AI predicts bacteria's resistance to cleaning agents

Researchers at the DTU National Food Institute have demonstrated that machine learning can be used to predict whether a disease-causing bacterial strain will survive cleaning, paving the way for smarter hygiene strategies and faster responses when there is a risk of pathogenic bacteria being present in a food production. Their work has been published in the journal Scientific Reports.

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 biofilm — a slimy layer that adheres to surfaces — which can, over time, lead to resistance against the disinfectants that are used to eliminate it.

“The danger lies in the fact that a surface may appear clean, yet resistant bacteria can still be hiding in cracks and corners,” said senior researcher Pimlapas Shinny Leekitcharoenphon. But while it is crucial to act swiftly to prevent the spread of disease, detecting resistance has until now required time-consuming laboratory tests that can take days.

The new study saw researchers analyse the entire genome of over 1600 Listeria strains, whose 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,” Leekitcharoenphon said.

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. As noted by Leekitcharoenphon, “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.”

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.

The researchers said their method can help the food industry use existing disinfectants more efficiently — by selecting the right product for the right bacterium based on its DNA profile. At the same time, the discovery of new resistance genes may inspire the future development of improved disinfectants that exploit the bacteria’s vulnerabilities.

“AI does not provide us with a recipe for new disinfectants, but it does tell us which bacteria are likely to survive which chemicals,” Leekitcharoenphon said. “This enables swift and precise action.”

This method shows that, with DNA data and machine learning, accurate bacterial resistance predictions can be made in minutes. But as the current standard for cleaning in the food industry is not based on genome sequencing, it will likely take time to incorporate the new method.

“We hope our method will become a valuable tool in the fight against disease-causing bacteria and contribute to making food production even safer,” Leekitcharoenphon said.

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