Response Surface Model for Predicting the Effects of Temperature pH, Sodium Chloride Content, Sodium Nitrite Concentration and Atmosphere on the Growth of Listeria monocytogenes

Abstract

A factorial plus supplemental central composite experimental design was used to assess quantitatively the effects and interactions of temperature (5°–37°C), pH (4.5–7.5), NaCl (5–45 g/1), NaNO2 (0–1000 μg/ml), and atmosphere (aerobic vs. anaerobic) on the growth kinetics of Listeria monocytogenes Scott A in Tryptose Phosphate Broth. A total of 709 growth curves were generated, with individual curves fitted using non-linear regression analysis in conjunction with the Gompertz function. The results were analyzed by response surface analysis to generate a cubic model to predict the growth of L. monocytogenes in response to any combination of the variables within the specified ranges. Evaluation of the model indicated that it can be used to provide reasonable “first estimates” of the impact of food formulation and storage conditions on the growth of L. monocytogenes, and can be automated readily to develop “user-friendly” software.

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

“…Accuracy factors varied from 1AE3 to 1AE5 indicating that predicted growth rate were, on average, different from 30 to 50% compared with the observed ones. The cardinal model including interactions #3 proposed by Augustin and Carlier (2000b) underestimated the growth rates with average bias factors greater than 1, this phenomenon was already pointed out by Cornu et al (2003) and Giménez and Dalgaard (2004) in cold-smoked Buchanan et al (1989), Buchanan and Klawitter (1990), Buchanan and Phillips (1990) …”

“…* n.a. et al (1989), Buchanan and Klawitter (1990), Buchanan and Phillips (1990) represented between 10 and 85% of the growth ranges.…”

“…For vacuum packaging, the proportion was set to 0AE3 (Beumer et al 1996). Buchanan and Klawitter (1990), Buchanan and Phillips (1990) 141 66 Farber et al (1989Farber et al ( , 1992 Rosenow and Marth (1987a,b) 30 30 Marshall and Schmidt (1988) 19 19 Ryser and Marth (1988) 21 21 Schaack and Marth (1988) 14 14 Papageorgiou and Marth (1989a) 16 16 Pearson and Marth (1990) 12 12 Walker et al (1990) 12 12 Buchanan and Klawitter (1991) 2 2 El- Gazzar et al (1991) 12 12 Wang and Johnson (1992) 5 5 Zapico et al (1993) 6 6 Bajard (1996 12 12 Murphy et al (1996) 9 9 Cheese Marth (1987, 1989) 16 20 Genigeorgis et al (1991) 121 121 Abdalla et al (1993) 2 2 P. Garry, personal communication 1 1…”

Modelling the growth rate ofListeria monocytogenesin cooked ham stored at different temperatures

“…Certain food categories are no longer under a zero tolerance regime but have a specified maximum allowed concentration of 100 CFU/g or ml for products placed on the market during their shelf-life. Therefore, many studies on modelling the effects of various factors on the growth of L. monocytogenes have been published in recent years (5,18,22,23,35). This research allows the behaviour of L. monocytogenes in RTE meat products to be predicted.…”

Impact of microbial variability on food safety and quality

“…Quantitative microbiology is used, amongst others, as a tool to evaluate the efficacy of certain product designs and thermal processes in controlling the presence and growth of microorganisms to ensure an acceptable level at the end of the shelf life period. The mathematical models and parameters used for prediction are generally established from experimental data obtained in well-defined experimental conditions using laboratory media (Adams et al, 1991;Buchanan and Phillips, 1990;Duh and Schaffner, 1993;Presser et al, 1997;Ross et al, 2003). When growth or inactivation kinetics in an actual food product are investigated, then the prediction is sometimes found to be unrealistic (Pin et al, 1999).…”