(69b) Machine Learning-Aided Graphene Chemisensors for Food Safety Monitoring

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications due to their remarkable precision and sensitivity. ISFETs operate by converting changes in the concentration/composition of chemical solutions into electrical signals, making them ideal for environmental monitoring, healthcare diagnostics, and industrial process control. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance. In this study, we illustrate the advantages of incorporating machine learning (ML) algorithms to construct predictive models using the extensive datasets generated by ISFET sensors. This integration enables us to extract intricate patterns that surpass what can be derived solely from human expertise. Furthermore, it addresses common challenges, including cycle-to-cycle, sensor-to-sensor, and chip-to-chip variations, thereby alleviating the manufacturing complexities associated with achieving flawless sensors. As a result, this paves the way for the broader adoption of ISFETs in commercial applications. Specifically, we utilize non-functionalized graphene-based chemitransistor arrays to train artificial neural networks (ANNs) with a remarkable ability to discern instances of food fraud, food spoilage, and food safety concerns. We anticipate that the fusion of compact, energy-efficient, and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.