SBIR Phase II: Combating Pathogens with Onsite Universal Detection
- Institution
- TOTAL ANALYSIS L.L.C., MI
- Principal investigator
- Darrell D Marshall Phd
- Funding
- $1.2M
- Duration
- 2026–2028
Moonbase score
Still being scored
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is the advancement of a rapid, on-site pathogen screening capability that strengthens food safety and public health preparedness. This project advances an early-warning screening approach that improves situational awareness and enables faster, more targeted responses within existing food safety systems. By supporting earlier identification of elevated risk, the project has the potential to reduce foodborne illness, limit economic losses associated with recalls or facility shutdowns, and improve resilience across domestic food supply chains. The project also contributes to scientific and technological understanding by advancing methods for interpreting complex biological signals and translating them into actionable screening information under real-world conditions. The proposed project addresses the need for rapid pathogen risk screening that operates prior to confirmatory laboratory diagnostics such as polymerase chain reaction testing. The research objective is to advance a point-of-need screening system based on ion mobility spectrometry, a technique that separates ionized molecules to generate characteristic signal patterns, from a manual prototype to an automated, higher-throughput platform suitable for food production. Key technical challenges include achieving consistent detection performance across various food matrices while meeting targets for limit of detection, sensitivity, and specificity without requiring pathogen growth, as well as reducing false positive and false negative rates in complex biological backgrounds. Phase II activities will focus on targeted experimental validation under realistic operating conditions, optimization of the ionization settings, further standardization of sample handling, expansion of curated reference signal databases, and integration of machine learning methods to improve classification accuracy and robustness. 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.
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