Managing time-temperature risk in fresh produce using predictive models for Listeria

Managing time-temperature risk in fresh produce using predictive models for Listeria

Jan. 1, 2019 - May. 31, 2019

$7,200.00

Donald Schaffner, Ph.D.
Rutgers

Final Report

Poster

Other

Rationale and Objectives

Many foods are perishable and require time/temperature control throughout their shelf life. In many cases this control is required to ensure quality, but in an uncertain number of situations, it may also be required for food safety (to control foodborne pathogen growth). This issue is often discussed by fresh produce producers and buyers and is now exacerbated by several federal regulations and policies including the Preventive Controls Rule and Sanitary Transportation Rule. While research funded by CPS may eventually provide definitive laboratory-based information to guide these discussions, there is an urgent need for short-term science-based parameters on this topic. This project will focus on the pathogen most likely to grow at the temperature range of interest (Listeria monocytogenes) and will use "off-the-shelf" computer models in the form of ComBase Predictor (https://browser.combase.cc/ComBase_Predictor.aspx?model=1. The objective will be to produce a report with a series of time and temperature tables, comparing relative risk of Listeria monocytogenes growth for different conditions to guide science-based risk management decisions.

Methods

The investigator will work in collaboration with a small team of produce industry experts to define times (days or weeks) and temperatures (e.g. 40-55 °F) that are relevant to the storage of fresh produce. The modeling predictions will include both constant temperature conditions, as well as representative examples of changing temperature conditions that are relevant to fresh produce storage. Although pH values in the range common in fresh produce are unlikely to have a dramatic effect on predicted L. monocytogenes growth rate, this factor will be included as well. This preliminary work will not consider the effect of spoilage organisms or competitive microflora. This work will also not explicitly consider whole versus cut produce, although the availability of moisture and nutrients are known to affect bacterial growth rates. These assumptions will make the models highly conservative, and thus quite robust and able to withstand scrutiny from regulatory agencies or over-zealous inspectors. It is quite likely that actual pathogen growth on specific produce commodities are much less than the model predictions, and in some cases no growth or slow decline would actually be observed in the real world. Schaffner has applied a similar approach in a paper entitled "Utilization of mathematical models to manage risk of holding cold food without temperature control " which was performed as a Schaffner CPS Listeria Modeling 2 contract for a cash and carry chain called Jetro/Restaurant Depot. That project resulted in a science-based means to assess risk and inform risk management decisions regarding transportation of cold food without temperature control for both the chain as well as the chains customers (restaurants) and in a peer-reviewed publication (Schaffner 2013). With additional funding, the final report produced by this project could still result in a similar output. This project also has a natural synergy with the 2019 funded project entitled "A Systematic Review of Listeria Growth and Survival on Fruit and Vegetable Surfaces: Responding to Critical Knowledge Gaps" (Strawn and Schaffner). Once that lab-based project concludes at the end of 2019, it will be possible compare those data to the conservative model-based predictions generated this project.