NSF Convergence Accelerator Track J: Rapid detection technologies and decision-support systems to mitigate food supply chain threats

沙门氏菌是美国和世界各地食源性疾病的主要原因之一，对社会经济地位较低和代表性不足的种族/民族群体造成了更高的负担。据估计，2018年，仅在美国，沙门氏菌污染导致的疾病总成本就超过106.9亿美元。该项目的目标是研究用于检测家禽供应链中沙门氏菌污染的多种变革性传感技术，从而开发数据驱动的决策支持系统，以提高食品安全、保障、公平、效率和复原力。通过与家禽业、零售市场、食品银行和地方卫生部门建立多部门合作伙伴关系，该项目将五个机构的多学科研究人员聚集在一起，调查和实施一个综合传感器食品供应链决策支持系统，用于风险评估和沙门氏菌缓解，以实现全系统公平的食品安全和更好的健康结果。这项技术有潜力用于检测牛肉、猪肉、乳制品和绿叶产品中的其他食源性病原体。它也可以应用于诊断细菌和病毒传染病在临床设置。拟议技术的应用将确保当地和全球消费者的公平粮食安全，并减轻食源性疾病的经济负担，特别是对面临更高粮食安全风险的脆弱和贫困人口。该研究小组将与多部门合作伙伴共同努力，解决弱势群体在粮食营养、可及性和公平性方面的独特需求。该项目将为学生提供研究和培训机会，让他们了解食品科学、公共卫生、动物科学、数据科学和传感技术交叉领域的融合科学方法。该团队将通过为学生提供研究经验的机会、吸引研究人员、与行业劳动力（例如，包括移民工人）和多部门利益相关者合作，并将有关代表性不足群体的数据纳入拟议的系统，扩大与代表性不足人群的接触。所提出的传感技术在多重/同时，定量和选择性检测方面是独一无二的，并且在30分钟的分析时间内对低浓度的沙门氏菌血清型进行监测。这可以通过在侧面抛光的多模光纤芯上开发表面增强拉曼光谱（SERS）传感器来实现，该传感器以15度角集成到三维印刷微观结构中，以最大限度地提高激发激光与分析物的相互作用，而纳米天线阵列将使用低成本的微球光刻技术来创建。沙门氏菌抗原将通过测量其振动指纹SERS光谱进行检测和定量。该项目还将在同一芯片上集成基于阻抗的生物传感器的多种创新功能，将病毒抗原样本浓缩到可检测的阈值，使用涂有特定抗体的电极阵列捕获和检测病原体，从而能够同时和选择性地检测沙门氏菌血清型。代替及时和昂贵的全基因组测序，纳米孔促进的多位点检查点测序传感器通过快速筛选分布在一个或多个位点的单核苷酸变异血清分型标记来区分沙门氏菌血清型。通过结合整个端到端食品供应链的样本结果，并整合国家人口层面的数据，该系统将建立一个集中的数据环境，为微生物风险评估和缓解提供可视化、预测和优化能力，并提供有效和及时的数据驱动决策支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Salmonella is one of the leading causes of foodborne illness in the U.S. and around the world, placing a higher burden on populations of lower socioeconomic status and underrepresented racial/ethnic groups. The total cost of illnesses due to Salmonella contamination in the U.S. alone was estimated to be greater than $10.69 billion in 2018. The goal of this project is to investigate multiple transformative sensing technologies for detecting Salmonella contamination along the poultry supply chain, leading to the development of a data-driven decision-support system to improve food safety, security, equity, efficiency, and resilience. By developing multi-sectoral partnerships with the poultry industry, retail markets, food banks, and local health departments, this project brings together a multidisciplinary group of researchers across five institutions to investigate and implement an integrated sensor-enabled food supply chain decision-support system for risk assessment and Salmonella mitigation to achieve system-wide equitable food safety and better health outcomes. This technology has the potential to be adapted for the detection of other foodborne pathogens in beef, pork, dairy, and green leaf products. It may also be applied to diagnose bacterial and viral infectious diseases in clinical settings.The application of the proposed technology will ensure equitable food security for local and global consumers and reduce the economic burden of foodborne diseases, especially for vulnerable and underprivileged populations who are facing higher food security risks. The research team will work alongside multisectoral partners to address the unique needs of disadvantaged populations in food nutrition, accessibility, and equity. This project will create research and training opportunities for students to learn about the convergence science approaches at the intersection of food science, public health, animal sciences, data science, and sensing technology. The team will expand engagement with under-represented populations by providing opportunities for student research experiences, engaging researchers, partnering with the industry workforce (e.g., including immigrant workers) and multi-sectoral stakeholders, and incorporating data about underrepresented groups into the proposed system.The proposed sensing technologies are unique in terms of multiplex/simultaneous, quantitative, and selective detection, and surveillance of Salmonella serovars at low concentrations within 30 minutes assay time. This can be accomplished by developing a Surface Enhanced Raman Spectroscopy (SERS) sensor on a side polished multimode optical fiber core, which is integrated into a 3-dimensional printed microstructure at a 15-degree angle to maximize the interaction of the excitation laser with the analytes, while the nanoantenna arrays will be created using low-cost microsphere photolithography. Salmonella antigens will be detected and quantified by measuring their vibrational fingerprint SERS spectra. The project will also integrate multiple innovative features of an impedance-based biosensor on the same chip to concentrate the viral antigen sample to a detectable threshold, capture, and detect the pathogens using arrays of electrodes coated with specific antibodies to enable simultaneous and selective detection of Salmonella serovars. Instead of timely and costly whole-genome sequencing, the nanopore-facilitated, multi-locus checkpoint sequencing sensor differentiates Salmonella serovars by rapid screening a panel of single-nucleotide-variation serotyping markers distributed in one or multi-locus. By combining results from samples throughout the end-to-end food supply chain and integrating the national population-level data, the system will populate a centralized data environment to develop visualization, prediction, and optimization capabilities for microbial risk assessment and mitigation with effective and timely data-driven decision support.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.