Apr 13, 2021Project seeks science-based interventions to persistent listeria strains
Martin Wiedmann likes to compare produce facilities’ attempts to identify and manage resident strains of the foodborne pathogen, Listeria monocytogenes, as “seek and destroy” missions.
Over the years, the produce industry has significantly improved its ability to detect, or seek out, persistent listeria strains, said Wiedmann, Ph.D., Cornell University. Where the industry often remains challenged is in the “destroy” part of the mission.
“Every processing facility is different, and often times the interventions are expensive,” he said in a news release. “The seek part is time consuming, but once you’ve trained people, you can, with a reasonable budget, find issues. Once you’ve identified interventions — whether they address design issues or maintenance or even cracks in a floor — it’s sometimes a capital investment with no certainty it will really fix the issue.”
As a result, Wiedmann said the industry needs improved resources to rapidly pinpoint factors that may contribute to listeria persistence and then identify appropriate science-based interventions to prevent, eliminate or manage relevant causes.
Through the project, “Factors affecting persistence of Listeria monocytogenes need to be identified for evaluation and prioritization of interventions,” he and his team plan to take a three-step approach that will ultimately result in a decision support tool.
Joining Wiedmann as co-principal investigator is Renata Ivanek, Ph.D., also with Cornell, who has expertise in developing computer-based models.
Their project began with a literature search that expanded beyond just produce-related situations since listeria persistence can also be a problem elsewhere. In addition, they looked at both published and unpublished write-ups.
Wiedmann provided a pallet jack as an example of equipment used in produce as well as other industries, such as meat packing, that can harbor persistent listeria populations.
“The idea is there will be useful experiences in the produce industry already and in other industries that could be helpful for the produce industry with the destroy part of it,” he said. “Field trials can be expensive and difficult. But if there is existing knowledge, we want to take advantage of it.”
After winnowing the original 1,656 documents down to 264, the team furthered screened them to identify about 32 of the most relevant strategies. They then validated some interventions they identified with four cooperating packinghouse facilities.
Initially they sampled each facility before any interventions and performed a formal root cause analysis with facility personnel.
“What do we think the problem is?” Wiedmann said. “Once we identified it, we looked at potential solutions from the literature but also from a common-sense standpoint. What interventions should we be doing? Then we followed up to see if they had an effect.”
Based on those results, the researchers developed a step-by-step approach packinghouses can use to use to conduct a root cause analysis to identify the most likely factors behind specific persistence as well as appropriate interventions.
Wiedmann said it was “absolutely essential” to have this type of produce-industry participation to ensure their results were applicable to produce facilities.
“To figure out what’s happening in the real world, we need to be in a real facility,” he said.<
Interventions they identified but were too difficult to validate experimentally in a packing facility were tested by using a previously developed computer model.
“We’ve re-created produce facilities in almost 3D, so we can identify places where Listeria can survive over time and we can see what happens as we change things,” Wiedmann said.
In the end, the researchers plan to develop a decision-support tool the industry can use to develop intervention strategies. Much like they did when they were validating interventions, the researchers will seek industry input on the support tool from a focus group.
The tool also will allow plant personnel to run “what if” scenarios to compare different strategies, but it won’t replace the human element.
“The operator will still make the decisions, but we want to improve the chances they make the right decision,” he said. “The model doesn’t make the decision for them, but it helps them make better decisions more quickly.”