Engineers improve electrochemical sensing by Incorporating Machine Learning

Another sensing them,  note integration with machine learning. 

Engineers improve electrochemical sensing by incorporating machine learning  by Mary Fetzer, Pennsylvania State University

Graphical abstract. Credit: Analytica Chimica Acta (2022). DOI: 10.1016/j.aca.2022.340447

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.

The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems.

In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine—two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders—in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.

Many biomarkers have similar molecular structures or overlapping electrochemical signatures, making it difficult to detect them simultaneously. Leveraging machine learning for measuring multiple biomarkers can improve the accuracy and reliability of diagnostics and as a result improve patient outcomes, according to the researchers. Further, sensing using the same device saves resources and biological sample volumes needed for tests, which is critical with clinical samples with scarce amounts.

"We developed a new approach to improve the performance of electrochemical biosensors by combining machine learning with multimodal measurement," said Aida Ebrahimi, Thomas and Sheila Roell Early Career Assistant Professor of Electrical Engineering and assistant professor of biomedical engineering.

"Using our optimized machine learning architecture, we could detect biomolecules in amounts 100 times lower than what conventional sensing methods can do."

The researchers' methodology features a hardware/software system that enables them to automatically gather and process information based on a machine learning model that is trained to identify biomolecules in biological fluids such as saliva and sweat, which are common choices for noninvasive health monitoring.

"The machine learning-powered electrochemical diagnostic approach presented in this paper may find broader application in multiplexed biochemical sensing," said Vinay Kammarchedu, 2022-23 Milton and Albertha Langdon Memorial Graduate Fellow in Electrical Engineering at Penn State and first author on the paper.  .... '