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的食源性疾病的主要原因之一，给较低的社会经济地位和代表性不足的种族/种族群体带来了更高的负担。仅在美国，由于沙门氏菌污染而导致的疾病总成本估计在2018年大于106.9亿美元。该项目的目的是调查多种转化性传感技术，用于检测沿着家禽供应链的沙门氏菌污染，从而导致数据驱动的决策系统的开发，以提高食品安全，安全性，平等性，等级，效率，效率，效率，效率。通过与家禽行业，零售市场，食品银行和当地卫生部门建立多部门的合作伙伴关系，该项目汇集了五个机构的多学科研究人员小组，以调查和实施综合的传感器食品供应链决策决策系统，以进行风险评估，以降低风险评估，以实现System Fore Corecorpile Corepleable Foodsabil Foodsable Health utearsyscomes。该技术有可能适应牛肉，猪肉，乳制品和绿叶产品中其他食源性病原体的检测。它也可以应用于诊断临床环境中细菌和病毒感染性疾病的诊断。拟议技术的应用将确保对本地和全球消费者的公平粮食安全，并减少粮食生物疾病的经济负担，尤其是面临较高粮食安全风险的脆弱和弱势群体。研究团队将与多个部门合作伙伴一起工作，以满足食品营养，可及性和权益中处境不利的人群的独特需求。该项目将为学生创造研究和培训机会，以了解食品科学，公共卫生，动物科学，数据科学和传感技术的交汇处的融合科学方法。该团队将通过为学生研究经验提供机会，吸引研究人员，与行业劳动力合作（例如，包括移民工人）和多部门利益相关者合作，扩大与代表性不足的人群的参与，并将有关较低的群体的数据合并到拟议的系统中。在30分钟的测定时间内以低浓度的沙门氏菌血清射手。这可以通过在侧面抛光多模光纤芯上开发表面增强的拉曼光谱（SERS）传感器来实现，该光纤芯芯被以15度的无角度整合到3维的印刷微结构中，以最大程度地利用nanoantenna阵列来创建激发激光器与分析的相互作用。通过测量其振动指纹SERS光谱，将检测和量化沙门氏菌抗原。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个创新特征，以将病毒抗原样品集中到可检测的阈值，捕获和检测病原体，并使用涂有特定抗体的电极阵列启用同时并选择性地检测咸浆液的抗体。通过快速筛选一组单个或多核分布的单核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸 - 核苷酸血清型标记，而不是及时且昂贵的全基因组测序，而是纳米相关的多层次检查点测序传感器可​​以区分沙门氏菌血清射手。通过结合整个端到端食品供应链中样品的结果并整合了国家人口水平的数据，该系统将填充集中的数据环境，以开发可视化，预测和微生物风险评估的优化能力，并有效，及时的数据驱动的决策支持NSF的法定任务和审查范围，这是通过评估的范围来表达的，这表明了这一奖项的范围。