Modelling the growth, survival and death of microorganisms in foods: the UK Food Micromodel approach

McClure, Peter; Blackburn, C. de W.; Cole, M.; Curtis, Peter S.; Jones, J.E.; Legan, J.D.; Ogden, I.D.; Peck, Michael W.; Roberts, T. A.; Sutherland, Jane P.; Walker, Samarpita

doi:10.1016/0168-1605(94)90156-2

Cited by 161 publications

(56 citation statements)

References 4 publications

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“…Although there are several software products available that are related to microbial growth prediction (25,33; P. Dalgaard, personal communication), these cannot be applied to the product tested in the present study (due to different microorganisms or the absence of a lag-time model). Such a software could be used as a practical tool in fish quality assurance and improve significantly the distribution and marketing in the fish industry.…”

Section: Resultsmentioning

confidence: 77%

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

Koutsoumanis

2001

Appl Environ Microbiol

165

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The behavior of the natural microflora of Mediterannean gilt-head seabream (Sparus aurata) was monitored during aerobic storage at different isothermal conditions from 0 to 15°C. The growth data of pseudomonads, established as the specific spoilage organisms of aerobically stored gilt-head seabream, combined with data from previously published experiments, were used to model the effect of temperature on pseudomonad growth using a Belehradek type model. The nominal minimum temperature parameters of the Belehradek model (T min ) for the maximum specific growth rate ( max ) and the lag phase (t Lag ) were determined to be ؊11.8 and ؊12.8°C, respectively. The applicability of the model in predicting pseudomonad growth on fish at fluctuating temperatures was evaluated by comparing predictions with observed growth in experiments under dynamic conditions. Temperature scenarios designed in the laboratory and simulation of real temperature profiles observed in the fish chill chain were used. Bias and accuracy factors were used as comparison indices and ranged from 0.91 to 1.17 and from 1.11 to 1.17, respectively. The average percent difference between shelf life predicted based on pseudomonad growth and shelf life experimentally determined by sensory analysis for all temperature profiles tested was 5.8%, indicating that the model is able to predict accurately fish quality in real-world conditions.Fresh fish are among the most perishable food products, and the monitoring and controlling of fish quality is one of the main goals in the fish industry. Fish shelf life is influenced by a number of factors, such as initial microbiological quality, season, handling, and feeding (17,37,40,41) and consequently can vary significantly from batch to batch. The limited and variable shelf lives of fish are major problems for fish quality assurance. This is the reason for the extensive research which has been carried out in the last few decades on the development of direct product methods (microbial, sensory, and biochemical) for the evaluation of fish spoilage (14,15,16). Nevertheless, several problems are related to the use of these methods mainly due to time and sensitivity limitations. An alternative to direct product testing is predictive microbiology. Predictions of food quality can improve significantly distribution and marketing, especially for chilled foods such as fish (28).Application of mathematical modeling for shelf life prediction requires sufficient knowledge of the product spoilage mechanisms (20). In the case of fish and fish products, spoilage is caused by a fraction of the total fish microflora, the specific spoilage organisms (SSO) (16). Since temperature is one of the most important factors influencing microbial growth, modeling the growth of the SSO as a function of temperature is essential in shelf life prediction. Although a large number of models for the prediction of growth of spoilage organisms at various temperatures have been developed, the majority of these studies have been carried out under constant cond...

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Section: Resultsmentioning

confidence: 77%

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

Koutsoumanis

2001

Appl Environ Microbiol

165

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“…Although many predictive models have been developed with data derived from experiments with pure pathogenic cultures in liquid media or simulated food systems (12,20,36), recent studies have started to evaluate growth kinetics of bacterial pathogens in mixed cultures with spoilage bacteria such as pseudomonads and lactic acid bacteria (LAB) (8,9,16,18). In addition, specific models have been proposed, designed, evaluated or revalidated to predict growth of mixed microbial populations in meat and other foods (1,7,28,49,51).…”

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confidence: 99%

Influence of the Natural Microbial Flora on the Acid Tolerance Response of Listeria monocytogenes in a Model System of Fresh Meat Decontamination Fluids

