Absence of growth of Listeria monocytogenes in naturally contaminated Cheddar cheese

Hurdle factors minimizing growth of Listeria monocytogenes while counteracting in situ antilisterial effects of a novel nisin A-producing Lactococcus lactis subsp. cremoris costarter in thermized cheese milks

The capacity of growth, survival, and adaptive responses of an artificial contamination of a three-strain L. monocytogenes cocktail in factory-scale thermized (65 °C, 30 s) Graviera cheese milk (TGCM) was evaluated. Bulk TGCM samples for inoculation were sequentially taken from the cheese making vat before process initiation (CN-LM) and after addition of a commercial starter culture (CSC), the CSC plus the nisin A-producing (NisA+) costarter strain Lactococcus lactis subsp. cremoris M78 (CSC + M78), and all ingredients with the rennet last (CSC + M78-RT). Additional treatments included Listeria-inoculated TGCM samples coinoculated with the NisA+ costarter strain M78 in the absence of the CSC or with the CSC in previously sterilized TGCM to inactivate the background microbiota (CSC-SM). All cultures were incubated at 37 to 42 °C for 6 h, followed by additional 66 h at 22 °C, and 48 h at 12 °C after addition of 2% edible salt. L. monocytogenes failed to grow and declined in all CSC-inoculated treatments after 24 h. In contrast, the pathogen increased by 3.34 and 1.46 log units in the CN-LM and the CSC-SM treatments, respectively, indicating that the background microbiota or the CSC alone failed to suppress it, but they did so synergistically. Supplementation of the CSC with the NisA+ strain M78 did not deliver additional antilisterial effects, because the CSC Streptococcus thermophilus reduced the growth prevalence rates and counteracted the in situ NisA+ activity of the costarter. In the absence of the CSC, however, strain M78 predominated and caused the strongest in situ nisin-A mediated effects, which resulted in the highest listerial inactivation rates after 24 to 72 h at 22 °C. In all TGCM treatments, however, L. monocytogenes displayed a "tailing" survival (1.63 to 1.96 log CFU/mL), confirming that this pathogen is exceptionally tolerant to cheese-related stresses, and thus, can't be easily eliminated.

PFGE as a tool to track Listeria monocytogenes in food processing facilities: case studies

The use of PFGE to track Listeria monocytogenes strains from the food processing environment to the food is explained in this chapter through two case studies. This illustrates the usefulness of this method to identify putative routes of contamination and persistent strains and to help in the implementation of corrective actions in food processing facilities to control the occurrence of this pathogenic bacterium.

Modeling the growth of Listeria monocytogenes on the surface of smear- or mold-ripened cheese

Surface-ripened cheeses are matured by means of manual or mechanical technologies posing a risk of cross-contamination, if any cheeses are contaminated with Listeria monocytogenes. In predictive microbiology, primary models are used to describe microbial responses, such as growth rate over time and secondary models explain how those responses change with environmental factors. In this way, primary models were used to assess the growth rate of L. monocytogenes during ripening of the cheeses and the secondary models to test how much the growth rate was affected by either the pH and/or the water activity (aw) of the cheeses. The two models combined can be used to predict outcomes. The purpose of these experiments was to test three primary (the modified Gompertz equation, the Baranyi and Roberts model, and the Logistic model) and three secondary (the Cardinal model, the Ratowski model, and the Presser model) mathematical models in order to define which combination of models would best predict the growth of L. monocytogenes on the surface of artificially contaminated surface-ripened cheeses. Growth on the surface of the cheese was assessed and modeled. The primary models were firstly fitted to the data and the effects of pH and aw on the growth rate (μmax) were incorporated and assessed one by one with the secondary models. The Logistic primary model by itself did not show a better fit of the data among the other primary models tested, but the inclusion of the Cardinal secondary model improved the final fit. The aw was not related to the growth of Listeria. This study suggests that surface-ripened cheese should be separately regulated within EU microbiological food legislation and results expressed as counts per surface area rather than per gram.