According to the US Dept. of Agriculture’s (USDA) Food Safety Inspection Service (FSIS) 2018 saw 21 recalls of beef, poultry, pork and products containing meat and poultry due to Listeria monocytogenes. Recalled pounds of product for 2018 totaled 4,127,696 lbs., according to FSIS.
Because Listeria exists in a multitude of places throughout nature including water and soil, it’s imperative for meat and poultry processors to remain vigilant in their efforts to identify and eradicate with extreme prejudice. New technologies are at the forefront of Listeria detection and control including testing both on-site and third-party, as well as new computer modeling, a method still in development, but moving in a positive direction.
Environmental modeling
New Cornell Univ. research, funded by the Frozen Food Foundation and published in the Jan. 24, 2019, issue of Scientific Reports, revealed a potential solution that would allow processors to control Listeria monocytogenes in their facilities. The Cornell study focused on the development and testing of a computer model potentially capable of finding specific locations in processing facilities where Listeria monocytogenes might be found.
“Our organization and industry are focused on better understanding potential entry points for Listeria in frozen food facilities, ultimately leading to specific food safety protocols,” said Frozen Food Foundation Executive Vice President Donna Garren, Ph.D., in a press release. “Listeria monocytogenes is a challenge because of its ubiquity and ability to survive freezing temperatures. Cornell’s innovative work opens a new, predictive model for the frozen food industry to better understand and develop more robust food safety programs for detecting and minimizing the presence of Listeria monocytogenes.
“Illness stemming from frozen foods is extremely rare. But we want to do our part to prevent a listeriosis event from occurring,” Garren added. “That’s why we invest in scientific research to guide Listeria monocytogenes-monitoring best practices, from the frozen food facility to fork. We are excited for the food safety advances Cornell has presented with this research.”
The study and the model developed are based on samples of Listeria spp. on equipment and surfaces in a cold-smoked salmon facility, and provided valuable data and patterns that can predict Listeria behavior. The information gleaned can be used to inform the design of all types of food manufacturing facilities, as well as Listeria monitoring programs.
The basis and logic of the model will allow users to create exact replications of their facilities, once the tool is perfected and becomes readily available. Facilities need the ability to input exact dimensions, locations of machinery, lighting, drainage, etc., to properly utilize the tool and reap all the benefits it offers. Things such as exact spatial locations give the model its strength.
“In a food processing environment every facility is different,” Ivanek says. “Even if they produce the same product, they will have different characteristics. Even if they are owned by the same company, they will have differences. So, one approach cannot fit all perfectly, but the skeleton, the main set up can.”
EnABLe takes a one size does not, and cannot, fit all approach. No matter how similar facilities may be to one another, they’ll never have the exact same dimensions or spatial relationships. But creating a computer twin of a given facility and inputting all available data relative to a pathogen, in EnABLe’s case Listeria, users can now start asking questions. If introduced, where should I look for the pathogen? If I find the pathogen in this location, where did it most likely come from?
“Or, we have a problem in this facility, and we think this wall and the drainage is creating the problem, lets tear this wall down. In the computer twin, you can test those hypotheses and assumptions before you make any changes,” Ivanek says. “So, it creates an immense value for the facility to be able to visualize what is happening and test approaches before actually using them in real life.”
Ivanek and her key associates on the project, Dr. Claire Zoellner, first author of the study, and Dr. Martin Wiedmann, professor of food science, co-author and the grant awardee, envision the final iteration of EnABLe as a user-friendly template. Processors would simply input facility specifications along with pathogen data.
“So, the user can introduce those specific aspects, and elements and connections,” Ivanek says. “For example, when you think about Listeria, everything that’s Listeria related, how it grows, how it dies, what kills it, that’ll be the same for all models that are Listeria specific.”
The EnABLe model is still a prototype at this point and Ivanek and her team, in conjunction with the Frozen Food Foundation, are currently in the process of confirming it works to their satisfaction.
“The way that the model is developed, it is not super user friendly yet,” Ivanek says. “It is just fresh from the research bench. So, the next steps could be to make it more user friendly so that indeed you as a user, you just make three clicks and you’re ready to go. Right now, it requires more effort, and really in knowing more what you’re doing because it’s in the form of a computer code.”
Outside help
Austin, Texas-based Food and Ag Lab LLC, offers food processors food safety testing lab services, consulting and auditing. Companies use Food and Ag Lab’s services for many reasons related to Listeria and at different points in time relative to a Listeria issue.
“They are usually looking for help with evaluation of their systems,” says Darren Toczko, MS, founder of Food and Ag Lab. “During a Listeria investigation, some processors ask for more help, either after they have repeated positives that they can’t figure out how to eradicate, or after a product positive that wasn’t first indicated as a potential issue from their environmental sampling plan.”
Once a processor has enlisted Food and Ag Lab’s services and the two companies establish a partnership, there’s a typical flow to how the relationship and execution goes.
“We go into facilities with the local staff, observe the process, review sampling plans, watch how sampling is conducted, observe the flow of product, water, people, traffic patterns, and sanitation,” Toczko says. “Then, we sit down together and go over observations and recommendations. It’s up to the processor to decide which of the recommendations should be implemented.”
