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T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy

文献类型： 外文期刊

作者： Lei Li a;;Wenfei Tian;;Zihui Zhao ;;Yonggui Xiao;; Yong Zhang;;Jindong Liu ;; Velu Govindan ;; Zhonghu He;; Maria Itria Ibba

关键词： Bread wheat machine learning near-infrared spectroscopy quality improvement

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Bread loaf volume is a critical indicator of wheat processing quality, but conventional bread-making tests are laborious and time-consuming. This study evaluated near-infrared spectroscopy combined with machine learning for rapid prediction of loaf volume. A dataset of 5003 wheat samples was divided into 4002 for model training and 1001 for independent testing. Conventional regression models and a convolutional neural network achieved testing set coefficient of determination (R2) of 0.70 to 0.73 with root mean square error (RMSE) of 23.29 to 25.08 mL/100 g. We propose a novel top 10 % sampling linear regression ensemble (T10SLRE) approach, which achieved a R2 of 0.74 and an RMSE of 22.92 mL/100 g, without spectral preprocessing or wavelength selections. It also classified wheat lines below the bread-making threshold of 850 mL/100 g with 97.90 % accuracy. This approach provides a practical tool that reduces cost and effort in wheat quality evaluation.

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