An Adaptive Weighted Polynomial Baseline Correction Method for Electrochemical Aptamer-based Sensor

Abstract

A background signal, or baseline, is typical a low frequency signal that is composited with the target signal and commonly occurs in electrochemical biosensing data. Square wave voltammetry (SWV) has been widely used to acquire data for electrochemical aptamer-based (EAB) biosensors. However, one challenge with SWV is that the true baseline is unable to be assessed, but can only be estimated. The background signal of SWV usually contains various features, such as levels, trends, and shapes. These features are usually uninformative and, if unaccounted for, they may confuse the results of the analysis. Consequently, standardizing the signal by correcting the baseline is an essential step processing electrochemical sensing results. In this research, we present an adaptive polynomial baseline correction method for the baseline correction of SWV data from real E-AB biosensors. This method can automatically identify the uninformative regions in the signal and provide a robust mathematical equation to estimate the baseline. Employing real world sensing data, We compared our method with other published methods and showed that our method performs more reliably within acceptable errors. We also used the baseline-corrected E-AB biosensing data to develop a statistical model for predicting the concentration of cocaine and THC in saliva samples and developed a friendly user interface that enables front-end users to analyse the data without code interaction. This work shows potential to facilitate data automation to detect specific analytes for point-of-care (POC) applications.