Jan. 1, 2020 - Dec. 31, 2021Amount Awarded
Paul L. Dawson, Ph.D.
Listeria monocytogenes, an important foodborne pathogen, has been involved in foodborne outbreaks linked to the consumption of produce and fresh fruits. Contamination of these products is problematic since these products are usually consumed without heating. To avoid contamination events, the packing industry must rely on rigorous sanitation practices and environmental sampling plans. This study proposes to develop informational tools regarding environmental sampling and sanitation frequency. Biofilms formed by the packing house background microbiota and L. monocytogenes will be grown in conditions simulating industry settings. The main findings regarding biofilm growth rate and transfer will be then validated in pilot plant studies. Data from the biofilm development and pilot plant validation will be used to build a mathematical model of biofilm development, and ultimately designed as a user-friendly Excel Add-in. The Add-in can turn into a practical tool to predict microbial behavior in the packing house, anticipate optimal sampling time and sanitation intervals and thus provide the scientific data for the sanitation and EMP schedules. Results from this study will provide improved pathogen control in addition to the basic good agricultural practices, thereby helping fruit industry to produce safer produce for human consumption.
The number of human outbreaks associated with Listeria monocytogenes contaminating fresh produce has been problematic for both industry and regulatory agencies. Stone fruits such as peaches and nectarines are usually consumed raw and since traditional preservation techniques such as heating, packaging in modified atmosphere, or reduced water activity do not apply, preventive measures are critical for these commodities. To avoid contamination events, packing houses are required to implement a sampling plan, based on scientific data and risk evaluation, as part of their Environmental Sampling Practices. In establishing scientific-based protocols for efficient environmental monitoring, multiple variables should be considered such as: (i) type of commodity processed, (ii) size and production volume of the facility, and (iii) type and age of the equipment. However, while sampling plans have similarities, there is an inherent degree of variability since facilities, processes, and products packed are different. Additionally, in the packing house, equipment or food contact surfaces can be intrinsically fabricated with materials that can retain microbial contaminants or surfaces hard-to-reach for routine cleaning. In these situations, resident microorganisms and foodborne pathogens including L.
monocytogenes, can grow as biofilms alone or in mixed cultures on abiotic surfaces, detach and transfer, and in turn contaminate food products. In this project we hypothesize that data resulted from the quantification and modelling of L. monocytogenes and the resident microbial community biofilms on surfaces in the packing house can help predict environmental sampling and sanitation intervals. If biofilm growth rates and overall physiological processes are known, then sanitation and sampling procedures can be planned accordingly. In Objective 1 we will determine biofilms properties of L. monocytogenes and resident microflora on food-contact surfaces commonly found in Zone 1 in the packing house. Experiments will involve packing house-isolated background microbiota in combination with L. monocytogenes, and we will determine physiological parameters such as biofilm growth rate, morphology, and detachment. In addition, biofilms will be subjected to a regimen typically found in the packing house (cleaning/sanitation, desiccation, regrowth). In Objective 2 we will establish factors (microbial, physical, temperature and relative humidity) important in L. monocytogenes biofilms persistence and colonization of the packing house. For example, biofilm growth rates and detachment will be established for L. monocytogenes and resident microbiota for conditions typically found in the refrigeration units. These findings will be validated in pilot plant trials, in which background microbiota will be inoculated and allowed to develop as biofilms on selected surfaces. Growth of the biofilms will be evaluated over time as well as transfer rates to the stone fruits. Data from biofilm growth and transfer experiments will be used to build a mathematical model of biofilm development, and ultimately designed as a user-friendly Excel Add-in, to predict optimal environmental sampling time and sanitation intervals. Overall, the findings from this project will improve packing operations and microbiological safety of the products. This project is intended for Part 1.1.2. Listeria monocytogenes preventive controls -cleaning & sanitation/ interventions).