通过使用FT-NIR光谱和机器学习,先进的非破坏性检测花生改在烤中,使用FT-NIR光谱和机器学习
1Faculty of Agriculture, Department of Biosystems Engineering, Hatay Mustafa Kemal University, Hatay, Türkiye.
概括
在子中检测花生改是非常重要的. 福利埃变换近红外光谱学 (FT-NIRS) 结合机器学习 (ML) 准确量化花生含量,为食品安全提供快速,环保的解决方案.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 食品改,特别是子与花生混合,会给健康和经济带来重大风险.
- 准确的检测和量化方法对于确保食品质量和消费者安全至关重要.
研究的目的:
- 开发和验证一种高效,非破坏性的方法,用于检测和量化花生在子中的改.
- 为了比较两个机器学习算法的性能,拉索和弹性网,用于光谱数据分析.
主要方法:
- 福里埃变换近红外光谱学 (FT-NIRS) 用于从子样本中收集光谱数据,这些样本被不同程度的碎花生 (0-50%) 改.
- 拉索和弹性网机器学习算法用于特征选择和光谱数据的定量分析.
- 用R2 (R2),剩余预测偏差 (RPD) 和根平均平方预测误差 (RMSEP) 等指标评估模型性能.
主要成果:
- 拉索模型在预测花生改方面表现出卓越的准确性,达到R2测试0.93,RPD3.9和RMSEP3.7%的R2测试.
- 弹性网模型的准确性略低,R2测试为0.87,RPD为2.9,RMSEP为5.0%.
- 减少lambda (λ) 值通常会提高预测的准确性,交叉验证证实了拉索模型的稳定性和通用性.
结论:
- 与基于ML的特征选择 (特别是拉索算法) 相结合,FT-NIRS提供了一种高度准确,快速和非破坏性的方法,用于检测和量化子中花生改.
- 这种方法支持可持续的食品生产,为食品改分析提供无浪费,环保和资源高效的解决方案.
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