Assessment of Mathematical Models for Predicting Staphylococcus aureus Growth in Cooked Meat Products

Castillejo-Rodríguez, A. M.; Gimeno, Rosa María García; Cosano, G. Zurera; Alcalá, E. Barco; Pérez, M. R. Rodríguez

doi:10.4315/0362-028x-65.4.659

Cited by 39 publications

(21 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…108 However, predictive models cannot be used as the sole determinant of product safety. 109 If important decisions are to be made based on the results of a predictive model it must be validated in the food of interest.…”

Section: Risk Assessment and Predicitive Microbiology To Control Entementioning

confidence: 99%

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

et al. 2011

View full text Add to dashboard Cite

Section: Risk Assessment and Predicitive Microbiology To Control Entementioning

confidence: 99%

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

et al. 2011

View full text Add to dashboard Cite

“…Data used in building a model are usually acquired in laboratory media. However, the predictions agree more or less successfully with observations of food products (6,36), and validation of the model proves to be necessary in such cases. Salter et al (31) underlined the importance of good prediction for food safety, although their model was not validated in their paper.…”

mentioning

confidence: 80%

Development and Validation of Experimental Protocols for Use of Cardinal Models for Prediction of Microorganism Growth in Food Products

Pinon

Zwietering

Perrier

et al. 2004

Appl Environ Microbiol

View full text Add to dashboard Cite

An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (a w ) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food-and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10°C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20°C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, SymPrevius, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.Predictive microbiology has proven its value for a useful model-based description of microbial growth in foods ever since its development (18,19). Data used in building a model are usually acquired in laboratory media. However, the predictions agree more or less successfully with observations of food products (6, 36), and validation of the model proves to be necessary in such cases. Salter et al. (31) underlined the importance of good prediction for food safety, although their model was not validated in their paper. Indeed, models should be validated for prediction in the product in question, to allow for risk assessment (26). This is all the more important when creating a software application (9, 16), such as the French national program of predictive microbiology, SymЈPrevius (14). In this research program, industry, public, university, and technical center laboratories first constructed a parameter database covering 50 bacterial strains of five species grown in laboratory medium (20,21). The pathogenic bacteria selected were Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella. This initial work led to a model describing growth rates versus temperature, pH, and water activity.The objective of this study was to develop a methodology to use these first results in foods. Thus, challenge tests were carried out, and then kinetics were analyzed to (i) obtain medium-dependent parameters and (ii) validate complete models. Since temperature is the major factor of interest in the food industry (18), the studies reported focused on that aspect. MATERIALS AND METHODSPrevio...

show abstract

“…The mean lag time predicted by PMP was 19.0 h (95% CI of 14.2 to 25.5 h), which is longer than that estimated by the BP dec method (10.4 h [ Table 4]). In comparison, Castillejo-Rodriguez et al (8) reported a lag time of 40 h for S. aureus in a cooked chicken meat product stored at 17.7°C.…”

Section: Discussionmentioning

confidence: 99%

“…Experiments were performed at 17°C in a broth simulating the reported properties of a cooked cured chicken meat product (8), chicken broth. The chicken broth was prepared using brain heart infusion (BHI) broth (Difco) as a base with pH, water activity (a w ), and concentrations of salts adjusted to the values reported previously (8). To achieve the desired a w , glycerol was used in addition to NaCl as a humectant.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Staphylococcus aureus Growth Parameters from Turbidity Data: Characterization of Strain Variation and Comparison of Methods

Lindqvist

2006

Appl Environ Microbiol

View full text Add to dashboard Cite

Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17°C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.

show abstract

Assessment of Mathematical Models for Predicting Staphylococcus aureus Growth in Cooked Meat Products

Cited by 39 publications

References 41 publications

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

Development and Validation of Experimental Protocols for Use of Cardinal Models for Prediction of Microorganism Growth in Food Products

Estimation of Staphylococcus aureus Growth Parameters from Turbidity Data: Characterization of Strain Variation and Comparison of Methods

Contact Info

Product

Resources

About