使用拉曼光谱学量化检测米油和玉米油伪造的米油产品:使用机器学习算法和化学算法模型之间的比较研究
Henan Liu1, Sijia Ma1, Ni Liang1
1School of Physical Science and Technology, Tiangong University, Tianjin 300387, China.
Foods (Basel, Switzerland)
|January 8, 2025
概括
准确检测驼花油改是非常重要的. 这项研究比较了各种模型,发现虽然一些方法对单个 adulterants 起到很好的作用,但复杂的混合物需要先进的技术,如 PLSR 与 CARS-ICA 以获得可靠的结果.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 驼花油的定量检测对于质量控制和真实性至关重要.
- 与类似的油 (如大米油和玉米油) 混合,由于光谱相似性,会带来挑战.
研究的目的:
- 评估不同特征提取方法和回归算法的有效性,以检测驼花油改.
- 为了比较反向传播神经网络 (BPNN) 与随机森林 (RF) 和部分最小平方回归 (PLSR) 的性能.
主要方法:
- 利用独立组件分析 (ICA) 和竞争性自适应重权采样 (CARS) 进行光谱特征提取.
- 开发了使用BPNN,RF和PLSR的回归模型来预测石油度.
- 测试的模型是用驼花油与大米油混合,以及大米和玉米油混合.
主要成果:
- 对于用大米油 adulterated 驼油,ICA-BPNN 和 ICA-PLSR 模型显示了令人满意的性能.
- 当米和玉米油都存在时,BPNN模型的表现不佳.
- 在复杂的伪造场景中,PLSR与CARS-ICA的组合产生了最好的预测准确度.
结论:
- 选择特征提取方法和回归算法的选择显著影响了对驼花油改量的定量检测的准确性.
- 像PLSR这样的先进方法与CARS-ICA相结合,对于复杂的伪造案件的可靠检测至关重要.
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