Researchers have developed new methods to test food for bacterial contamination that are faster and do not require expert training.
The work is being done at the University of Connecticut College of Agriculture, Health and Natural Resources and is led by Yangchao Luo and Zhenlei Xiao. The research was published in Food Frontiers and in Food Chemistry.
The researchers have developed new methods powered by machine learning to test for bacterial contamination and spoilage that radically reduce the cost and time required to perform such tests. Their method works by using a 96-well plate – a plate with many small areas to fill with samples – and an array of 12 sensors.
The sensors react differently with different bacteria based on their molecular structure. These interactions produce unique patterns. By feeding these patterns into a machine learning algorithm, the researchers taught a computer to detect the pathogens based on the patterns.
The new technology can detect eight different pathogenic and spoilage bacteria in milk in just two hours with more than 98 percent accuracy.
“We hope to develop a technology that can detect simultaneously as many species as possible so that we can easily trace back the original source of contamination,” Luo said.
The research team tested for five pathogenic bacteria including Listeria, E. coli, and Salmonella. They also tested for three non-pathogenic bacteria that cause spoilage.
“With this combination, we are pretty sure that we covered most cases of milk contamination,” Luo says.
The researchers approach is an improvement over existing methods, which can only test for one kind of bacteria at a time. The current process takes days and requires trained laboratory technicians.
The method developed by the researchers uses nanotechnologies with high sensitivity and machine learning to achieve results.
Because performing this test does not require any formal laboratory training, the researchers hope to eventually develop an at-home test using an app that consumers can use to check their milk for pathogens or spoilage.
Luo’s group is also developing an app that would enable a smartphone to read fluorescence data produced by sensors.
The research team is also developing a sensor to detect volatile organic compounds (VOCs), which are produced by bacteria that cause spoilage in meat. These sensors can detect VOCs to determine food’s freshness, specifically beef, and determine the presence of bacteria that causes foodborne illnesses.
“Based on the VOCs we can detect a pattern that can translate into which type of bacteria these VOCs are coming from,” Luo said.
The technology works similarly to the bacterial sensors. When VOCs are released from meat, it produces a color change in the sensor that gives researchers information about what VOCs are being produced and by which bacteria. The group developed machine learning models to read the data.
The advantage of testing for VOCs rather than bacteria in raw meat is that with VOCs, the sensors do not need to be in direct contact with the bacteria, so you don’t need to take a sample out of the product to test it. Taking a sample from a batch of milk is relatively simple, but taking it out of a cut of meat is less so.
The researchers say their technology could be incorporated directly into food packaging to create an easily readable measure of potential food spoilage or contamination based on color changes in the sensor.
“VOCs are volatile – they’re just in the air,” Luo said. “So, you can detect VOCs without touching bacteria. It doesn’t require a sampling process that way. So, we can put a simple sensor on the packaging.”
