电化学传感器检测牛奶中的抗生素,基于机器学习算法
Timur A Aliev1, Vadim E Belyaev1, Anastasiya V Pomytkina1
1ITMO University, Lomonosova strasse 9, Saint-Petersburg 191002, Russia.
ACS applied materials & interfaces
|October 24, 2023
概括
这项研究开发了一种新的电化学传感器和机器学习模型,用于检测牛奶中的抗生素残留物. 该系统实现了高精度,为乳制品农场监测提供了潜在的解决方案.
科学领域:
- 分析化学 分析化学
- 电化学 电化学 电化学
- 机器学习 机器学习
背景情况:
- 对牛奶等复杂混合物的电化学分析具有挑战性.
- 检测牛奶中的抗生素残留物对于食品安全和公共卫生至关重要.
研究的目的:
- 开发一种模式识别系统,用于检测脱脂牛奶中的抗生素残留物.
- 用一种新型传感器研究抗生素的电化学行为.
主要方法:
- 制造一个多电极传感器 (铜,,碳纤维).
- 使用循环电压测量用于电化学数据收集.
- 使用机器学习 (渐变增强) 来进行模式识别.
- 分子对接和密度函数理论建模用于模拟.
主要成果:
- 梯度增强算法在模型训练中表现出高效率.
- 开发的系统在识别牛奶中的抗生素残留物方面取得了很高的准确性.
- 分析了电极表面过程,表明与金属离子的复杂化有助于抗生素指纹.
结论:
- 循环电压测量和机器学习的结合方法在乳中检测抗生素残留物方面是有效的.
- 该方法对集成到乳制品农场系统进行实时监测具有前景.
- 这种技术为确保牛奶质量和安全提供了可靠的工具.
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