Protecting families from contaminated food and water
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Protecting families from contaminated food and water
Posted in Our Blog on March 30, 2026
What if an AI sensor could sniff out foodborne germs? What if it could identify potential contamination at different steps in the production process? How beneficial could a technology like this be?
Researchers from the University of Connecticut have developed a new AI sensor to accurately detect microbial contamination and spoilage in milk and meat products. This new technology provides a lower cost option for manufacturers to detect microbial contamination and spoilage in their products.
Before they leave the facility!
The costs associated with precautionary product sampling have been historically cost prohibitive for smaller manufacturers and time consuming for larger ones. Sampling often takes place. However, not at the scale sometimes necessary to capture all risk. This new technology satisfies both!
What exactly is this new AI sensor technology? How will it help the dairy and meat industry? How soon could we see its use become mainstream?
Here’s what we know about the new AI sensor technology right now.
In the University of Connecticut journal article, Machine Learning supported Single-Stranded DNA Sensor Array for Multiple Foodborne Pathogenic and Spoilage Bacteria Identification in Milk, that was published in the scientific journal, Food Chemistry, scientists explained this novel technology.
This technology, discussed in applications for milk and meat products, provides the opportunity for both “onsite and consumer-level” testing.
No specialized training is necessary, and a simple smartphone app is in the works.
Additional steps are being made to eliminate sample preparation steps (things like protein removal) that historically must be performed to ensure accuracy.
In a different journal article, Machine Learning Supported Ground Beef Freshness Monitoring Based on Near-Infrared and Paper Chromogenic Array, scientist explain that they are also working on sensors used to detect volatile organic compounds associated with meat spoilage bacteria. Machine learning models (AI technology) were trained to identify these compounds and associate them with specific bacterial types.
No direct contact is needed, minimal training is required, and the tool can be easily integrated into normal quality control activities.
But, how exactly does it work?
According to University of Connecticut scientists, eight illness-causing and spoilage germs can be identified in milk samples with a 98 percent accuracy, within two hours. However, work to shorten that timeframe is in progress.
The current proposed technology uses a 96-well plate, where samples are placed. A small amount of each sample to be tested.
Using a detection sensor (testing 12 samples at a time) an AI sensor analyzes the molecular structure of any bacteria present and notifies the user.
Currently, the sensor can identify five pathogenic (illness-causing) bacteria, including Listeria, Escherichia coli, and Salmonella, along with three spoilage organisms commonly associated with milk contamination.
The current alternatives on the market are traditional microbial methods, enzyme immunoassays, and genetic analysis.
Historically, microbiological methods have been the gold standard for definitive pathogen identification. The sample is cleaned, spread on a growth medium designed for one bacteria at a time, and incubated.
It is slow (requires several days), labor intensive (each bacteria must be tested separately), and requires specialized laboratory infrastructure (clean rooms and incubators) and trained personnel.
Next up, are enzyme immunoassays. More specifically, ELISA (Enzyme-Linked Immunosorbent Assay). This technology detects peptides, proteins, and other identifying compounds in samples to identify bacteria in samples. It is quite expensive and also requires specialized laboratory infrastructure, large and expensive equipment, and trained personnel.
Finally, there is genetic analysis. This type of testing is often used in identifying shared illnesses. Whole Genome Sequencing (WGS) technology can determine if two samples are genetically related. This technology is expensive, requires very specialized equipment, is time consuming, and trained personnel.
If this AI sensor technology works like they say it does, just about anyone in the facility could manage testing. It is quick, accurate (94% at 30 minutes and 98% at one hour), and can be performed onsite.
When germs get into our food supply, people get sick. Dairy and meat are especially vulnerable to supply chain contamination.
The U.S. Centers for Disease Control and Prevention (CDC) estimates that each year 48 million people get sick from a foodborne illness in the United States. Around 128,000 people are hospitalized, and 3,000 die.
Anyone can become sick with foodborne illness. However, there are groups of people who are more likely to get sick if exposed and experience more severe illness if infected. This includes pregnant women, children under five years of age, adults over 65 years, and people with a weakened immune system.
According to the most recent FDA CORE Annual Report, 10% of all outbreaks in 2024 were linked to solid or semi solid dairy products (cheese and soft cheese). Meat has shown up on previous year’s reports. These products have historically been associated with foodborne illness and foodborne illness outbreaks.
We use AI for so many things in our daily lives. From the rudimentary autocorrect and predictive text, to photo filters and social media applications, and so many places we do not see. Using AI sensors to make our food supply safer is a great step in the right direction for the technology.
Accessibility and affordability are the key to making its use more mainstream.
So, how soon could we see these AI sensors in use?
It could take some time. The U.S. Food and Drug Administration has strenuous requirements for devices like this. However, with a safe food supply agenda in focus, we may see movement sooner rather than later.
As a scientist, I look forward to following this technology as it develops.
If you’d like to know more about food safety topics in the news, like “Researchers Develop AI Sensor to Accurately Detect Microbial Contamination and Spoilage in Milk and Meat,” check out the Make Food Safe Blog. We regularly update trending topics, foodborne infections in the news, recalls, and more! Stay tuned for quality information to help keep your family safe, while The Lange Law Firm, PLLC strives to Make Food Safe!
By: Heather Van Tassell (contributing writer, non-lawyer)