Location: Foodborne Toxin Detection and Prevention Research
Project Number: 2030-42000-053-015-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 15, 2026
End Date: Sep 14, 2029
Objective:
The goal of this project is to develop and validate a compact, high-speed, metasurface-enhanced infrared (IR) spectroscopic platform for rapid screening of USDA-regulated foods for pathogenic bacteria and associated metabolic products or toxins. Conventional detection methods (culture, PCR, immunoassays) are widely used with high sensitivity, but they typically require lengthy processing times, costly reagents, and centralized laboratory facilities. IR spectroscopy offers a rapid, non-invasive, reagent-free alternative. However, existing systems suffer from low sensitivity, bulky instrumentation, and slow acquisition rates.
The platform proposed here aims to overcome these limitations by exploiting metasurface-enhanced light–matter interactions and chip-integrated detector architectures to achieve high-sensitivity, single-shot, multi-analyte detection. The approach targets biochemical and metabolic signatures associated with bacterial growth, which become measurable after short enrichment steps.
This work will establish a new strategy for food safety monitoring based on rapid spectroscopic prescreening, enabling early identification of contaminated samples for confirmatory testing. The proposed technology addresses critical needs in USDA-regulated food systems by improving screening speed, scalability, and field deployability while remaining compatible with existing regulatory workflows.
Approach:
Task 1. Development of metasurface-enhanced IR spectroscopic platform.
Design and demonstrate a compact, high-speed, metasurface-enhanced IR spectroscopic platform capable of frapid screening of pathogenic bacteria along with their metabolic products or toxins. The system will provide broadband spectral coverage across key biochemical fingerprint regions (1000–3000 cm¿¹), achieve sub-second acquisition times, and deliver improved sensitivity through resonant absorption. Performance will be evaluated using metrics such as spectral resolution, signal-to-noise ratio, response time, and operational stability.
Task 2. Identification of pathogen-associated spectral signatures.
Identify infrared spectral signatures indicative of bacterial contamination, including characteristic features of proteins (amide bands), lipids, carbohydrates, and extracellular metabolites or toxins. Distinct spectral differences between contaminated and uncontaminated samples will be analyzed and documented.
Task 3. Sensitivity assessment and enrichment-time optimization
Perform controlled spiking experiments with representative pathogens (e.g., Salmonella, E. coli, Listeria) to determine detection limits and minimum enrichment time required for reliable screening. Samples will be evaluated at multiple time points during enrichment (e.g., 4–12 hours) to identify the earliest time point at which contamination becomes detectable. Results will be compared with conventional PCR and culture-based methods to benchmark performance.
Task 4. Multi-analyte detection and data analysis
Develop chemometric and machine-learning models capable of distinguishing contaminated from uncontaminated samples and identifying specific contamination types. Where applicable, dual-band (UV–Vis + mid-IR) data fusion will be applied to enhance detection specificity. Model performance will be assessed using standard metrics including sensitivity, specificity, and accuracy.