介电光谱技术与机器学习方法相结合,用于非破坏性检测新鲜牛奶中的蛋白质含量
Qing Liang1,2,3, Yang Liu1,2,3, Hong Zhang1,2,3
1College of Mechanical and Electronic Engineering, Tarim University, Alaer, China.
Journal of food science
|October 11, 2024
概括
这项研究开发了一种快速,非破坏性的方法,使用介电光谱检测牛奶蛋白. 最好的模型在预测蛋白质含量方面取得了很高的准确性,有助于评估牛奶质量.
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
- 生物物理学的生物物理.
背景情况:
- 准确和快速的蛋白质含量测定对于新鲜牛奶的质量控制至关重要.
- 对于实时牛奶分析来说,非常需要非破坏性分析技术.
- 介电光谱学为分析牛奶成分提供了一种有前途的非侵入性方法.
研究的目的:
- 建立一种快速,非破坏性的方法来检测新鲜牛奶中的蛋白质含量.
- 探索介电光谱在2-20 GHz范围中的有用性,以量化蛋白质.
- 开发和比较各种化学测量模型,以准确预测蛋白质.
主要方法:
- 使用网络分析仪和开放的同轴探针测量2-20 GHz范围内的牛奶样本的介电光谱 (介电常数 ε'和损失因子 ε').
- 应用的特征提取算法:连续投影算法 (SPA),非信息变量消除 (UVE) 和联合UVE-SPA.
- 开发和评估预测模型,包括部分最小平方回归 (PLSR),支持向量机 (SVM),决策树 (DT),随机森林 (RF) 和最小平方提升 (LSBOOST).
主要成果:
- 介电常数 (ε') 随频率单调地下降,而介电损耗因子 (ε'') 则单调地增加.
- 在选择从介电光谱中的最佳特征变量时,UVE-SPA方法被证明是有效的.
- 该UVE-SPA-PLSR模型实现了优异的预测性能,其中RC2 = 0.998,RP2 = 0.989,RMSEC = 0.019%,以及RMSEP = 0.032%.
结论:
- 介电光谱在2-20 GHz范围内,结合先进的特征选择和化学建模,可以准确,非破坏性地确定牛奶蛋白质含量.
- 该UVE-SPA-PLSR模型显示出对牛奶蛋白质量定量的卓越能力.
- 这种方法为开发用于实时评估牛奶质量的智能设备提供了宝贵的参考.
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