红外光谱学与机器学习算法相结合,用于预测乳牛中详细的牛奶矿物质概况
Vittoria Bisutti1, Lucio Flavio Macedo Mota1, Diana Giannuzzi1
1Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro (PD), Italy.
Food chemistry
|August 20, 2024
概括
富里埃变形中红外光谱 (FTIR) 可以预测17种牛奶矿物质. 机器学习模型在评估牛奶质量和牛健康方面表现优于部分最小平方回归.
科学领域:
- 分析化学 分析化学
- 乳制品科学 乳制品科学
- 频谱学是一种光谱学.
背景情况:
- 牛奶矿物质对人类健康至关重要,也是牛奶和牛健康的指标.
- 准确的矿物质分析对于乳制品生产和质量控制至关重要.
研究的目的:
- 评估福里埃变形中红外光谱 (FTIR) 用于预测牛奶中的17种宏观,微量和环境元素.
- 为了比较机器学习 (ML) 和部分最小平方回归 (PLS) 的有效性,用于矿物质预测.
主要方法:
- 福里埃变形中红外光谱法 (FTIR) 用于分析牛奶.
- 部分最小平方回归 (PLS) 和自动机器学习 (ML) 模型被用于元素预测.
- 预测性能使用R平方 (R2) 值进行评估.
主要成果:
- 自动ML模型在矿物元素预测方面显著优于PLS回归.
- 宏观矿物质的R2值在0.59到0.78.8之间.
- 对铜 (Cu) 和 (B) 观察到有希望的可预测性 (R2 = 0.66-0.74),对铁 (Fe), (Mn), (Zn) 和 (Al) 观察到适度的结果 (R2 = 0.48-0.58).
结论:
- 与ML相结合的FTIR光谱学提供了一种快速且具有成本效益的方法来量化牛奶矿物质.
- 这种方法可以帮助奶牛农提高牛奶生产质量和优化奶酪特性.
- 这项研究验证了FTIR光谱作为常规牛奶分析的宝贵工具.
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