Predictive Modeling of Mixed Microbial Populations in Food Products: Evaluation of Two-species Models

Vereecken, K.M; Dens, E; Impe, Jan Van

doi:10.1006/jtbi.2000.2046

Cited by 57 publications

(31 citation statements)

References 21 publications

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“…However, since their studies were mathematical simulations, one needs to evaluate whether their model is applicable to real microbial competition with microbial data. Similar studies were also reported by other researchers Powell et al, 2004;Vereecken et al, 2000. Liu et al 2006 studied interactions of microorganisms on pork with the modified Gompertz model coupled with a modified LV model.…”

Section: Introductionsupporting

confidence: 82%

Development of a Competition Model for Microbial Growth in Mixed Culture

2014

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A novel competition model for describing bacterial growth in mixed culture was developed in this study. Several model candidates were made with our logistic growth model that precisely describes the growth of a monoculture of bacteria. These candidates were then evaluated for the usefulness in describing growth of two competing species in mixed culture using Staphylococcus aureus, Escherichia coli, and Salmonella. Bacterial cells of two species grew at initial doses of 10 Biocontrol Science, 2014, Vol. 19, No. 2, 61 71 Here N is the microbial population at time t. N max and N min correspond to the maximum and the initial cell populations, respectively. m and n are the parameters for the curvature of the deceleration phase and the period of the lag phase, respectively Fujikawa, 2010 .The NL model has been shown to describe and predict microbial growth in broth and on agar plates successfully at constant and dynamic temperatures Fujikawa et al., 2003 andFujikawa and Morozumi, 2005 . It could further predict the amount of s t a p h y l o c o c c a l e n t e r o t o x i n A p r o d u c e d b y Staphylococcus aureus in milk and a blue pigment caused by Pantoea agglomerans on agar plate Fujikawa and Morozumi, 2006;Fujikawa and Akimoto, 2011 . We now consider the NL model to be a

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Section: Introductionsupporting

confidence: 82%

Development of a Competition Model for Microbial Growth in Mixed Culture

2014

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“…However, since their studies were mathematical simulations, it is necessary to evaluate whether their model is applicable to real microbial competition with microbial data. Similar studies were also reported by other researchers Powell et al, 2004;Vereecken et al, 2000. Liu et al 2006 studied interactions of microorganisms on pork with the modified Gompertz model coupled with a modified LV model.…”

Section: Development Of Competition Modelssupporting

confidence: 82%

Prediction of Competitive Microbial Growth

Fujikawa

2016

Biocontrol Sci.

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Prediction of competitive microbial growth is becoming important for microbial food safety. There would be two approaches to predict competitive microbial growth with mathematical models. The first approach is the development of a growth model for competitive microbes. Among several candidates for the competition model considered, the combination of the primary growth model of the new logistic NL model and the competition model of the Lotka-Vorttera LV model showed the best performance in predicting microbial competitive growth in the mixed culture of two species. This system further successfully predicted the growth of three competitive species in mixed culture. The second approach is the application of the secondary model especially for the parameter of the maximum cell population in the primary growth model. The combination of the NL model and a polynomial model for the maximum population successfully predicted Salmonella growth in raw ground beef. This system further successfully predicted Salmonella growth in beef at various initial concentrations and temperatures. The first approach requires microbial growth data in monoculture for analysis. The second approach to the prediction of competitive growth from the viewpoint of microbial food safety would be more suitable for practical application.

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“…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). This may indicate an increasing scientific interest in the effects of microbial competition on food preservation and safety.…”

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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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Predictive Modeling of Mixed Microbial Populations in Food Products: Evaluation of Two-species Models

Cited by 57 publications

References 21 publications

Development of a Competition Model for Microbial Growth in Mixed Culture

Development of a Competition Model for Microbial Growth in Mixed Culture

Prediction of Competitive Microbial Growth

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

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