Abstract

Environmental sanitation in food manufacturing plants promotes food safety and product microbial quality. However, the development of experimental models remains a challenge due to the complex nature of commercial cleaning processes, which include spraying water and sanitizer on equipment and structural surfaces within manufacturing space. Although simple in execution, the physical driving forces are difficult to simulate in a controlled laboratory environment. Here, we present a bench-scale bioreactor system which mimics the flow conditions in environmental sanitation programs. We applied computational fluid dynamic (CFD) simulations to obtain fluid flow parameters that better approximate and predict industrial outcomes. According to the CFD model, the local wall shear stress achieved on the target surface ranged from 0.015 to 5.00 Pa. Sanitation efficacy on six types of environmental surface materials (hydrophobicity, 57.59 to 88.61°; roughness, 2.2 to 11.9 μm) against two different microbial targets, the bacterial pathogen Listeria monocytogenes and Exophiala species spoilage fungi, were evaluated using the bench-scale bioreactor system. The relative reduction ranged from 0.0 to 0.82 for Exophiala spp., which corresponded to a 0.0 to 2.21 log CFU/coupon reduction, and the relative reduction ranged from 0.0 to 0.93 in L. monocytogenes which corresponded to a 0.0 to 6.19 log CFU/coupon reduction. Although most treatment parameters were considered statistically significant against either L. monocytogenes or Exophiala spp., contact time was ranked as the most important predictor for L. monocytogenes reduction. Shear stress contributed the most to Exophiala spp. removal on stainless steel and Buna-N rubber, while contact time was the most important factor on HDPE (high-density polyethylene), cement, and epoxy.IMPORTANCE Commercial food manufacturers commonly employ a single sanitation program that addresses both bacterial pathogen and fungal spoilage microbiota, despite the fact that the two microbial targets respond differently to various environmental sanitation conditions. Comparison of outcome-based clusters of treatment combinations may facilitate the development of compensatory sanitation regimes where longer contact time or greater force are applied so that lower sanitizer concentrations can be used. Determination of microbiological outcomes related to sanitation program efficacy against a panel of treatment conditions allows food processors to balance tradeoffs between quality and safety with cost and waste stream management, as appropriate for their facility.

Highlights

  • Environmental sanitation in food manufacturing plants promotes food safety and product microbial quality

  • One notable difference between bacterial foodborne pathogens and fungal spoilage microbes is the variation in typical cell sizes which may represent an important difference in ranking the relative contribution of sanitation program features between the two groups

  • This study incorporated the impact of shear stress in environmental sanitation, as well as the interactions between surface material and microbial target within a bench-scale model

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Summary

Introduction

Environmental sanitation in food manufacturing plants promotes food safety and product microbial quality. According to the CFD model, the local wall shear stress achieved on the target surface ranged from 0.015 to 5.00 Pa. Sanitation efficacy on six types of environmental surface materials (hydrophobicity, 57.59 to 88.61°; roughness, 2.2 to 11.9 ␮m) against two different microbial targets, the bacterial pathogen Listeria monocytogenes and Exophiala species spoilage fungi, were evaluated using the bench-scale bioreactor system. IMPORTANCE Commercial food manufacturers commonly employ a single sanitation program that addresses both bacterial pathogen and fungal spoilage microbiota, despite the fact that the two microbial targets respond differently to various environmental sanitation conditions. Wall shear stress is the tangential force of the flowing cleaning solution on the soiled surface and has been identified as an essential parameter in sanitation effectiveness, but it is difficult to model experimentally and extrapolate to industrial settings. We apply these advanced modeling tools in the simulation of environmental sanitation

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