Machine Learning Applied to Electrochemical Data Processing for Improved Analyte Quantification in Complex Saliva.

Biofluids that can be noninvasively and frequently collected, such as saliva, have great promise for real-time analyte monitoring at the point of care to inform on patient health. However, analyte quantification in these fluids can be challenging due to their complex composition that can reduce signal-to-noise ratio. In the context of electrochemical sensing in saliva, the complexity of saliva can result in signal interference through a high and variable background such that accurate and reproducible analyte quantification is challenging. Simple analysis algorithms that focus on a single peak feature may work well for analyte quantification in well-defined buffer backgrounds but may not be ideal for analyte quantification in complex biofluids. Motivated by this, for the task of quantifying drug levels in saliva from electrochemical voltammogram measurements, we assessed the performance of five different types of regression models: k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process (GP), and linear multivariate. We trained and tested the models on hundreds of voltammograms spanning five different analyte concentrations of the anti-seizure drug carbamazepine spiked into whole human saliva. For each regression model type, we performed feature selection from nine voltammogram features coupled with hyperparameter tuning, using a performance metric that combined coefficient of determination (