Foodborne illness is a common, distressing, and even life-threatening problem for millions of people around the world. Food testing is becoming a common need for our daily lives, i.e., a sensitive and cost-effective detector can guarantee the safety and quality of food to fulfill strict food legislation and consumer demands. However, current electrochemical sensors not only require lengthy and complex sample processing, but also need expensive equipment for an adequate level of purification and enrichment. Meanwhile, low-cost sensors produce noisy signals that require computationally expensive machine-learning models for noise-robust processing. To remedy these issues, this research project is to develop an intelligent food-pathogen detection system, which is portable and affordable, yet accurate. The success of this project will enable affordable, portable and accurate food-borne pathogen detection, including food analysis, food safety, food-borne illness detection and therapy, water measurement, and many microbiome-related environmental monitoring. This project will provide an intellectual foundation for portable, disposable, intelligent foodborne pathogen detection expanding the burgeoning biosensor, machine learning and hardware accelerator design research community.
Specifically, this project aims to develop (1) a portable electrochemical sensing system detecting foodborne pathogens in contaminated foods, (2) hardware-friendly bitwise machine learning models for de-noising and recognition of real-samples and (3) an ultra-low power and portable accelerator for bit-wise machine learning inferences. The three aims are to improve the recognition performance in a collaborative fashion, where affordable sensing technology is backed up by machine-learning-based signal processing, while the hardware-algorithm co-design controls system-level efficiency.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
