Electrochemical Impedance Spectroscopic Detection of E.coli with Machine Learning
Electrochemical Impedance Spectroscopic Detection of E.coli with Machine Learning
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利用机器学习对大肠杆菌进行电化学阻抗谱检测
DOI：
10.1149/1945-7111/ab732f
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发表时间：
2020-02-17
影响因子：
3.9
通讯作者：
Yu, Hui
中科院分区：
文献类型：
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作者：
Xu, Ying;Li, Chao;Yu, Hui
电化学阻抗谱（EIS）是一种用于公共卫生和安全的病原体生物传感检测的常用方法。在其最一般的形式中，电荷转移电阻的增加或界面上双层电容的减少用于报告病原体引起的EIS系统变化。然而，这种策略并不普遍适用于各种EIS传感器，并且可能导致不准确的检测。在此，我们展示了一种基于机器学习的EIS生物传感器，用于提高大肠杆菌检测的准确性。EIS数据是通过抗体结合固定大肠杆菌的金电极获得的，并采用Randles模型来推断多个阻抗参数。利用主成分分析和支持向量回归训练机器学习模型，自动建立多个阻抗参数与细菌浓度之间的定量关系。结果表明，测定细菌浓度的准确性得到了提高。改进是由于电容和电阻信息的集成。因此，这些结果为各种应用中的自动和准确的EIS生物传感器铺平了道路。
Electrochemical impedance spectroscopy (EIS) is a common method in biosensing detection of pathogens for public health and safety. In its most general form, increases of charge transfer resistance or decrease of double layer capacitance at the interface are used for reporting EIS system changes due to pathogens. However, this strategy is not universally adaptable to various EIS sensors and could lead to inaccurate detection. Herein, we demonstrated a machine learning-based EIS biosensor for E.coli detection with improved accuracy. EIS data was obtained from gold electrodes immobilized with E.coli through antibody binding and fitted with the Randles model to extrapolate multiple impedimetric parameters. A machine learning model, using principle component analysis and support vector regression, was trained to automatically establish a quantitative relationship between multiple impedimetric parameters and bacterial concentrations. Results showed an improved accuracy in determining bacterial concentration. The improvement is due to the integration of both capacitance and resistance information. These results thus pave the way for automatic and accurate EIS biosensors in various applications.