Rapid Evaluation of Wet Gluten Content in Wheat Using Hyperspectral Technology Combined with Machine Learning Algorithms.

ABSTRACT: The development of rapid and intelligent methods is urgently needed for wheat quality evaluation. Using the prediction of wet gluten content as a case study, this work systematically investigated the performance of various machine learning algorithms and their optimization for content prediction, based on hyperspectral data from the visible and near-infrared ranges of wheat grains and flour. The results revealed that the random forest regression (RFR) algorithm delivered the best predictive performance under two conditions: first, when applied directly to visible spectra; and second, when applied to fused visible and near-infrared spectral data. This held true for both grains and flour. Conversely, its direct application to NIR spectra alone yielded relatively worse performance. Following

SUBMITTER: Lai Y 

PROVIDER: S-EPMC12785360 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature