Corbion has improved its Listeria control system within an online simulation tool that helps manufacturers test out supply chain storage and distribution scenarios more accurately. The ingredient supplier has incorporated a new temperature control feature in this virtual platform that permits producers to visualize how five different storage conditions with ascending temperatures will cause ingredients to change in quality.
The Corbion Listeria Control Model (CLMC) is designed to help producers “fight real-world pathogen wars more effectively,” according to the business.
Moreover, it helps producers identify optimal antimicrobial interventions based on product formulation.
Easy to use tool
The tool helps producers save time and money that would have been spent carrying multiple iterations of validation testing.
“Corbion’s Listeria control model is more intuitive and user-friendly than other models currently being used. Users can easily add in their own data points to clearly defined boxes with a simplified interface,” Evelien Jochems, senior project manager at Corbion, tells.
“Furthermore, the data reports provided are also easy to read and understand, reducing the overall complexity for the user. By easily screening for a variety of food characteristics, the CLCM can be used to optimize the formulation and final product.”
With the addition of dynamic temperature, up to five different storage conditions with ascending temperature profiles can be used in the overall prediction result. Allowing for a better and more realistic prediction for users, according to Corbion.
“If our customers had to run these additional features as part of a challenge study, complexity and cost will increase exponentially. This feature is also added in a very user-friendly manner to accommodate the user experience,” Jochems notes.
The tool upgrade is particularly significant in Europe. To comply with EU regulations, European manufacturers must test their products for Listeria under three different storage temperatures, simulating real-world scenarios.
Control options
When carrying out their tests using the CLCM, users can quickly determine the impact of different variables, including food characteristics such as water activity, pH and salt content and environmental factors.
“This can reduce the overall number of treatments required to be run,” Jochems underscores.
The formulation can be easily modified by changing the dosage or trying a different antimicrobial if the predicted results are not aligned with the formulation goals.
“Typical challenge studies can run anywhere from 90 to 150 days. Without using a predictive modeling tool, running several rounds of challenge studies can tremendously increase the development time, costs and ultimately reduce the speed to market.”
Future of food pathogen control
Corbion recently launched a new online model, the Corbion Natural Mold Inhibition Model (CNMIM), to predict time-to-visible mold growth for bread. Jochems notes that it is the first tool of its kind in the market.
“Initially, our teams wanted to develop a model that would help reduce food waste in the baking industry; however, this model is also related to food safety.”
“Additionally, we have several tools and models used internally and directly with customers to predict the outgrowth of other pathogens and food spoilage microorganisms. An example of these tools would be our models related to Clostridia. We have several tools regarding Clostridium botulinum which can be used for both proteolytic and non-proteolytic bacteria,” she explains.
The current Corbion model is built using a variety of extensive data points, including published scientific literature, in-vitro data, and in-house challenge studies. Currently, the CLMC does not use AI, but it might be a “possibility” in future models.
“With the possibility of developing with AI, Corbion can continue to maintain our industry leadership in this area,” Jochems concludes.
