Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration

Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6....

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Autor principal: López-Malo, A.
Otros Autores: Guerrero, S., Alzamora, S.M
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: IAMFES 2000
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Acceso en línea:Registro en Scopus
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100 1 |a López-Malo, A. 
245 1 0 |a Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration 
260 |b IAMFES  |c 2000 
270 1 0 |m Lopez-Malo, A.; Depto. Ingenieria Quimica/Alimentos, Universidad de las Americas-Puebla, Sta. Catarina Martir, Puebla 72820, Mexico; email: amalo@mail.udlap.mx 
506 |2 openaire  |e Política editorial 
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504 |a Cerrutti, P., (1988) Efectos Combinados de aw, pH, Aditivos y Tratamiento Térmico en el Crecimiento y Supervivencia de Sacchammyces Cerevisiae, , Ph.D. dissertation. Universidad de Buenos Aires. Argentina 
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504 |a Hosmer, D.W., Lemeshow, S., (1989) Applied Logistic Regression, , John Wiley and Sons, New York 
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504 |a McClure, P.J., Cole, M.B., Davies, K.W., An example of the stages in the development of a predictive mathematical model for microbial growth: The effects of NaCl, pH and temperature on the growth of Aeromonas hydrophila (1994) Int. J. Food Microbiol., 23, pp. 359-375 
504 |a McMeekin, T.A., Olley, J.N., Ross, T., Ratkowsky, D.A., (1993) Predictive Microbiology: Theory and Application, , Research Studies Press, Ltd., England 
504 |a Presser, K.A., Ross, T., Ratkowsky, D.A., Modelling the growth limits (growth/no growth interface) of Escherichia coli as a function of temperature, pH, lactic acid concentration, and water activity (1998) Appl. Environ. Microbiol., 64, pp. 1773-1779 
504 |a Ratkowsky, D.A., Ross, T., Modelling the bacterial growth/no growth interface (1995) Lett. Appl. Microbiol., 20, pp. 29-33 
504 |a Razavilar, V., Genigeorgis, C., Prediction of Listeria spp. growth as affected by various levels of chemicals, pH, temperature and storage time in a model broth (1998) Int. J. Food Microbiol., 40, pp. 149-157 
504 |a Ross, T., McMeekin, T.A., Predictive microbiology (1994) Int. J. Food Microbiol., 23, pp. 241-264 
504 |a Schaffner, D.W., Ross, W.H., Montville, T.J., Analysis of the influence of environmental parameters on Clostridium botulinum time-to-toxicity by using three modeling approaches (1998) Appl. Environ. Microbiol., 64, pp. 4416-4422 
504 |a Skinner, G.E., Larkin, J.W., Mathematical modeling of microbial growth: A review (1994) J. Food Saf., 14, pp. 175-217 
504 |a Whiting, R.C., Microbial modeling in foods (1995) Crit. Rev. Food Sci. Nutr., 35, pp. 467-494 
504 |a Whiting, R.C., Buchanan, R.L., Microbial modeling (1994) Food Technol., 48, pp. 113-120 
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520 3 |a Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6.0, 5.0, 4.0, and 3.0), and potassium sorbate (0, 50, 100, 200, 500, and 1,000 ppm). The proposed model predicts the probability of growth under a set of conditions and calculates critical values of a(w), pH, and potassium sorbate concentration needed to inhibit yeast growth for different probabilities. The reduction of pH increased the number of combinations of a(w) and potassium sorbate concentration with probabilities to inhibit yeast growth higher than 0.95. With a probability of growth of 0.05 and using the logistic models, the critical pH values were higher for 50 h of incubation than those required for 350 h. With lower a(w) values and increasing potassium sorbate concentration the critical pH values increased. Logistic regression is a useful tool to evaluate the effects of the combined factors on microbial growth.  |l eng 
593 |a Depto. de Ing. Quim. y Alimentos, Univ. de las Américas-Puebla, Sta. Catarina Martir, Puebla, 72820, Mexico 
593 |a Departamento de Industrias, Fac. de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina 
690 1 0 |a FUNGAL GROWTH 
690 1 0 |a GROWTH INHIBITION 
690 1 0 |a GROWTH PERIOD 
690 1 0 |a MATHEMATICAL MODEL 
690 1 0 |a REGRESSION ANALYSIS 
690 1 0 |a SORBATE POTASSIUM 
690 1 0 |a SACCHAROMYCES CEREVISIAE 
690 1 0 |a SACCHAROMYCES CEREVISIAE 
650 1 7 |2 spines  |a PH 
700 1 |a Guerrero, S. 
700 1 |a Alzamora, S.M. 
773 0 |d IAMFES, 2000  |g v. 63  |h pp. 91-95  |k n. 1  |p J. Food Protection  |x 0362028X  |w (AR-BaUEN)CENRE-324  |t Journal of Food Protection 
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