Prediction of an organism's inactivation patterns from three single survival ratios determined at the end of three non-isothermal heat treatments.
Traditionally, an organism's heat resistance parameters have been determined from a set of experimental isothermal survival data. Sometimes, however, even approximating an isothermal profile, and/or obtaining counts at sufficiently short time intervals, is extremely difficult for technical and logistic reasons. The problem would be avoided if the survival parameters could be calculated from the final survival ratios determined at the end of non-isothermal heat treatments with known temperature profiles. Theoretically, if the heat resistance were characterized by three unknown survival parameters, they could be extracted by solving three simultaneous dynamic survival curves' equations. In practice, because of the three equation's complexity - they are themselves the numerical solutions of three differential rate equations - and because the experimental final survival ratios might have a scatter, realistic estimates of the survival parameters require short cut and averaging methods for their calculation. Such a method has been tried with published dynamic inactivation data on Salmonella enteritidis and Escherichia coli. The concept was validated by the ability of the Weibullian-Log logistic model, whose three survival parameters had been obtained directly from final experimental survival ratios only, to predict entire non-isothermal survival curves that had not been used in the model's formulation. The methodology need not be restricted to Weibullian and simpler survival patterns but its practicality might be lost if there are more than three survival parameters. In principle, the same procedure can be extended to biochemical processes that occur during heat preservation, especially at very high temperatures. Estimating inactivation kinetic parameters without isothermal data could also facilitate the quantification of microbial survival under realistic processing conditions and in the actual food rather than in a surrogate medium.
Corradini
Maria G
MG
Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA.
Normand
Mark D
MD
Peleg
Micha
M
D015169:Colony Count, Microbial / Q000379:methods*; D003257:Consumer Product Safety; D004926:Escherichia coli / Q000254:growth & development*; D005516:Food Microbiology; D006801:Humans; D007700:Kinetics; D016015:Logistic Models; D008433:Mathematics; D008954:Models, Biological; D011237:Predictive Value of Tests; D012477:Salmonella enteritidis / Q000254:growth & development*; D013696:Temperature