Computer program aids food safety types with pathogen testing

Cornell University scientists have developed a computer program, Environmental Monitoring With an Agent-Based Model of Listeria (EnABLe), to simulate the most likely locations in a processing facility where the foodborne pathogen Listeria monocytogenes might be found. Food safety managers may then test those areas for the bacteria’s presence, adding an important tool to prevent food contamination and human exposure to the pathogen through tainted food.

The computer model, which is described in the Jan. 24 issue of Scientific Reports, has the potential to be modified for a wide range of microbes and locations.

“The goal is to build a decision-support tool for control of any pathogen in any complex environment,” said Renata Ivanek, associate professor in the Department of Population Medicine and Diagnostic Sciences and senior author of the paper. The study was funded by the Frozen Food Foundation through a grant to Martin Wiedmann, professor of food science, who is also a co-author of the paper.

The researchers, including first author Claire Zoellner, a postdoctoral research associate in Ivanek’s lab, want to eventually apply the framework to identifying contamination from pathogens that cause hospital-acquired infections in veterinary hospitals or E. coli bacteria in fruit and vegetable processing plants.

Food safety professionals at processing facilities keep regular schedules for pathogen testing. They rely on their own expertise and knowledge of the building to determine where to swab for samples.

“Whenever we have an environment that is complex, we always have to rely on expert opinion and general rules for this system, or this company, but what we’re trying to offer is a way to make this more quantitative and systematic by creating this digital reality,” Ivanek said.

For the system to work, Zoellner, Ivanek and colleagues entered all relevant data into the model – including historical perspectives, expert feedback, details of the equipment used and its cleaning schedule, the jobs people do, and materials and people who enter from outside the facility.

“A computer model like EnABLe connects those data to help answer questions related to changes in contamination risks, potential sources of contamination and approaches for risk mitigation and management,” Zoellner said.

“A single person could never keep track of all that information, but if we run this model on a computer, we can have in one iteration a distribution of Listeria across equipment after one week. And every time you run it, it will be different and collectively predict a range of possible outcomes,” Ivanek said.

The paper describes a model system that traces Listeria species on equipment and surfaces in a cold-smoked salmon facility. Simulations revealed contamination dynamics and risks for Listeria contamination on equipment surfaces. Furthermore, the insights gained from seeing patterns in the areas where Listeria is predicted can inform the design of food processing plants and Listeria-monitoring programs. In the future, the model will be applied to frozen food facilities.

Safe food, farm to fork

It’s been my lab’s moto for over 20 years.

Nice to see the American Society for Microbiology catch up (nothing personal, Randy, just idle academic chirping, but at least you get paid).

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes.

The postharvest supply chain with microbial travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to the United States, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.

IMPORTANCE Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and postharvest handling is an important food safety goal. Since studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The postharvest supply chain with microbial travelers (PSCMT) model, presented here, provides a unique tool for postharvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing postharvest research and interventions to improve understanding and control of fresh produce contamination.

Postharvest supply chain with microbial travelers: A farm-to-retail microbial simulation and visualization framework

American Society for Microbiology, 10.1128/AEM.00813-18

Claire Zoellner, Mohammad Abdullah Al-Mamun, Yrjo Grohn, Peter Jackson, Randy Worobo

Good on ya, Tom; McMeekin receives Australian award for risk modeling work

Tom always reminded me of my uncle Larry – gregarious and quick with a quip for Douggie whenever I saw him.

ABC News reports that University of Tasmania Emeritus Professor Tom McMeekin has been made an Officer of the Order of Australia for his tom.mcmeekin.jun.13distinguished service to science particularly in the development of food safety standards and education.

Tom was the Professor of Microbiology at the School of Agricultural Science at UTAS and was instrumental in the establishment in the Australian Food Safety Centre of Excellence.

The work he and a group of four other scientists did established new systems of predicting food safety around the world.

“We can do safety and we can do shelf life. We can also predict how a pro-biotic organism will grow in a particular medium like a yoghurt, or if it will die out.”

Tom McMeekin says the model has been adopted in Australia and around the world.

“The biggest breakthrough in application we had was with Meat and Livestock Australia who negotiated with AQIS on behalf of the Australian meat industry to change the way meat was tested.

Prior to these predictive models meat in a chiller in an export abattoir had to be cooled and then tested for the e coli or whatever. So retrospective, holding your product until you are sure nothing has grown on it.

Now we can use the model as a surrogate for that testing, and the model gives you an answer in real time.

That is now mandated in the export control orders and that is what monitors safe chilling process in Australian export abattoirs.