Wang Yuling, Zhang Chen, Li Xinhua, Xing Longzhu, Lv Mengchao, He Hongju, Pan Leiqing, Ou Xingqi
School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China.
School of Information Engineering, Xinxiang Institute of Engineering, Xinxiang 453700, China.
Foods. 2025 Jul 7;14(13):2393. doi: 10.3390/foods14132393.
This research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. Five different algorithms were employed to mine the relationship between the full-range spectra (900-1700 nm) and three parameters, with support vector regression (SVR) demonstrating the best prediction performance for all gluten parameters (R = 0.9370-0.9430, RMSEP = 0.3450-0.4043%, and RPD = 3.1348-3.4998). Through a comparative evaluation of five wavelength selection techniques, 25-30 optimal wavelengths were identified, enabling the development of optimized SVR models. The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (R = 0.9190-0.9385, RMSEP = 0.3927-0.5743%, and RPD = 3.0424-3.2509). The model's robustness was confirmed through external validation and statistical analyses ( > 0.05 for F-test and -test). The results highlight the effectiveness of micro-NIRS combined with iWOA-SVR for the nondestructive gluten quality assessment of wheat flour, providing a more valuable reference for expanding the use of NIRS technology and developing portable specialized NIRS equipment for industrial-level applications in the future.
本研究实现了一种集成机器学习方法的小型近红外光谱（NIRS）系统，用于以非侵入性方式定量评估小麦粉中的干面筋含量（DGC）、湿面筋含量（WGC）和面筋指数（GI）。采用五种不同算法挖掘全谱（900 - 1700 nm）与三个参数之间的关系，支持向量回归（SVR）对所有面筋参数表现出最佳预测性能（R = 0.9370 - 0.9430，RMSEP = 0.3450 - 0.4043%，RPD = 3.1348 - 3.4998）。通过对五种波长选择技术的比较评估，确定了25 - 30个最佳波长，从而开发出优化的SVR模型。基于改进鲸鱼优化算法iWOA的SVR（iWOA - SVR）模型在基于五个最佳波长的模型中表现出最强的预测能力，对所有面筋参数的预测精度与全谱SVR相当（R = 0.9190 - 0.9385，RMSEP = 0.3927 - 0.5743%，RPD = 3.0424 - 3.2509）。通过外部验证和统计分析（F检验和t检验的p值> 0.05）证实了该模型的稳健性。结果突出了微型NIRS结合iWOA - SVR用于小麦粉面筋质量无损评估的有效性，为未来扩大NIRS技术的应用范围以及开发用于工业级应用的便携式专用NIRS设备提供了更有价值的参考。
