coefficient differences and is solved along its exact solution path. Guided by Mallows' criterion, this yields a piecewise‐constant coefficient profile that forms data‐driven, contiguous segments, replacing manually defined interval widths. Second, backward interval partial least squares (BiPLS) identifies a subset of these intervals without further shrinkage. Evaluated across six benchmark NIR datasets spanning agricultural, petrochemical, biological, and pharmaceutical materials, Fused‐BiPLS achieved prediction errors comparable to or lower than those of established chemometric models. The method yielded medium to large positive effect sizes () against most baselines on the Diesel, Soil, and Meat datasets. The adaptive segmentation isolated contiguous blocks corresponding to known molecular overtones, demonstrating stability across sampling variations. Although the exact solution path incurs a higher off‐line calibration cost, the resulting interval‐based models are computationally efficient during inference. Because the selected contiguous intervals align directly with the bandwidth specifications of optical filters, the framework supports the configuration of compact, portable NIR devices.