Presentation Title

P-10 Machine Learning Assisted ECL Sensor Analysis

Presenter Status

Chair and Professor of Dept. of Engineering

Second Presenter Status

Associate professor, Dept. of Computing

Third Presenter Status

Visiting scholar, Dept. of Andrews University

Fourth Presenter Status

Student, Dept. of Computing

Fifth Presenter Status

Student, Dept. of theology

Sixth Presenter Status

Professor, Dept. of Publich health

Preferred Session

Poster Session

Start Date

25-10-2019 2:00 PM

Presentation Abstract

Current biosensor data analysis is based on calibration curves, which are generated based on a single key feature of the sensor signal, while other features in the biosensor are generally ignored. Inclusion of these ignored features allows us to make more informed predictions regarding unknown concentrations. In this work, a multimodal electrochemiluminescent (ECL) sensor was explored to improve on the data analysis using machine learning (ML) techniques. The multimodal ECL sensor data was collected for light intensity and electrochemical data in the Ru(bpy)2(3+)/tri-n-propylamine system, and a disposable electrode was used as a sensor unit. The results from ML based analysis demonstrate the potential to replace the traditional calibration curve based method and have potential to improve development of low-cost point-of-care diagnostic devices.

Acknowledgments

This research was funded by the National Science Foundation (NSF) (Grant number 1706597).

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Oct 25th, 2:00 PM

P-10 Machine Learning Assisted ECL Sensor Analysis

Current biosensor data analysis is based on calibration curves, which are generated based on a single key feature of the sensor signal, while other features in the biosensor are generally ignored. Inclusion of these ignored features allows us to make more informed predictions regarding unknown concentrations. In this work, a multimodal electrochemiluminescent (ECL) sensor was explored to improve on the data analysis using machine learning (ML) techniques. The multimodal ECL sensor data was collected for light intensity and electrochemical data in the Ru(bpy)2(3+)/tri-n-propylamine system, and a disposable electrode was used as a sensor unit. The results from ML based analysis demonstrate the potential to replace the traditional calibration curve based method and have potential to improve development of low-cost point-of-care diagnostic devices.