The effects of pH and lactic acid or acetic acid concentration on Listeria monocytogenes inactivation were studied in brain heart infusion broth using a three strain mixture. Combinations of lactic acid/sodium lactate and acetic acid/sodium acetate were used to achieve concentrations of 0.1, 0.5, 1.0, and 2,0 M in conjunction with pH values of 4.0, 5.0, 6.0, and 7.0. Cultures adjusted with HCl to pH 3.0 to 7.0 in 0.5 pH unit intervals were used as 0.0 M controls. Each pH/concentration combination was inoculated to a level of 108 CFU/ml and incubated at 28°C for up to 60 d. Bacterial populations were determined periodically by plate counts. Inactivation was exponential after an initial lag period. Survivor curves (log# versus time) were fitted using a linear model that incorporated a lag period. The model was subsequently used to calculate D values and “time to a 4-D (99.99%) inactivation” (t4-D); t4-D values were directly related to pH and inversely related to acid concentration. At acid/pH combinations that supported growth, the level of the organism increased slightly (2- to 10-fold) before declining. In the HCl-adjusted controls with pH's ≤5.5, the rate of inactivation was linearly related to pH. In the presence of the monocarboxylic acids, the duration of the lag period and the rate of inactivation were dependent on the pH, as well as the identity and concentration of acid. 4-D inactivation times were related to the level of undissociated lactic and acetic acids. That relationship was described by the equations, t4-D = exp (−0.1773*LA0.5 + 7.3482) and t4-D = exp (−0.1468*AA0.5 + 7.3905) for lactic and acetic acids, respectively, where LA and AA are mM of undissociated acid. These relationships were used in conjunction with the Henderson-Hasselback equation to develop a model for predicting the rate of inactivation as a function of pH and total organic acid concentration.

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The effects of temperature, lactic acid (or pH), sodium chloride, and sodium nitrite on the non-thermal inactivation of a three strain mixture of Listeriu monocytogenes were examined in brain heart infusion broth. A total of 249 survivor curves representing 157 combinations of the four variables were generated. The survivor curves were described mathematically by fitting data using linear and nonlinear primary models. Supplemental studies demonstrated that (1) preculturing the microorganism in an acidic environment or in media containing glucose increased acid tolerance, (2) survivor curve tailing was not due to the presence of a more resistant subpopulation, and (3) the rate of nonthermal inactivation was independent of initial population density. Response surface models were developed for predicting the effects and interactions of the four independent variables on the inactivation of List&u monocytogenes under adverse environmental conditions. INTRODUCTION LIKE OTHER foodborne pathogens, when Listetiu monocytogenes is placed in an adverse environment, it will be inactivated at a rate that is dependent on the severity of the conditions and the physiological characteristics of the species. In general, the environmental and cultural factors that influence the growth of bacteria in foods also influence their inactivation. This includes well recognized parameters such as storage temperature, pH, acidulant identity, water activity, humectant identity, animicrobials, etc. With Listeria monocytogenes, the rate of nonthermal inactivation is dependent on most of these factors (Ahamad and Marth, 1989;El-Shenawy and Marth, 1989;Sorrells et al., 1989;Sorrells and Enigl, 1990;Cole et al., 1990; Buchanan et al., 1993).While the kinetics of microbial inactivation in response to thermal processing has been studied extensively, there has been relatively little reported quantitative data on nonthermal inactivation. Using Lisferia monocytogenes as an example of a relatively hardy, vegetative foodborne pathogen, the objective of our study was to examine quantitatively the effects and interactions of temperature, sodium chloride (water activity), lactic acid (or pH), and sodium nitrite on aerobic inactivation. These data were then used to (1) evaluate the effectiveness of two primary models for depicting survivor curves, and (2) assess the feasibility of developing secondary models for predicting the impact of the same variables on the rate of L. monocytogenes inactivation.