Electrochemical Impedance Spectroscopic Detection of E.coli with Machine Learning

Abstract

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.

Electrochemical Immunosensor for Detection of H. pylori Secretory Protein VacA on g-C3N4/ZnO Nanocomposite-Modified Au Electrode

“…The EIS technique is also important to determine the resistance on the electrode material and also between the electrode and the electrolyte. 49 The R ct value (charge-transfer resistance) of the nanocomposite-decorated Au-ET interface was monitored. In Figure 5 B, impedance curves are reported for bare Au-ET, g-C 3 N 4 /ZnO/Au-ET, and VacA Abs@g-C 3 N 4 /ZnO/Au-ET in 0.1 M potassium ferricyanide and potassium ferrocyanide electrolyte solution.…”

The Two Weapons against Bacterial Biofilms: Detection and Treatment

“…For biofilm detection, the use of machine learning models was already reported, e.g., detection of E. coli biofilm using an electro-chemical impedance spectroscopy (EIS)-based biosensor [ 82 ]. Machine learning systems, for instance, can be trained to recognize multiple impedimetric parameters and determine bacteria concentration.…”

“…Machine learning systems, for instance, can be trained to recognize multiple impedimetric parameters and determine bacteria concentration. The conjugation of machine learning systems with EIS already showed promising results, even with thicker biofilm [ 82 ].…”

Review—Lab-in-a-Mouth and Advanced Point-of-Care Sensing Systems: Detecting Bioinformation from the Oral Cavity and Saliva

“…The increased number of sensors or signals results in high data throughputs, representing a challenge to effectively process such a huge amount of sensory information. 211 Hence, we need machine learning-a recent technology in artificial intelligence (AI). Machine learning involves computer algorithms that can improve themselves automatically through experience and training data (i.e., a built model from sample data by the machine learning algorithms).…”