Scientific Publications - Work Done by Microbiology Reader Bioscreen C

E. Dengremont and J.M. Membre, Statistical Approach for Comparison of the Growth Rates of Five Strains of Staphylococcus aureus, Applied and Environmental Microbiology, Dec. 1995, p. 4389-4395

ABSTRACT

The interaction of temperature (10, 14, 25, 31, and 37°C), pH (pH 5, 5.6, 6.5, 7.4, and 8), and NaCl (0, 2, 5, 8, and 10%) in a laboratory medium affects the specific growth rate of Staphylococcus aureus. From growth curves obtained by the turbidimetric technique, a nonlinear model in which the specific growth rate (µ) is fitted directly, without data transformation and with the residual error variations taken into account, is proposed. This model correctly fits experimental data and gives more biological information than the quadratic polynomial model. Moreover, the comparison of five strains of S. aureus was performed by a principal-component analysis in which the specific growth rate was the identifying characteristic for S. aureus strains. The results obtained from model coefficient comparison among the five strains and from multivariate data analysis allow the same classification of strains to be performed. Two of them have similar behaviors during food spoilage, two others could be distinguished by their capacity to grow at a low temperature, whereas the last one was markedly different from the others.

INTRODUCTION

Staphylococcus aureus is recognized as a cause of food poisoning which occurs after an initial contamination of food with a toxigenic strain. The growth may occur in the absence of enterotoxin synthesis (17) and could have a role in the pathogenicity of some other staphylococcal diseases. In order to quantify the growth of S. aureus during storage at low temperature and to include the influence of pH and NaCl concentration on the shelf life of a food product, we have chosen a predictive microbiological approach. This method uses mathematical equations to estimate the specific growth rate (m) as affected by storage conditions (3). Among the various models proposed, the response surface methodology technique is widely employed in food microbiology (4, 7). This method allows the estimation of m by modelling growth curves and leads to the establishment of a linear model which describes log m as a function of temperature, pH, and water activity. Recently, Sutherland et al. (15) proposed a polynomial model to predict the growth of S. aureus, but they noted that there was an important lack of fit between the published results and those predicted by the model. In order to predict S. aureus food spoilage, Broughall and Brown (2) used the nonlinear Arrhenius equation with constants describing the enthalpy of activation for microbial growth and low-temperature inactivation, which are linear functions of pH and water activity. Moreover, a nonlinear model was developed (20) and applied to predict m when several factors act in combination for several microorganisms, but not for S. aureus. Basing our work upon this approach, we propose a nonlinear model in this paper. The contribution of this model in comparison with that of a polynomial function is presented. Furthermore, the predictive microbiological technique has been employed, up to now, to describe the behavior of one strain—or a mixture of strains—as a function of environmental factors. It seemed interesting to attempt to generalize its use to the comparative study of several strains. This method allows the contamination of a specific food product by various microorganisms to be appreciated. In order to test whether predictive microbiological models were a suitable statistical tool to carry out comparisons, results obtained with five different strains of S. aureus were reported and compared with those obtained by a multivariate data analysis. This method allows strains to be classified according to their behaviors during food spoilage. Cluster analysis is a statistical method widely employed in taxonomic study, and its contribution to predictive microbiology is discussed.

---------- 8< ----------

REFERENCES

1. Adams, M. R., C. L. Little, and M. C. Easter. 1991. Modelling the effect of pH, acidulant, and temperature on the growth rate of Yersinia enterocolitica. J. Appl. Bacteriol. 71:65–71.

2. Broughall, J. M., and C. Brown. 1984. Hazard analysis applied to microbial growth in foods: development and application of three-dimensional models to predict bacterial growth. Food Microbiol. 1:13–22.

3. Buchanan, R. L. 1993. Predictive food microbiology. Trends Food Sci. Technol. 4:6–11.

4. Buchanan, R. L., and J. G. Phillips. 1990. Response surface model for predicting the effects of temperature pH, sodium chloride content, sodium nitrite concentration and atmosphere on the growth of Listeria monocytogenes. J. Food Prot. 53:370–376.

5. Chirife, J. 1994. Specific solute effects with special reference to Staphylococcus aureus. J. Food Eng. 22:409–419.

6. Davey, K. R. 1989. A predictive model for combined temperature and water activity on microbial growth during the growth phase. J. Appl. Bacteriol. 67:483–488.

7. Gibson, A. M., N. Bratchell, and T. A. Roberts. 1988. Predicting microbial growth: growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature. Int. J. Food Microbiol. 6:155–178.

8. Halpin-Dohnalek, M. I., and E. H. Marth. 1989. Staphylococcus aureus: production of extracellular compounds and behaviour in foods—a review. J. Food Prot. 52:267–282.

9. Huet, S., E. Jolivet, and A. Messe´an (ed.). 1992. La re´gression non-line´aire, me´thodes et applications en biologie. Institut National de la Recherche Agronomique, Paris.

10. Membre, J. M., and P. M. Burlot. 1994. Effects of temperature, pH, and NaCl on growth and pectinolytic activity of Pseudomonas marginalis. Appl. Environ. Microbiol. 60:2017–2022.

11. Notermans, S., and C. J. Heuvelman. 1983. Combined effect of water activity, pH and sub-optimal temperature on growth and enterotoxin production of Staphylococcus aureus. J. Food Sci. 48:1832–1840.

12. Schoolfield, R. M., P. J. H. Sharpe, and C. E. Magnuson. 1981. Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J. Theor. Biol. 88:719–731.

13. Smith, J. L., R. L. Buchanan, and S. A. Palumbo. 1983. Effect of food environment on staphylococcal enterotoxin synthesis: a review. J. Food Prot. 46:545–555.

14. Smith, J. L., M. J. Maurer, M. M. Bencivengo, and C. A. Kunsch. 1987. Effect of sodium chloride on uptake of substrate by Staphylococcus aureus 196E. J. Food Prot. 50:968–974.

15. Sutherland, J. P., A. J. Bayliss, and T. A. Roberts. 1994. Predictive modelling of growth of Staphylococcus aureus: the effects of temperature, pH and sodium chloride. Int. J. Food Microbiol. 21:217–236.

16. Tatini, S. R. 1973. Influence of food environments on growth of Staphylococcus aureus and production of various enterotoxins. J. Milk Food Technol. 36:559–563.

17. Varnam, A. H., and M. G. Evans. 1991. Foodborne pathogens, p. 235–265. Wolfe Publishing Ltd., BPCC Hazell Books, Aylesbury, England.

18. Willcox, F., M. Mercier, M. Hendrickx, and P. Tobback. 1993. Modelling the influence of temperature and carbon dioxide upon the growth of Pseudomonas fluorescens. Food Microbiol. 10:159–173.

19. Zwietering, M. H., I. Jongenburger, F. M. Rombouts, and K. Van’t Riet. 1990. Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 56:1875–1881.

20. Zwietering, M. H., T. Wijtzes, J. C. De Wit, and K. Van’t Riet. 1992. A decision support system for prediction of the microbial spoilage in foods. J. Food Prot. 55:973–979.

© 2005 Transgalactic Ltd (manufacturer of Bioscreen C software) | Privacy Statement | P.O. Box 1393, 00101 Helsinki, Finland, phone: +358 9 85172920, fax: +358 9 8749481, e-mail: microbiology@bionewsonline.com
 

Last modified: May 25, 2005