Machine learning-enabled colorimetric sensors for foodborne pathogen detection.

Abstract

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.