Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, United States.
Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, United States.
Adv Food Nutr Res. 2024;111:179-213. doi: 10.1016/bs.afnr.2024.06.004. Epub 2024 Jun 29.
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.
在过去的十年中，比色传感器取得了各种进展，以提高其在食品和农业领域的潜在应用。其中一个越来越受到关注的应用是检测食源性病原体。在食品工业中进行感测有一些独特的考虑因素，包括食品样本破坏、复杂食品基质中的特异性以及高灵敏度要求。将纳米技术、微流控和智能手机应用程序开发等新技术纳入比色传感方法可以提高传感器的性能。然而，将传感器与现有的食品安全基础设施集成仍然存在挑战。最近，越来越先进的机器学习技术被用于促进非破坏性、多重检测，以实现传感器在食品工业中的可行应用。机器学习具有从高度复杂的数据中进行分析和预测的能力，因此在实现先进但实用的食源性病原体比色感测方面具有潜力。本文总结了机器学习支持的比色食源性病原体感测的最新进展和障碍。这些进展突显了跨学科、前沿技术在提供更安全、更高效的食品系统方面的潜力。
