Heat inactivation and survival of Listeria monocytogenes during polony production and shelf life

Abstract

Food safety continues to be a challenge due to re-occurring foodborne illness outbreaks such as listeriosis. The recent listeriosis outbreak in South Africa, in which polony was implicated, prompted for the need to strengthen the food safety systems and the application of more stringent measures to facilitate the production of safe polony products. The study was aimed at determining the effect of the conventional polony processing method against Listeria monocytogenes strains, as well as assessing the applicability of predictive tertiary models in estimating the growth of the strains in polony during the product shelf life. The first phase of the study involved the bacteriological analysis (total plate counts-TPC, lactic acid bacteria-LAB, and Listeria spp.) of polony emulsion and processed (heat-treated to a core temperature of 72℃ and cooled to 10℃) polony, as well as physiochemical characterisation of polony. To determine the processing effect on L. monocytogenes, polony emulsion was inoculated (5 log CFUg-1) with strains of L. monocytogenes (159/10, 69, 732), processed and analysed for L. monocytogenes. Processing affected LAB, TPC, and all strains of L. monocytogenes investigated; a bacterial reduction of more than 5 log CFUg-1 was achieved. Strain heterogeneity influenced the extent to which processing affected L. monocytogenes strains as shown by significant differences (p<0.05) between percentage log MPNg-1 reductions of the strains. L. monocytogenes strain 69 had the lowest percentage log MPNg-1 reduction (61%), followed by strain 732 (71%), and strain 159/10 had the highest percentage log MPNg-1 reduction (91%). Polony had pH of 6.21±0.2, aw of 0.95±0.08, 20±2% fat content and 68±1.8% moisture content. The second phase of the study involved the investigation of the growth of L. monocytogenes strains and LAB in polony during the product shelf life (storage at 4℃ for 12 weeks). Specific maximum growth (μ_max) and lag phase duration (λ) of L. monocytogenes strains were determined. Predictive tertiary models selected based on their availability and the microorganisms of interest were used to estimate LAB and L. monocytogenes growth in polony. The models used for the estimation of L. monocytogenes strains were the “growth of L. monocytogenes in RTE cured meats” model available in MicroHibro software, the "Broth growth" model in ComBase software, and the "growth of L. monocytogenes and LAB in chilled seafood and meat products” model in FSSP software. Growth of LAB in polony was estimated using two models; the “growth of LAB in ground beef” model in MicroHibro and the “growth of L. monocytogenes and LAB in chilled seafood and meat products” model in FSSP. Model performance was evaluated by comparing predicted growth data and observed growth data. The coefficient of determination (R2), bias factor (Bf), accuracy factor (Af) and root mean square error (RMSE) were used as indices for the evaluation of the model performances. L. monocytogenes strains and LAB were able to survive and grow during refrigerated storage. Strain heterogeneity influenced growth (p<0.05) of L. monocytogenes as shown by varying μ_max, and λ of the strains. The growth of L. monocytogenes strains in polony, predicted by the “growth of L. monocytogenes in RTE cured meats” and the “growth of L. monocytogenes and LAB in chilled seafood and meats” models in MicroHibro and FSSP software packages gave acceptable and fail-safe prediction (Bf between 0.87 and 1.43). The ComBase Broth growth model gave unacceptable predictions (Bf ˃1.43) of L. monocytogenes strains in polony. The “growth of LAB in ground beef” and the “growth of L. monocytogenes and LAB in chilled seafood and meats” models gave acceptable predictions of LAB growth in polony. Correct implementation of the polony processing method, coupled with good hygiene procedures, can lead to the production of safe polony. The South African polony industry can also explore the field of predictive food microbiology to strengthen and supplement their surveillance and food safety management systems hence protect human life while saving time and money.