A study published in Scientific Reports, a peer-reviewed journal from the Nature Portfolio, demonstrates that machine learning can be used to predict whether a bacterial strain will survive cleaning. The research paves the way for smarter hygiene strategies and faster responses when there is a risk of pathogenic bacteria being present in a food production.
The hidden threat in clean environments
Listeria monocytogenes is a foodborne bacterium that thrives in the cold and damp environments, that are often found in food processing facilities. One of the major challenges posed by listeria is its ability to form biofilms—a slimy layer that adheres to surfaces—which can, over time, lead to resistance against the disinfectants that are used to eliminate it. Until now, detecting this resistance has required time-consuming laboratory tests.
“The danger lies in the fact that a surface may appear clean, yet resistant bacteria can still be hiding in cracks and corners,” says Senior Researcher at the DTU National Food Institute Pimlapas Shinny Leekitcharoenphon.
DNA and AI— a powerful duo
In the study, researchers analysed the entire genome of over 1,600 listeria strains. These DNA profiles were used to train a machine learning model that learned to identify genetic patterns associated with resistance to disinfectants commonly used in the food industry.Three different disinfectants were tested: two pure chemical compounds—benzalkonium chloride (BC) and didecyldimethylammonium chloride (DDAC)—as well as a commercial product, Mida San 360 OM.
“It’s like teaching a computer to read the bacteria’s manual, and then letting it tell us whether the bacterium is likely to survive cleaning with a particular disinfectant,” says Pimlapas Shinny Leekitcharoenphon.
The AI model achieved an accuracy of up to 97% and was able to predict tolerance to both the pure chemical substances and the commercial product.
“It is promising that the models work not only for the pure chemical substances, but also for a product that is actually used in the food industry. This suggests that the method could be applied in real-world settings,” says Pimlapas Shinny Leekitcharoenphon.
In addition to known resistance genes, the researchers also discovered several new genes that may play a role in the bacteria’s ability to survive disinfectants. This improves the predictive power of the model and may provide new insights into how bacteria develop and spread resistance.
Do we need new disinfectants?
The researchers suggest that their method can initially help the food industry use existing disinfectants more efficiently—by selecting the right product for the right bacterium based on its DNA profile.
“AI does not provide us with a recipe for new disinfectants, but it does tell us which bacteria are likely to survive which chemicals. This enables swift and precise action,” says Pimlapas Shinny Leekitcharoenphon.
At the same time, the discovery of new resistance genes may inspire the future development of improved disinfectants that exploit the bacteria’s vulnerabilities.
A breakthrough for food safety
Testing bacterial resistance in the laboratory can take days. This method shows that, with DNA data and machine learning, accurate predictions can be made in minutes. When pathogenic bacteria emerge in a food production facility, it is crucial to act swiftly to prevent the spread of disease.
“We hope our method will become a valuable tool in the fight against disease-coursing bacteria and contribute to making food production even safer,” says Pimlapas Shinny Leekitcharoenphon.
The current standard for cleaning in the food industry is not based on genome sequencing, and, as with any other new technology, it will take time to incorporate a new method.
‘We have just received funding to continue the work, and the goal of the research is for the method to be easily usable by employees in a food production site,” says Pimlapas Shinny Leekitcharoenphon.
