COLLABORATIVE RESEARCH: High-precision monitoring of foodborne pathogens in food manufacturing facilities

与食物有关的致病细菌微生物可引起各种对健康有重大影响的传染病。食品制造和加工设施是这些食源性病原体传播的丰富场所；因此，重要的是拥有有效的检测系统，可以用于快速监测食品制造设施中的食源性细菌病原体。目前可用于检测病原体的技术可能昂贵和/或需要很长时间才能取得结果，因此它们不适合在产品不断开发的快节奏食品制造场所的常规应用。需要新的和改进的技术来快速检测食源性病原体，以避免因食品召回而对健康造成的影响和经济损失。该项目将开发一种新技术，使用一种名为“适配子”的独特人造分子来准确检测食源性细菌病原体，该分子被设计为对所需病原体的真正测试，并以多种方式检测结果以提高精确度。这项技术将使用计算方法和移动设备，如智能手机，以实现快速检测和无线数据传输，以便远程访问结果。这项技术可以被开发来实时检测各种类型的食源性细菌病原体，这是朝着提高食品制造和加工设施的食品安全迈出的重要一步。该项目将为学生提供技术开发方面的实践培训，以培养美国劳动力的相关技能。该项目还将促进关于食品安全的K-12外联计划，并在妇女和代表性不足的少数民族中促进STEM(科学、技术、工程和数学)教育。食品行业需要能够轻松集成到制造过程系统中的“适合用途”的病原体检测技术，以快速、准确地实时识别和量化细菌病原体。利用一个跨学科的研究团队，将利用结构生物信息学、数据分析和基于适体的生物传感，拟议的项目将开发一种具有移动集成的无线多模式适配传感器，通过集成的电化学和光学监测精确检测食源性细菌病原体。该技术将提供以下性能特点：(I)高精度的流动监测方法；(Ii)可移动到不同加工地点的便携式设备；(Iii)在复杂的食品微环境中检测目标病原体的能力；(Iv)实时远程监测和集成到SCADA(监督控制和数据采集)系统的能力；以及(V)提高精度的多模式传感能力。这是对现有食品生产设施中使用的食源性病原体检测系统的重大改进。该奖项反映了NSF的法定使命，通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Disease-causing bacterial microorganisms associated with food can cause various infectious diseases with significant health impacts. Food manufacturing and processing facilities are a rich place for these foodborne pathogens to spread; thus, it is important to have efficient detection systems that can be used to quickly monitor foodborne bacterial pathogens in food manufacturing facilities. Current technologies that are available for detecting pathogens can be expensive and/or take a long time to get results, so they are not suitable for routine application in fast-paced food manufacturing sites where products are developed continuously. New and improved technologies are needed for rapid detection of foodborne pathogens in order to avoid health impacts and economic losses due to food product recalls. This project will develop a novel technology that will accurately detect foodborne bacterial pathogens using a unique artificial molecule called ‘aptamer’ that is designed to act as a true test for the desired pathogen with the result detected in multiple ways to enhance precision. The technology will use computational methods and mobile devices, such as smartphones, to enable rapid detection and wireless data transmission for remote access to the results. The technology can be developed to detect various types of foodborne bacterial pathogens in real-time, a significant step towards improving food safety in food manufacturing and processing facilities. The project will provide students with hands-on training in the development of the technology to build the related skillset in the United States workforce. The project will also facilitate K-12 outreach programs on food safety as well as promote STEM (Science, Technology, Engineering, and Math) education amongst women and underrepresented minorities.The food industry needs “fit-for-purpose” pathogen detection technologies that can be easily integrated into manufacturing process systems to rapidly deliver precise identification and quantification of bacterial pathogens in real-time. Leveraging an interdisciplinary research team that will exploit structural bioinformatics, data analytics, and aptamer-based biosensing, the proposed project will develop a wireless multimodal aptasensor with mobile integration to precisely detect foodborne bacterial pathogens via an integrated electrochemical and optical monitoring. The technology will deliver the following performance characteristics: (i) a high-precision method for flow-through monitoring; (ii) a portable device that can be moved to different process locations; (iii) an ability to detect target pathogens in complex food micro environments; (iv) capacity for real-time remote monitoring and integration into SCADA (supervisory control and data acquisition) systems; and (v) multimodal sensing capability to enhance precision. These represent significant improvements over existing foodborne pathogen detection systems used in food manufacturing facilities.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.