In the united states, salmonella infection is a leading cause of hospitalizations and deaths from foodborne illness, with poultry products being a dominant source

Microsensor Labs, LLC received a $181,500 Phase I Project Grant from the U.S. Department of Agriculture's National Institute of Food and Agriculture (NIFA) under the Small Business Innovation Research (SBIR) Program (CFDA 10.212), awarded September 30, 2025, with a completion date of May 30, 2026. The award funds development and validation of MagiCyte MB, a fully automated, high-speed detection and enumeration system for Salmonella pathogen isolation. The technology integrates novel biological methods, advanced robotics, computer vision, and artificial intelligence to replace traditional manual detection processes that are currently slow, labor-intensive, and expensive—significant bottlenecks in food safety assurance.

During this Phase I research period, Microsensor Labs will validate the technology's ability to rapidly and accurately isolate and enumerate Salmonella from chicken carcass rinse samples at its Chicago, Illinois facility. This innovative approach addresses a critical public health need, as Salmonella infection is a leading cause of foodborne illness hospitalizations and deaths in the United States, with poultry products representing a dominant contamination source. Upon successful Phase I completion, the technology has the potential to fundamentally transform how the food industry conducts pathogen testing and ensures public safety through paradigm-shifting rapid detection capabilities.

The current methods used to detect, isolate and enumeratesalmonella are slow, labor-intensive, and expensive, which creates a major bottleneck in ensuring food safety. This research aims to solve this critical problem by developing magicyte MB, a fully automated, high-speed, and cost-effective system for the rapid detection, enumeration and isolation of salmonella. Our approach leverages novel biological methods, advanced robotics, computer vision, and artificial intelligence to replace traditional manual processes. In this phase i research, we will validate our new technology by testing its ability to quickly and accurately isolate and enumeratesalmonella from chicken carcass rinse samples. The ultimate goal is to introduce a paradigm-shifting technology for pathogen testing that fundamentally changes how the food industry ensures public safety.