Mahmoud Almasri and Shuping Zhang

Principal Investigators

Mahmoud Almasri, PhD

Department of Electrical and Computer Engineering

Shuping Zhang, PhD, DACVM

Department of Veterinary Pathobiology

Mahmoud Almasri, PhD, (left)
and Shuping Zhang, PhD, DACVM

GermSensor system for rapid detection of salmonella and other pathogens

Salmonella is a significant food-borne pathogen that causes millions of infections annually in the U.S. Early detection of salmonella in clinical samples is necessary for prompt treatment and control of the infection. Timely detection and removal of contaminated food products — such as fruits, vegetables, water supplies, meats and poultry — can ensure the safety of consumers and help prevent food-borne outbreaks of salmonellosis, which can lead to large financial losses because of medical costs and product recalls. The traditional methods for pathogen detection have relied on microbiological culture and colony counting. These methods are time-consuming (three to five days to identify the contaminant), require trained personnel and cannot be used in the field, making them unsuitable for timely assessment. Numerous groups have tried to develop techniques to reduce pathogen detection time, however, high cost, long reaction times and/or lack of sensitivity and specificity have precluded their implementation for on-site detection.

The principal investigators have designed, modeled, fabricated and characterized an impedance-based MEMS biosensor prototype that will enable rapid detection of very low levels of salmonella in food products. The high sensitivity of their device is the result of a novel design that utilizes microchannels with varying diameters to concentrate the bacteria. The detection region uses an electrode functionalized with anti-salmonella antibodies to selectively detect salmonella.