UAlbany Scientists Receive USDA Funding to Develop Color-Changing Salmonella Detection Kit

A man with brown hair and glasses holds a small plastic vial containing liquid that is purple at the bottom and red on top. He is wearing purple gloves and a green lab coat.
Mehmet Yigit demonstrates the color change technology to be applied to the salmonella detection kits. (Photo by Patrick Dodson)

By Erin Frick 

ALBANY, N.Y. (May 13, 2024) — Salmonella is a bacteria that can cause serious illness in humans, resulting in an estimated 1.35 million infections, 26,500 hospitalizations and 420 deaths in the U.S. each year, according to the CDC

Most salmonella infections are the result of consuming contaminated food, with poultry, dairy products, eggs and produce being common culprits. Since many of these food items have a short shelf life, it is necessary to deliver them to consumers quickly. When contamination is suspected, fast detection and communication are key. 

University at Albany researchers have been awarded $611,000 from the USDA National Institute of Food and Agriculture to develop a new, fast-acting tool for Salmonella detection.  Similar to the test strips used to measure pH or detect COVID-19, it will display results on a color-changing panel — purple if positive, red if negative. 

If successful, the test will reduce the time it takes to detect salmonella in food from days to hours, making it possible to quickly implement preemptive measures to prevent human illness and lost revenue. 

“Foodborne pathogens pose a significant risk to public health and the global economy, with Salmonella alone costing the U.S. economy a staggering $3.7 billion annually,” said co-principal investigator Mehmet Yigit, associate professor in UAlbany’s Department of Chemistry and the RNA Institute. “Our proposed research aims to address this issue by developing an ultra-sensitive, nanotechnology-enabled approach for rapid Salmonella detection that can easily be used anywhere, without the need for special instrumentation.”

This project will focus on Salmonella enteritidis and Salmonella typhimurium, the predominant serotypes responsible for half of all human infections in the U.S.

A clear plastic well plate containing two wells full of liquid; one well is red, one well is purple. the plat is being held be a person wearing purple gloves holding a clear plastic pipette.
In the presence of salmonella, the test strip will turn purple. (Photo by Patrick Dodson)

"Salmonella outbreaks not only jeopardize public health but also disrupt the food supply chain, causing economic stress and public anxiety,” said co-principal investigator Abdullah Canbaz, assistant professor of Information Sciences and Technology in the College of Emergency Preparedness, Homeland Security and Cybersecurity. “Leveraging cutting-edge technology, our goal is to contribute to early and precise identification of Salmonella, thereby advancing public health initiatives.” 

“This system will make it easy for anyone suspecting contamination to test a sample and receive verified results within six hours. For comparison, current methods requiring microbial culturing can take several days.”

Central to this work is the development of a novel detection approach that relies on nanotechnology and artificial intelligence, instead of microbial culturing or whole genome sequencing — common techniques that are reliable yet time-consuming.

The proposed diagnostic kit will include pre-filled vials, allowing the user to easily add their sample and induce the chemical reaction necessary to determine Salmonella presence or absence. When the processing is complete, color-coded results (purple/positive or red/negative) will be visible in a test-tube solution or on a paper test strip. 

Two men pose for a portrait in a chemistry lab. One is seated, wearing a black blazer and jeans; the other is leaning against a lab bench wearing a gray blazer and khaki pants. Various glass and plastic containers are on the bench and shelf behind them.
Co-principal investigators Abdullah Canbaz (left) and Mehmet Yigit (right). (Photo by Patrick Dodson)

The team is further exploring the development of an image analysis system, integrating machine learning techniques to interpret the color patterns exhibited on the nanoarray test strip. This innovative approach aims to empower users by providing automated assistance in result interpretation, enhancing the overall usability and accessibility of the Salmonella detection process. 

“This feature will operate in a way similar to certain COVID-19 tests that allow you to take a photo of the test strip with your phone, send it off for analysis, and receive a confirmation as to whether the result is positive or negative,” Yigit said. 

Canbaz, the director of the 'AI in Complex Systems Lab' at UAlbany, will take the lead in overseeing the development of a smartphone application specifically designed for image analysis, incorporating cutting-edge image recognition and machine learning techniques.

“Our lab will be creating machine learning models for Salmonella detection via image classification, harnessing the power of data analytics to deliver verified results via mobile app,” Canbaz said. “This mobile communication method will play a crucial role in sharing test results immediately with inspectors, industry experts, consumer stakeholders and other public health and regulatory partners, ensuring that — if needed — swift action can be taken to contain pathogen transmission, minimize health risks and prevent economic loss.”

The team’s nano-diagnostic system will also serve as a template for foodborne bacterial detection beyond Salmonella, marking a significant step forward in the field of food safety.

This work is supported by the USDA National Institute of Food and Agriculture, AFRI project [proposal number: 2022-08596 and accession number: 1031549].