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.

The effects and interactions of temperature (12–45C), initial pH (4.5–9.0), NaCl (0.5–16.5%), and sodium nitrite (1–200 μg/ml) on the aerobic and anaerobic growth of Staphylococcus aureus 196E were studied using 50 ml portions of Brain Heart Infusion Broth in 250ml unsealed and sealed trypsinizing flasks, respectively. The flasks were inoculated to a level of approximately 103 cfu/ml, incubated on a rotary shaker, sampled periodically, and enumerated on Tryptic Soy Agar. Growth curves were generated by fitting the data to the Gompertz function using nonlinear regression analysis. The general growth characteristics of S. aureus in response to the five environmental variables were similar to those observed by other investigators including (1) enhanced growth in the presence of oxygen, (3) ability to grow at high sodium chloride concentrations, and (3) dependence of the bacteriostatic activity of sodium nitrite on pH and oxygen availability. Supplemental studies indicated that growth kinetics were independent of inoculum size, which allowed the Gompertz A term to be treated as a constant. However, the maximum population density (MPD) achieved by the cultures was dependent on the independent variables, requiring that it be modeled in addition to the Gompertz B and M terms. The MPD was then used to calculate the Gompertz C term. Quadratic and cubic response surface models were generated using various data transformations. Quadratic models using and LN‐transformation provided reasonable predictions of the effects of the four variables on the growth kinetics of S. aureus, and should prove useful for providing initial estimates of the behavior of S. aureus in foods.

A time‐resolved fluorescence technique was developed to detect Escherichia coli O157:H7 in ground beef burger. After a 4.5 h enrichment period, streptavidin coated magnetic beads conjugated with biotin‐labeled anti E. coli O157:H7 were used to capture the bacteria. The bacteria were, at the same time, also labeled by a nonfluorescent, europium (Eu)‐tagged anti‐E. coli O157:H7 antibody. The sandwiched bacterial complexes were then concentrated using a magnetic particle concentrator and washed to remove other solution components. Upon addition of an enhancement buffer, the Eu‐labels were then released from the antibodies and chelated to nitrilo‐triacetic acid (NTA) and trioctylphosphine oxide (TOPO) to form highly fluorescent Eu‐(2‐NTA)3(TOPO)2–3 micellar complexes. Delayed fluorescence associated with these complexes was measured and its intensity was used to estimate the original bacterial concentration spiked in hamburger. This approach was applied to detect E. coli O157:H7 spiked in hamburgers. The results indicated this method is able to detect 1 CFU/g of the bacteria after a brief enrichment for four and half hours at 37C. Specificity studies indicated that the approach exhibited no or limited cross reactivity to Salmonella typhimurium, E. coli K‐12 or Shigella dysenteriae spiked in hamburgers. Thus, the developed approach may be used as a rapid screening procedure for E. coli O157 bacteria in foods.

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The effects and interactions between pH and CitriC acid concentration on the inactivation of Listeria monocytogenes was determined using a three-strain mixture. Citric acid/sodium citrate combinations were added to brain heart infusion (BHI) broth to achieve concentrations of 0.1, 0.5, 1.0 and 2.0 M in conjunction with pH values of 4, 5, 6 and 7. The media were dispensed in 20-ml portions in dilution bottles, inoculated to approximately 108 CFU/ml, and incubated at 28°C. Survivor curves were generated using a linear model incorporating a lag term, and D-values and “time to 4-D inactivation” values were calculated. The results were compared against control cultures in which the pH was modified using hydrochloric acid (HCI). The rate of inactivation was dependent on both the pH and concentration of citric acid. Low levels of citric acid were protective, particularly at pH 5 and 6. At higher concentrations, a distinct anion effect was observed as compared to the HCl controls, with inactivation rates being correlated with the completely undissociated form of the acid. Comparison of the kinetic data with earlier results with lactic and acetic acids suggests that citric acid has both protective and bactericidal activity against L. monocytogenes, which involve different modes of action.

. 1997. Previously developed four-variable response surface models for describing the effects of temperature, pH/lactic acid, sodium chloride and sodium nitrite on the time to achieve a 4-log, non-thermal inactivation (t4,,) of Listeria monoqytogenes in aerobic, acidic environments were expanded to five-variable models that distinguish the effects of pH and acidulant concentration. A total of 18 new variable combinations were evaluated and the inactivation kinetics data appended onto a consolidation of two data sets from earlier studies. The consolidated data set, which included 3 15 inactivation curves representing 209 unique combinations of the five variables, was analysed by response surface analysis.T h e quadratic model without backward elimination regression was selected for further evaluation. Three additional quadratic models were generated using the concentrations of undissociated lactic and/or nitrous acids as variables in place of percentage lactic acid and sodium nitrite concentration. Comparison of predicted t4,, values against literature values for various food systems indicated that the models provide reasonable initial estimates of the inactivation of L. monocytogenes. The models based on the concentration of undissociated lactic and nitrous acids support the hypothesis that antimicrobial activity is associated with this form of the compounds. Evaluation of several examples suggests that these models may be useful for predicting the equivalent of the compounds' 'minimal inhibitory concentrations' for accelerating inactivation under various conditions.