预测多种大豆品种的组成从整个和磨砂的种子使用福里埃变换近红外光谱学 (FT-NIRS) 和机器学习

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Guillaume Lassalle¹, Stéphane Michaux¹, Philippe Pollien²

¹Nestlé Institute of Agricultural Sciences, Plant Sciences Department, 101 Avenue Gustave Eiffel, 37390 Notre-Dame-D'Oé, France.

|July 17, 2025

概括

福利埃转换近红外光谱 (FT-NIRS) 与机器学习相结合,可以准确预测大豆的成分,包括营养素,氨基酸和挥发性化合物. 这种进步有助于质量控制和作物开发.

科学领域:

农业科学农业科学
分析化学分析化学
食品科学食品科学食品科学

背景情况:

大豆越来越多地被纳入饮食中,因此需要进行全面的成分分析.
目前的里叶变换近红外光谱 (FT-NIRS) 模型仅限于宏观组成.
需要先进的分析方法来对大豆进行详细的分析.

研究的目的:

评估FT-NIRS和机器学习用于预测广泛的大豆成分.
量化蛋白质,脂质,脂肪酸,氨基酸,微量营养素,维生素和挥发性有机化合物 (VOCs).
探索大豆超越宏观组成的详细分析的潜力.

主要方法:

利用FT-NIRS进行光谱数据采集.
应用机器学习算法,包括支持向量,弹性网和部分最小平方回归.
集成频谱预处理技术用于模型优化.

主要成果:

对蛋白质,脂质,多不和脂肪酸,亚麻酸,15种氨基酸,4种微量营养素,5种维生素和14种挥发性有机化合物,达到可接受的高精度 (R2 0.600.96,NRMSE 5.321%).
确定了最佳回归模型和预处理过器,以准确量化.
证明FT-NIRS能够预测各种大豆成分.

结论:

与机器学习相结合的FT-NIRS提供了用于详细分析大豆成分的强大工具.
开发的模型显示出在食品安全,质量控制和育种计划中的应用非常有前途.
这种方法支持大豆制品的改进和品种开发.

关键词:

组成构成构成里埃变换近红外光谱学近红外光谱学机器学习机器学习豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆多样性多样性多样性

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