Machine Learning Analysis for Phenolic Compound Monitoring Using a Mobile Phone-Based ECL Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 18; p. 6004
• Main Authors: Taylor, Joseph, Ccopa-Rivera, Elmer, Kim, Solomon, Campbell, Reise, Summerscales, Rodney, Kwon, Hyun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 08.09.2021; MDPI
• Subjects: Acids; Algorithms; Biosensors; Cameras; Cellular telephones; Data analysis; Datasets; ECL; Electrodes; Experiments; Light; low-cost sensor; mobile phone-based sensor; Partial differential equations; Sensors; Time series
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21186004

Machine learning (ML) can be an appropriate approach to overcoming common problems associated with sensors for low-cost, point-of-care diagnostics, such as non-linearity, multidimensionality, sensor-to-sensor variations, presence of anomalies, and ambiguity in key features. This study proposes a novel approach based on ML algorithms (neural nets, Gaussian Process Regression, among others) to model the electrochemiluminescence (ECL) quenching mechanism of the [Ru(bpy)3]2+/TPrA system by phenolic compounds, thus allowing their detection and quantification. The relationships between the concentration of phenolic compounds and their effect on the ECL intensity and current data measured using a mobile phone-based ECL sensor is investigated. The ML regression tasks with a tri-layer neural net using minimally processed time series data showed better or comparable detection performance compared to the performance using extracted key features without extra preprocessing. Combined multimodal characteristics produced an 80% more enhanced performance with multilayer neural net algorithms than a single feature based-regression analysis. The results demonstrated that the ML could provide a robust analysis framework for sensor data with noises and variability. It demonstrates that ML strategies can play a crucial role in chemical or biosensor data analysis, providing a robust model by maximizing all the obtained information and integrating nonlinearity and sensor-to-sensor variations.