Zhao Zihui, Li Lei, Li Wenduan, Tian Yuanyuan, Zhang Yan, Zhang Yong, Ibba Maria Itria, He Zhonghu, Hao Yuanfeng, Tian Wenfei
State Key Laboratory of Crop Gene Resource and Breeding/National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China.
State Key Laboratory of Crop Gene Resource and Breeding/National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; Zhongyuan Research Center, Chinese Academy of Agricultural Sciences (CAAS), Xinxiang 453519, China.
Food Res Int. 2025 Oct;218:116915. doi: 10.1016/j.foodres.2025.116915. Epub 2025 Jun 20.
Bread wheat (Triticum aestivum L.) plays a vital role in global food security and processing. Understanding the rheological properties of dough is crucial in the food industry and wheat breeding programs to select high-quality varieties. Traditional tests such as Farinograph and Extensograph are essential, but labor-intensive and impractical for high-throughput screening. Near-infrared spectroscopy is a rapid and cost-effective alternative to grain quality assessment. This study aimed to develop calibration models for key rheological properties of dough in wheat using a dataset of 1082 representative samples. Various spectral pre-processing, variable selection, and regression algorithms have been employed for model calibration. The partial least squares regression model for Farinograph water absorption demonstrated strong predictive capabilities (Rc = 0.92, Rv = 0.90, and RPD = 3.20), while qualitative analysis was feasible for other characteristics with high accuracy from 80.23 % to 94.27 %. The developed NIR models provide an efficient method for evaluating wheat quality in food processing and wheat breeding.
面包小麦（Triticum aestivum L.）在全球粮食安全和加工中发挥着至关重要的作用。了解面团的流变学特性对于食品工业和小麦育种计划中选择优质品种至关重要。传统测试如粉质仪和拉伸仪是必不可少的，但劳动强度大且不适用于高通量筛选。近红外光谱是一种快速且经济高效的谷物品质评估替代方法。本研究旨在利用1082个代表性样品的数据集开发小麦面团关键流变学特性的校准模型。已采用各种光谱预处理、变量选择和回归算法进行模型校准。粉质仪吸水率的偏最小二乘回归模型显示出强大的预测能力（Rc = 0.92，Rv = 0.90，RPD = 3.20），而对于其他特性的定性分析可行，准确率高达80.23%至94.27%。所开发的近红外模型为食品加工和小麦育种中评估小麦品质提供了一种有效方法。
