Rapid Determination of Soybean Protein Content by Near-Infrared Spectroscopy Coupled with Multi-Learner Ensemble Wavelength Selection
Weida Wang; Chunqi Wang; Baocheng Zhao; Jiayi Shi; Changan Xu; Jinming Liu · 2026 · Foods
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Soybean protein content is a key indicator of nutritional value and quality grade, and its determination is important for quality evaluation and cultivar selection. To overcome the time-consuming and costly limitations of conventional chemical assays, this study proposed a multiple linear learner ensemble importance-score wavelength selection (MLLEISWS) method to identify informative wavelengths from soybean near-infrared spectra and establish a partial least squares (PLS) model. MLLEISWS was compared with competitive adaptive reweighted sampling, successive projections algorithm, and uninform
Abstract by Weida Wang; Chunqi Wang; Baocheng Zhao; Jiayi Shi; Changan Xu; Jinming Liu, Foods (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/foods15101755