Samelis¹,

Sofos²,

Kendall

et al. 2001

Appl Environ Microbiol

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Depending on its composition and metabolic activity, the natural flora that may be established in a meat plant environment can affect the survival, growth, and acid tolerance response (ATR) of bacterial pathogens present in the same niche. To investigate this hypothesis, changes in populations and ATR of inoculated (10 5 CFU/ml) Listeria monocytogenes were evaluated at 35°C in water (10 or 85°C) or acidic (2% lactic or acetic acid) washings of beef with or without prior filter sterilization. The model experiments were performed at 35°C rather than lower (<15°C) temperatures to maximize the response of inoculated L. monocytogenes in the washings with or without competitive flora. Acid solution washings were free (<1.0 log CFU/ml) of natural flora before inoculation (day 0), and no microbial growth occurred during storage (35°C, 8 days). Inoculated L. monocytogenes died off (negative enrichment) in acid washings within 24 h. In nonacid (water) washings, the pathogen increased (approximately 1.0 to 2.0 log CFU/ml), irrespective of natural flora, which, when present, predominated (>8.0 log CFU/ml) by day 1. The pH of inoculated water washings decreased or increased depending on absence or presence of natural flora, respectively. These microbial and pH changes modulated the ATR of L. monocytogenes at 35°C. In filter-sterilized water washings, inoculated L. monocytogenes increased its ATR by at least 1.0 log CFU/ml from days 1 to 8, while in unfiltered water washings the pathogen was acid tolerant at day 1 (0.3 to 1.4 log CFU/ml reduction) and became acid sensitive (3.0 to >5.0 log CFU/ml reduction) at day 8. These results suggest that the predominant gram-negative flora of an aerobic fresh meat plant environment may sensitize bacterial pathogens to acid.

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“…O verdadeiro poder das abordagens feitas pela microbiologia preditiva é que, ao contrário do processo tradicional de armazenamento, os modelos, uma vez validados, podem ser utilizados para predizer com rapidez e segurança a resposta dos micro-organismos sob várias condições, dentro dos limites investigados para temperatura, pH e concentração de sal. Isto faz com que a microbiologia preditiva seja considerada uma ferramenta preciosa para os microbiologistas de alimentos na tomada de decisão diária (McClure et al, 1994).…”

Section: Introductionunclassified

Estudo Do Crescimento De Salmonella Em Ovos Líquidos Refrigerados a Diferentes Temperaturas

Decker¹,

Galvão²,

Robazza³

2015

Anais Do XX Congresso Brasileiro De Engenharia Química

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RESUMO -A preocupação com uma alimentação saudável vem aumentando à medida que cresce a população. A ausência de microrganismos patogênicos nos alimentos é uma exigência de órgãos de fiscalização. Entre estes, a Salmonella é uma constante fonte de preocupações para a indústria. O objetivo deste trabalho foi estudar através de um modelo matemático o crescimento de Salmonella em ovos líquidos armazenados a diferentes temperaturas. Os dados de crescimento foram obtidos para temperaturas entre 10 0 C e 35 0 C. O ajuste dos dados ao modelo de Baranyi foi feito com o DMFit comumente usado neste tipo de análise. Verificou-se que a taxa de crescimento especifico cresceu com o aumento da temperatura. O valor obtido à 35ºC foi 21 vezes superior ao obtido para 10ºC. O período de adaptação diminuiu com a temperatura. A dependência da vida útil com a temperatura é bem acentuada, com valores bem baixos para altas temperaturas. Este tipo de análise pode fornecer informações úteis para o consumo de alimentos.

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Modelling the growth, survival and death of microorganisms in foods: the UK Food Micromodel approach

Cited by 161 publications

References 4 publications

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

Influence of the Natural Microbial Flora on the Acid Tolerance Response of Listeria monocytogenes in a Model System of Fresh Meat Decontamination Fluids

Estudo Do Crescimento De Salmonella Em Ovos Líquidos Refrigerados a Diferentes Temperaturas

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