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

NSF 融合加速器轨道 J：缓解食品供应链威胁的快速检测技术和决策支持系统

• 批准号: 2236622
• 负责人: Mahmoud Almasri
• 金额: $ 75万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236622&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Rapid

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.

沙门氏菌是美国和世界各地食源性疾病的主要原因之一，给社会经济地位较低和代表性不足的种族/族裔群体带来了更大的负担。 2018 年，仅美国沙门氏菌污染造成的疾病总成本估计就超过 106.9 亿美元。该项目的目标是研究多种变革性传感技术，用于检测家禽供应链中的沙门氏菌污染，从而促进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、食品银行和当地卫生部门建立多部门合作伙伴关系，该项目汇集了五个机构的多学科研究人员小组，研究和实施集成传感器支持的食品供应链决策支持系统进行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。该技术有潜力适用于检测牛肉、猪肉、乳制品和绿叶产品中的其他食源性病原体。它还可用于诊断临床环境中的细菌和病毒传染病。所提出的技术的应用将确保当地和全球消费者公平的粮食安全，并减少食源性疾病的经济负担，特别是对于面临的弱势和贫困人群粮食安全风险较高。研究团队将与多部门合作伙伴合作，解决弱势群体在食品营养、可及性和公平方面的独特需求。该项目将为学生创造研究和培训机会，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，以及将有关代表性不足群体的数据纳入拟议系统，扩大与代表性不足群体的接触。所提出的传感技术在多重/同时、定量和选择性检测以及 30 分钟测定时间内低浓度沙门氏菌血清型监测方面是独一无二的。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 3 维印刷微结构中，以最大限度地提高激发激光与激光的相互作用。分析物，而纳米天线阵列将使用低成本微球光刻技术创建。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，从而实现同时和选择性检测沙门氏菌血清型。纳米孔促进的多位点检查点测序传感器不是及时且昂贵的全基因组测序，而是通过快速筛选分布在一个或多位点的一组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数据，该系统将填充一个集中的数据环境，以开发可视化、预测和优化能力，以有效和有效地评估和缓解微生物风险。及时的数据驱动决策支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。