Improving the Selectivity in Electrochemical Detection of Chloramphenicol Against Metronidazole With Machine Learning

In electrochemical sensing, the selectivity could be low if the analyte and interference species share similar redox potentials. This is a limiting factor in electrochemical detection of the broad-spectrum antibiotic chloramphenicol (CAP) against metronidazole (MNZ), which is commonly applied in conjunction. Here, we present machine learning-assisted detection of CAP with the presence of high-concentration MNZ interference. From data collected with cyclic voltammetry (CV), differential pulse voltammetry (DPV), and chronoamperometry (CA), we quantified the features in electrochemical profiles and verified their correlation with CAP concentrations by the Pearson correlation. Using correlated features, an artificial neural network model was trained to accurately predict the concentration of CAP. The results show that the interference of MNZ could be minimized with the presented machine learning-assisted electrochemical sensing scheme, and the CAP concentration could be accurately determined in buffer as well as in unmodified complex samples. We anticipate that the machine learning-assisted electrochemical detection scheme could contribute to the improvement of selectivity of various electrochemical sensors.

Automated summary

In electrochemical sensing, the low selectivity caused by similar redox potentials of analyte and interference species is a limiting factor in the detection of broad-spectrum antibiotic chloramphenicol (CAP) against metronidazole (MNZ). In this study, a machine learning-assisted detection method for CAP was developed in the presence of high-concentration MNZ interference. By quantifying features in electrochemical profiles and training an artificial neural network model using correlated features, the CAP concentration could be accurately predicted. The results demonstrate that this machine learning-assisted electrochemical sensing scheme can minimize the interference of MNZ and accurately determine CAP concentration in various samples.