Modeling the growth boundary of Listeria monocytogenes in ready-to-eat cooked meat products as a function of the product salt, moisture, potassium lactate, and sodium diacetate concentrations

A central composite response surface design was used to determine the time to growth of Listeria monocytogenes as a function of four continuous variables: added sodium chloride (0.8 to 3.6%), sodium diacetate (0 to 0.2%), potassium lactate syrup (60% [wt/wt]; 0.25 to 9.25%), and finished-product moisture (45.5 to 83.5%) in ready-to-eat cured meat products. The design was repeated for ready-to-eat uncured meat products giving a fifth categorical variable for cure status. Products were stored at 4 degrees C. The results were modeled using a generalized regression approach. All five main effects, six two-factor interactions, and two quadratic terms were statistically significant. The model was used to show the boundary between growth and no-growth conditions at 4 degrees C using contour plots of time to growth. It was validated using independent challenge studies of cured and uncured products. Generally, the model predicted well, particularly for cured products, where it will be useful for establishing conditions that prevent the growth of L. monocytogenes. For uncured products, there was good agreement overall between predicted and observed times to growth, but the model is less thoroughly validated than for cured products. The model should initially only be used for screening of formulations likely to prevent growth of Listeria monocytogenes in uncured products, with recommendations subject to confirmation by challenge studies.

Effect of challenge temperature and solute type on heat tolerance of Salmonella serovars at low water activity

Salmonella spp. are reported to have an increased heat tolerance at low water activity (a(w); measured by relative vapor pressure [rvp]), achieved either by drying or by incorporating solutes. Much of the published data, however, cover only a narrow treatment range and have been analyzed by assuming first-order death kinetics. In this study, the death of Salmonella enterica serovar Typhimurium DT104 when exposed to 54 combinations of temperature (55 to 80 degrees C) and a(w) (rvp 0.65 to 0.90, reduced using glucose-fructose) was investigated. The Weibull model (LogS = -bt(n)) was used to describe microbial inactivation, and surface response models were developed to predict death rates for serovar Typhimurium at all points within the design surface. The models were evaluated with data generated by using six different Salmonella strains in place of serovar Typhimurium DT104 strain 30, two different solutes in place of glucose-fructose to reduce a(w), or six low-a(w) foods artificially contaminated with Salmonella in place of the sugar broths. The data demonstrate that, at temperatures of > or =70 degrees C, Salmonella cells at low a(w) were more heat tolerant than those at a higher a(w) but below 65 degrees C the reverse was true. The same patterns were generated when sucrose (rvp 0.80 compared with 0.90) or NaCl (0.75 compared with 0.90) was used to reduce a(w), but the extent of the protection afforded varied with solute type. The predictions of thermal death rates in the low-a(w) foods were usually fail-safe, but the few exceptions highlight the importance of validating models with specific foods that may have additional factors affecting survival.

Modeling the growth boundary of Staphylococcus aureus for risk assessment purposes

Knowing the precise boundary for growth of Staphylococcus aureus is critical for food safety risk assessment, especially in the formulation of safe, shelf-stable foods with intermediate relative humidity (RH) values. To date, most studies and resulting models have led to the presumption that S. aureus is osmotolerant. However, most studies and resulting models have focused on growth kinetics using NaCl as the humectant. In this study, glycerol was used to investigate the effects of a glass-forming nonionic humectant to avoid specific metabolic aspects of membrane ion transport. The experiments were designed to produce a growth boundary model as a tool for risk assessment. The statistical effects and interactions of RH (84 to 95% adjusted by glycerol), initial pH (4.5 to 7.0 adjusted by HC1), and potassium sorbate (0, 500, or 1,000 ppm) or calcium propionate (0, 500, or 1,000 ppm) on the aerobic growth of a five-strain S. aureus cocktail in brain heart infusion broth were explored. Inoculated broths were distributed into microtiter plates and incubated at 37 degrees C over appropriate saturated salt slurries to maintain RH. Growth was monitored by turbidity during a 24-week period. Toxin production was explored by enterotoxin assay. The 1,280 generated data points were analyzed by SAS LIFEREG procedures, which showed all studied parameters significantly affected the growth responses of S. aureus with interactions between RH and pH. The resulting growth/no growth boundary is presented.

The boundary for growth of Zygosaccharomyces bailii in acidified products described by models for time to growth and probability of growth

Models to predict days to growth and probability of growth of Zygosaccharomyces bailii in high-acid foods were developed, and the equations are presented here. The models were constructed from measurements of growth of Z. bailii using automated turbidimetry over a 29-day period at various pH, NaCl, fructose, and acetic acid levels. Statistical analyses were carried out using Statistical Analysis Systems LIFEREG procedures, and the data were fitted to log-logistic models. Model 1 predicts days to growth based on two factors, combined molar concentration of salt plus sugar and undissociated acetic acid. This model allows a growth/no-growth boundary to be visualized. The boundary is comparable with that established by G. Tuynenburg Muys (Process Biochem. 6:25-28, 1971), which still forms the basis of industry assumptions about the stability of acidic foods. Model 2 predicts days to growth based on the four independent factors of salt, sugar, acetic acid, and pH levels and is, therefore, much more useful for product development. Validation data derived from challenge studies in retail products from the U.S. market are presented for Model 2, showing that the model gives reliable, fail-safe predictions and is suitable for use in predicting growth responses of Z. bailii in high-acid foods. Model 3 predicts probability of growth of Z. bailii in 29 days. This model is most useful for spoilage risk assessment. All three models showed good agreement between predictions and observed values for the underlying data.