New Zealand Statistical Association 2024 Conference

Zhijian Wen

ESR

An adaptive polynomial baseline correction method in voltammetry with application to the prediction of the concentration of cocaine aptamer

This is joint work with Janet Stacey, Yasmin Liu

A background signal, or baseline, is a typical low frequency that is composited with the target signal that commonly occurs in electrochemical biosensing data. The background signal 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. Therefore, a correction for baseline is an essential step for analyzing the sensing results. In this project, we will present an adaptive polynomial baseline correction method for the baseline correction of SWV-based electrochemical aptasensors. This method can automatically identify the uninformative regions in the signal and provide a robust mathematical equation to estimate the baseline. We compared our method with other published methods using our aptasensor data. The result shows that our method performs more reliably with acceptable errors. We also used the baseline-corrected aptamer data to develop a statistical model for predicting the cocaine concentrations in saliva. This model shows huge potential to facilitate data automation to detect specific analytes for point-of-care applications.