T10SLRE:一种新的集体学习方法,用于快速而非破坏性地预测小麦中的面包面包体积,使用NIR光谱学
Lei Li1, Wenfei Tian2, Zihui Zhao1
1State Key Laboratory of Crop Gene Resource and Breeding/ National Wheat Improvement Center, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Food chemistry
|October 29, 2025
概括
近红外光谱和机器学习可以快速预测小麦面包的体积,这是一个关键的质量特征. 一种新的组合方法提高了预测准确度,并有效地分类了用于繁殖的小麦品种.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 面包面包的体积是小麦加工质量的关键指标.
- 传统的面包制造试验是劳累和耗时的,阻碍了高效的小麦育种和质量控制.
研究的目的:
- 评估近红外光谱学 (NIRS) 与机器学习相结合,以快速预测小麦面包体积.
- 开发一种新,高效,准确的小麦质量评估方法.
主要方法:
- 使用了5003个小麦样本的数据集,其中4002个用于模型培训和1001个用于独立测试.
- 传统的回归模型和卷积神经网络与一种新的前10%抽样线性回归整体 (T10SLRE) 方法进行了比较.
- 评估T10SLRE方法是因为它能够预测面包体积,并根据面包制造值对小麦生产线进行分类.
主要成果:
- 传统模型和CNN实现了0.70-0.73的R2和23.29-25.08毫升/100克的RMSE.
- 在没有光谱预处理或波长选择的情况下,T10SLRE方法实现了0.74的R2和22.92mL/100g的RMSE.
- 在T10SLRE方法准确地分类小麦线低于850毫升/100克的值,准确度为97.90%.
结论:
- 与机器学习相结合,NIRS为小麦质量评估提供了一个快速而实用的工具.
- 拟议的T10SLRE方法提高了预测准确度和评估面包体积的效率.
- 这种方法可以显著降低小麦育种计划和质量控制所需的成本和精力.
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