4.7 Article

Modeling survival of high hydrostatic pressure treated stationary- and exponential-phase Listeria innocua cells

期刊

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ELSEVIER SCI LTD
DOI: 10.1016/j.ifset.2008.11.004

关键词

High hydrostatic pressure; Inactivation kinetics; Gompertz and Baranyi models; Listeria innocua; Predictive miciobiology

资金

  1. CONACYT in Mexico
  2. Spanish Ministry of Education and Science [AGL-2006-13320-C03-01]

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High Hydrostatic Pressure (HHP) inactivation (325-400 MPa; 0-20 min; maximum temperature 30 degrees C) of cells of Listeria innocua CEC7 910 was studied in two different growth phases (exponential and stationary), and the corresponding survival curves were obtained for each case. The curves were fitted to two nonlinear models, the modified Gompertz equation and the Baranyi model. The kinetic constants calculated for both models, mu(max) and k(max), indicated that cells in exponential growth phase were more sensitive to pressure than those in stationary phase. Both mathematical models were suitable for describing L innocua HHP survival curves. rendering kinetic constants that increased with increasing pressure. When considering the experimental models validation, both Gompertz and Baranyi predicted in a similar way. however Baranyi had slightly lower A(f) (Accuracy factor) and B-f (Bias factor) values, which indicated better prediction values. In summary, both mathematical models were perfectly valid for describing L innocua inactivation kinetics under HHP treatment. Industrial relevance: The mathematical models for inactivation and growth of microorganisms are the foundation of predictive microbiology and are used in risk assessments procedures as part of the food safety management system. Besides, these models together with those applied to inactivation of enzymes and destruction of quality factors are essential to optimize processes and thus to lay the foundations for industrial processing. It is therefore necessary to identify generally applicable kinetic models that will produce primary and secondary kinetic parameters and are statistically reliable as a key tool to predict the behaviour of microorganisms, enzymes and quality factors after processing. (C) 2008 Elsevier Ltd. All rights reserved.

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