机器学习辅助区分和检测使用多色微流体化学发光检测芯片的抗生素
1Anhui Province Key Laboratory of Advanced Catalytic Materials and Reaction Engineering, School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei, Anhui, 230009, People's Republic of China.
Talanta
|December 3, 2023
概括
本研究介绍了一种机器学习增强的微流体芯片,用于使用多色化学发光来检测多种抗生素. 该芯片成功地识别和量化了抗生素,为环境和食品安全分析提供了一种新的方法.
科学领域:
- 分析化学 分析化学
- 生物传感技术的技术
- 机器学习应用 机器学习应用
背景情况:
- 使用多色化学发光 (CL) 检测多种分析物,特别是抗生素,存在重大挑战.
- 现有的方法往往缺乏复杂样本矩阵所需的灵敏度和特异性.
研究的目的:
- 开发一种机器学习辅助的多色微流体CL检测芯片,用于识别和预测抗生素度.
- 创建一个强大的传感策略来区分和量化多种抗生素分析物.
主要方法:
- 用CL催化剂和不同的CL试剂 (明醇,明醇-光素,明醇-素B) 修改的三通道微流体芯片的制造.
- 产生对各种抗生素敏感的多色CL排放 (蓝色,绿色,粉红-紫色).
- 提取R,G,B和灰度值以形成多色CL响应数据库.
- 机器学习算法的应用 (PCA,HCA,DNN) 用于抗生素的分类和量化.
主要成果:
- 对于不同的抗生素,观察到明显的类似指纹的CL反应模式.
- 使用PCA和HCA成功对五种抗生素进行分类.
- 使用DNN精确量化抗生素,包括识别二进制混合物和增值样本.
- 对水样中的抗生素度预测的结果令人满意.
结论:
- 开发的机器学习辅助多色微流体CL芯片为抗生素检测提供了一个简单但有效的策略.
- 这种方法显示出在复杂样本中区分和量化多种分析物的高潜力.
- 这项研究为环境监测和食品安全领域的先进传感平台铺平了道路.
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