Hurdle Technology Applied to Meat Products of the Shelf Stable Product and Intermediate Moisture Food Types

Leistner, L.

doi:10.1007/978-94-009-5103-7_19

Cited by 77 publications

(62 citation statements)

References 2 publications

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“…DISCUSSION Probability or growth/no growth interface models are of particular interest in managing the safety of foods which occasionally may be contaminated with pathogens and for which subsequent growth of the pathogen would increase the risk of food-borne illness. Definition of all combinations of environmental factors that prevent growth of L. monocytogenes in foods will, in effect, quantify the Hurdle Concept (23,24). The data underlying the growth/no growth models presented here are based on an observation period of 90 days to ensure that if growth were possible it would have been detected.…”

Section: Model Performance Goodness Of Fitmentioning

confidence: 99%

Growth Limits of Listeria monocytogenes as a Function of Temperature, pH, NaCl, and Lactic Acid

Tienungoon

Ratkowsky

McMeekin

et al. 2000

Appl Environ Microbiol

218

111

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Models describing the limits of growth of pathogens under multiple constraints will aid management of the safety of foods which are sporadically contaminated with pathogens and for which subsequent growth of the pathogen would significantly increase the risk of food-borne illness. We modeled the effects of temperature, water activity, pH, and lactic acid levels on the growth of two strains of Listeria monocytogenes in tryptone soya yeast extract broth. The results could be divided unambiguously into "growth is possible" or "growth is not possible" classes. We observed minor differences in growth characteristics of the two L. monocytogenes strains. The data follow a binomial probability distribution and may be modeled using logistic regression. The model used is derived from a growth rate model in a manner similar to that described in a previously published work (K. A. Presser, T. Ross, and D. A. Ratkowsky, Appl. Environ. Microbiol. 64:1773-1779, 1998). We used "nonlinear logistic regression" to estimate the model parameters and developed a relatively simple model that describes our experimental data well. The fitted equations also described well the growth limits of all strains of L. monocytogenes reported in the literature, except at temperatures beyond the limits of the experimental data used to develop the model (3 to 35°C). The models developed will improve the rigor of microbial food safety risk assessment and provide quantitative data in a concise form for the development of safer food products and processes.Predictive microbiology combines mathematical modeling with experimental data on combinations of factors that influence the growth of food spoilage and/or food-borne pathogenic microorganisms. The models developed are intended to predict the fate of microorganisms in foods. Since the experimental data are usually derived from studies using laboratory media, the models must be validated with data collected under conditions under which food products are customarily stored.Predictive microbiology models can be divided into kinetic models and probability models. With the former type, one calculates the microbiological life of food products, i.e., the period of time during which the number of microorganisms in the food is less than a specified value. With the latter type, one determines whether a microorganism can grow and identifies storage conditions with a low or nil probability of growth.Kinetic and probability models may be closely related, because the probability of detectable growth within a specified time period depends on germination, lag, and generation times, i.e., on kinetic parameters. In some cases, a probability model may be derived from a kinetic model by some simple mathematical transformations. For example, in references 33, 35 and 41, a kinetic model was transformed into a probability model by taking the natural logarithm of both sides of the original equation and then replacing one side with the "logit" of a probability, i.e., ln [P/(1 Ϫ P)], where P is the probability that growth occurs...

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Section: Model Performance Goodness Of Fitmentioning

confidence: 99%

Growth Limits of Listeria monocytogenes as a Function of Temperature, pH, NaCl, and Lactic Acid

Tienungoon

Ratkowsky

McMeekin

et al. 2000

Appl Environ Microbiol

218

111

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“…Geleneksel gıda muhafaza işlemlerine alternatif olarak geliştirilen yeni kombine yöntemlerde aw'nin azaltılması ile birlikte pH düşürülmesi, hafif ısıtma, koruyucu kullanımı vb. gibi koruyucu etkenlerin bir arada ve düşük oranda kullanılmasıyla gıdanın orijinal niteliklerinin çok az değiştiği ürünler elde edilmektedir [8]. Kurutulmuş ürünlerde de yaş ürün ile arasında tat ve dokuda az fark olan 'yarı-kurutulmuş' veya 'orta nemli' ürünler, son yıllarda tüketici tercihleri açısından önem kazanmıştır.…”

Section: Yarı Kurutulmuş Gıdalarunclassified

Semi-Dried Fruits and Vegetables

Seçkin¹,

Taşeri²

2015

Pamukkale J Eng Sci

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“…The counts of Staphylococcus aureus are worthy of note due to the fact that it is a facultatively anaerobic halotolerant bacteria that is able to overcome the obstacles of the Theory of Leistner obstacles (Leistner, 1985) and because it can produce thermostable enterotoxin, which once present in foodstuff, is capable of resisting to conventional thermal processing techniques. According to several authors (Sperber, 1983;Bergdoll, 1989), a minimum value water activity ranging from 0.93 and 0.86 is necessary to preclude the production of this enterotoxin.…”

Section: The Values Shown Inmentioning

confidence: 99%