Estimation of non-isothermal growth of bacteria Aeromonas hydrophila from isothermal data

Authors

  • Marcos dos Santos Lima IFSertãoPE
  • Páulia Maria Cardoso de Lima Reis IFSertãoPE
  • Gláucia Maria Falcão Aragão UFSC

DOI:

https://doi.org/10.31416/rsdv.v3i2.169

Keywords:

Predictive microbiology, Mathematical modeling, Non-isothermal model

Abstract

In recent years considerable effort has been invested in the development of mathematical models to explain the growth of microorganisms in food products. For these models may be applied in foods stored in real conditions is necessary to consider the effect of changes in variables such as temperature. The objective of this study was to evaluate the methodology proposed by Corradini and Peleg (2005) and Corradini et al. (2006) to obtain a nonisothermal model from isothermal growth data of the bacterium Aeromonas hydrophila, adjusted by Gompertz modified model. The isothermal growth data were obtained from the database ComBase Predictor and secondary models were obtained with the mathematical functions "Power" and "Power 1". The modified Gompertz model fits well to the data of isothermal growth of the bacterium Aeromonas and use of functions "Power" and "Power 1" in the secondary model adjustment showed good results. Was possible to predict the non-isothermal growth of Aeromonas hydrophila bacteria using the methodology proposed by Corradini and Peleg (2005) and Corradini et al. (2006).

References

Azevedo, V. M.; Morita, M.; Dropa, M.;

Cabianca, M. A. A.; Esteves, K. E. E.; Matté,

G. R. Matté, M. H. Ocorrência de Aeromonas

spp. e Vibrio cholerae em Pesque-Pagues da

Região Metropolitana de São Paulo. REVNET

DTA. Vol. 3, No. 4, Julho 2003.

Baranyi, J.; Roberts, T. A., Mathematics of

Predictive Food Microbiology. International

Journal of Food Microbiology, v. 26, p.199-

, 1995.

Bobelyn, E.; Hertog, L. A. T. M. M.; Nicolaï,

B. M. (2006) Applicability of an enzymatic

time temperature integrator as a quality

indicator for mushrooms in the

distributionchain. Postharvest Biology and

Technology 42, 104–114.

Buchanan, R. L. Predictive Microbiology.

Trends Food Science Technology, v.4, p.6-11,

Buchanan, R. L.; Whiting, R. C.; Damert, W.

C. When is simple good enough: A comparison

of the Gompertz, Baranyi, and three-phase

linear models for fitting bacterial growth

curves. Food Microbiology, v.14, p.313-326,

Cayré, M. E.; Vignolo, G.; Garro, O. Modeling

lactic acid bacteria growth in vacuum-packaged

cooked meat emulsion stored at three

temperatures. Food Microbiology, v. 20,

p.561-566, 2003.

Cayré, M. E.; Vignolo, G.; Garro, O. Effect of

storage temperature and gas permeability of packaging film on the growth of lactic acid

bacteria and Brochothrix thermosphacta in

cooked meat emulsions. Food Microbiology,

v. 22, p.505-512, 2005.

COMBASE Predictor – Base de dados de

microrganismos patogênicos. Disponível em:

<http://modelling.combase.cc/ComBase_Predic

tor.aspx> Acessado em 08/09/2011.

Corradini, M. G.; Amezquita, A.; Normand, M.

D.; Peleg, M. Modeling and predicting non-

isothermal microbial growth using general

purpose software. International Journal of

Food Microbiology, v.106, p. 223-228, 2006.

Corradini, M. G.; Peleg, M. Estimating non-

isothermal bacterial growth in foods from

isothermal experiments data. Journal of

Applied Microbiology, v. 99, p 187-200, 2005.

Fujikawa, H.; Kai. A.; Morozumi, S.; A new

logistic model for Escherichia coli growth at

constant and dynamic temperatures. Food

Microbiology, v.21, p.501-509, 2004.

Gaspovic, R.; Kreyenschmidt, J.; Bruckner, S.;

Popov, V.; Haque, N. Mathematical modelling

for predicting the growth of Pseudomonas spp.

in poultry under variable temperature

conditions. International Journal of Food

Microbiology 127 (2008) 290–297.

Gibson, A. M.; Bratchell, H.; Roberts, T. A.

(1987). The effect of sodium chloride and

temperature on rate and extent of growth of

Clostridium botulinum type A in pasteurized

pork slurry. Applied Bacteriology. 62, 479–

McMeekin, T. A.; Brown, J. ; Krist, K.; Miles,

D.; Neumeyer, K.; Nichols, D. S. ; Olley, J.;

Presser, K. ; Ratkowsky, T. D. A.; Ross, M. S.;

Soontranon, S. Quantitative Microbiology: A

Basis for Food Safety, Emerging Infectious

Diseases, v. 3, n° 4, 1997.

McMeekin, T. A.; Olley, M. B.; Ross, T.,

Ratkowsky, D. A., Predictive Microbiology:

theory and application. Researches Studies, p.

-86, 1993.

Nakashima, S. M. K.; André, D. S. ; Franco, B.

D. G. M. Revisão: Aspectos Básicos da

Microbiologia Preditiva. Brazilian Journal of

Food Technology, v. 3, p.41-51, 2000.

Ratkowsky, D. A.; Lowry, R. K.; Mcmeekin, T.

A.; Stokes, A. N.; Chandler, R. E. (1983)

Model for bacterial culture growth rate through

the entire biokinetic temperature range.

Journal of Bacteriology 154, 1222–1226.

Ross, T.; McMeekin, T. A. Predictive

Microbiology. International Journal of Food

Microbiology, v.23, p.41-264, 1994.

Sarmento, C. M. P. Modelagem do

crescimento microbiano e avaliação

sensorial no estudo da vida de prateleira da

mortadela e da lingüiça defumada em

armazenamento isotérmico e não isotérmico.

(Tese de Doutorado), Programa de pós-

graduação em Engenharia Química. UFSC,

Florianópolis, setembro de 2006.

Slongo, A. P.; Rosenthal, A.; Camargo, L. M.

Q.; Deliza, R.; Mathias, S. P.; Aragão, G. M. F.

Modeling the growth of lactic acid bacteria in

sliced ham processed by high hydrostatic

pressure. Food Science and Technology, 42

(2009) 303–306.

Van Impe, J. F.; Bart, M. N.; Schellekens, M.;

Martens, T.; Baerdemaeker, J. A. Predictive

microbiology in a dynamic environment: a

system theory approach. International

Journal of Food Microbiology, v. 25, p.227-

, 1995.

Published

2015-08-31

How to Cite

LIMA, M. dos S. .; REIS, P. M. C. de L.; ARAGÃO, G. M. F. Estimation of non-isothermal growth of bacteria Aeromonas hydrophila from isothermal data. Revista Semiárido De Visu, [S. l.], v. 3, n. 2, p. 64–72, 2015. DOI: 10.31416/rsdv.v3i2.169. Disponível em: https://revistas.ifsertao-pe.edu.br/index.php/rsdv/article/view/169. Acesso em: 24 nov. 2024.