Modeling Salmonella (S. Typhimurium ATCC14028, ATCC 13311, S. Typhi ATCC 19430, and S. enterica) and Listeria (L. monocytogenes Scott A, ATCC 7644, and CDBB-B-1426) cocktails’ survival under the effects of pH, protein, and essential oil concentration

A response surface Bok–Behnken design was utilized to determine the effect of pH (6.5, 6.0, or 5.5), soy protein isolate (SPI: 10.0%, 11.5%, or 13.0% wt/wt), and thyme or oregano essential oil (EO) concentrations (0.22%, 0.25%, or 0.28% wt/wt) on the survival of Salmonella or Listeria cocktails; pH and protein values were chosen according to what is expected for deli meats classified as commercial or economical. Significant coefficients (p < 0.05) describing studied microorganism’s response were identified through polynomial regression models. EOs (0.25% wt/wt) were able to reduce bacterial populations by 2-log cycles at SPI concentrations from 10% to 12.5% (wt/wt) and pHs from 5.5 to 6.5 while 5-log reductions were achieved in media with highest tested EO concentrations and lowest tested SPI concentration at pH 5. Bacterial growth/no-growth interfaces and growth probabilities under selected combinations of tested factors (including inoculation levels, 3 to 8 log CFU/ml) were determined by means of binary logistic regressions; at pH 6.3, 0.28% (wt/wt) of EO is able to control bacterial growth (probability of growth < 0.1) at every tested SPI level.

Practical applications

Since ready-to-eat (RTE) protein-based products like cold cuts and deli meats may be consumed without an additional cooking or heating step, they have been implicated in the risk of diseases transmitted by the consumption of these types of foods.

This work highlights the importance of modeling the survival of Salmonella or Listeria cocktails in order to predict the impact of pH, soy protein isolate, and thyme or oregano essential oil (EO) concentrations. This information can be utilized as a guideline to produce economical deli meats that are safe as well as to establish the lower limits of EO concentration for further testing in real food matrices.

Additionally, our results emphasize the importance of the use of food model media to assess by means of predictive microbiology the antimicrobial potential of EOs previous to apply them in real food systems.