| NSF Org: |
ITE Innovation and Technology Ecosystems |
| Recipient: |
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| Initial Amendment Date: | December 9, 2022 |
| Latest Amendment Date: | December 9, 2022 |
| Award Number: | 2236622 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Michael Reksulak
mreksula@nsf.gov (703)292-8326 ITE Innovation and Technology Ecosystems TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | December 15, 2022 |
| End Date: | November 30, 2024 (Estimated) |
| Total Intended Award Amount: | $750,000.00 |
| Total Awarded Amount to Date: | $750,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
121 UNIVERSITY HALL COLUMBIA MO US 65211-3020 (573)882-7560 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
316 UNIVERSITY HALL COLUMBIA MO US 65211-3020 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Convergence Accelerator Resrch |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
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 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, 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 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 food security for local and global consumers and reduce the economic burden of foodborne diseases, especially for vulnerable 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 and accessibility. 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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The NSF Convergence Accelerator Phase I SENS-D project, titled "Rapid Detection Technologies and Decision-Support Systems to Mitigate Food Supply Chain Threats," successfully advanced sensor-based detection and data-driven decision-making in food safety. This initiative aimed to address critical challenges in pathogen detection and supply chain management by developing innovative sensor technologies and a robust decision-support system. Through extensive interdisciplinary collaboration, this project laid the groundwork for transforming how food safety risks are identified, mitigated, and managed at multiple levels of the poultry supply chain.
One of the most significant achievements of Phase I was the development and validation of three novel sensor technologies designed for rapid and accurate detection of Salmonella in poultry products. The impedance-based biosensor was designed to offer high-sensitivity pathogen detection by capturing and quantifying impedance changes associated with bacterial binding. The fiber optics-based Surface Enhanced Raman Spectroscopy (SERS) sensor provided ultra-sensitive pathogen identification by detecting unique molecular signatures, allowing for real-time analysis with minimal sample preparation. Meanwhile, the nanopore checkpoint sequencing sensor was developed to enable real-time, on-site differentiation of Salmonella serovars, offering a highly efficient molecular tracking tool. These technologies underwent rigorous validation, with results confirming their ability to outperform traditional microbiological methods in terms of speed, accuracy, and sensitivity.
To complement these sensor technologies, the project also developed a data-driven decision-support system (DSS) that integrates real-time sensor data into an advanced analytics framework. The DSS was designed with a One Health approach, incorporating key factors such as food access, transportation logistics, and food security vulnerabilities to improve risk assessment and response strategies. The system was refined through a human-centered design process, involving extensive engagement with stakeholders across the food supply chain, including poultry processors, food distributors, retailers, and public health agencies. These engagements helped shape the system’s functionalities, ensuring its usability in diverse real-world scenarios. The DSS also includes predictive modeling capabilities, allowing stakeholders to anticipate contamination risks, optimize intervention strategies, and improve food safety decision-making.
A strong focus on workforce development and inclusion was embedded throughout the project. The research team was composed of diverse professionals, with 57% of its members representing underrepresented racial and ethnic groups, and 33% of the investigators being women. In addition, the project provided training opportunities for seven students—one undergraduate, four master’s, and two Ph.D. candidates—most of whom identified with at least one underrepresented group in STEM. To further expand educational outreach, an upcoming workshop on Convergence Science is planned at Lincoln University, a Historically Black College and University (HBCU), to engage undergraduate students in interdisciplinary research and technological innovation related to food safety and supply chain management.
Industry and community partnerships played a crucial role in the success of Phase I, with several major stakeholders actively participating in the project. Formal collaborations were established with leading industry players, including Cargill, Wayne-Sanderson Farms, and LTI, all of whom have committed to adopting the developed technologies in Phase II. Additionally, the USDA Food Safety and Inspection Service (FSIS) contributed 15 Salmonella serotypes to support the development of a spectral library, which will be used to train machine learning models for automated pathogen identification. Beyond industry partnerships, the project also garnered attention from a venture capital firm, which has expressed strong interest in investing in the commercialization of these technologies, contingent on continued advancements in Phase II.
Public outreach and dissemination efforts were highly successful, leading to broad media engagement and increasing public awareness about the significance of rapid pathogen detection. The project was featured on National Public Radio’s Marketplace, reaching an audience of 11.7 million weekly listeners. Additional coverage in over 20 national and regional media outlets, including Food Safety Magazine and Missouri Public Radio, further amplified the project’s impact. These media engagements highlighted the critical need for innovative food safety solutions and reinforced the project’s contributions to enhancing public health and supply chain resilience.
Looking ahead, Phase II will build upon these accomplishments by focusing on optimizing sensor deployment across the poultry supply chain, refining the DSS to enhance its predictive and real-time decision-making capabilities, and expanding industry adoption. Collaborations with policymakers will be strengthened to facilitate the integration of these rapid detection technologies into existing food safety regulations and standards. Additionally, efforts will be made to explore commercialization pathways to scale up production and implementation of the developed technologies. The team also plans to investigate broader applications of the sensor and DSS framework beyond poultry, potentially extending these innovations to other areas of food safety and supply chain management.
In conclusion, Phase I of the NSF Convergence Accelerator SENS-D project successfully demonstrated the feasibility and impact of integrating cutting-edge sensor technologies with advanced data analytics to revolutionize food safety practices. Through interdisciplinary collaboration, strong industry partnerships, and a commitment to inclusivity and workforce development, the project has set a solid foundation for Phase II and beyond.
Last Modified: 04/14/2025
Modified by: Mahmoud F Almasri
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