Antimicrobial resistance (AMR) of foodborne pathogens is a global crisis in public health and economic growth. A real-time surveillance system is key to track the emergence of AMR bacteria and provides a comprehensive AMR trend from farm to fork. However, current AMR surveillance systems, which integrate results from multiple laboratories using the conventional broth microdilution method, are labor-intensive and time-consuming. To address these challenges, we present the internet of things (IoT), including colorimetric-based microfluidic sensors, a custom-built portable incubator, and machine learning algorithms, to monitor AMR trends in real time. As a top priority microbe that poses risks to human health, Campylobacter was selected as a bacterial model to demonstrate and validate the IoT-assisted AMR surveillance. Image classification with convolution neural network ResNet50 on the colorimetric sensors achieved an accuracy of 99.5% in classifying bacterial growth/inhibition patterns. The IoT was used to carry out a small-scale survey study, identifying eight Campylobacter isolates out of 35 chicken samples. A 96% agreement on Campylobacter AMR profiles was achieved between the results from the IoT and the conventional broth microdilution method. The data collected from the intelligent sensors were transmitted from local computers to a cloud server, facilitating real-time data collection and integration. A web browser was developed to demonstrate the spatial and temporal AMR trends to end-users. This rapid, cost-effective, and portable approach is able to monitor, assess, and mitigate the burden of bacterial AMR in the agri-food chain.
Keywords: antimicrobial resistance; foodborne pathogen; internet of things; lab on a chip; machine learning; surveillance.