Predicting microbial heat inactivation under nonisothermal treatments.
The aim of this study was to develop an equation that accurately predicts microbial heat inactivation under nonisothermal treatments at constantly rising heating rates (from 0.5 to 5 degrees C/min) in media with different pH values (4.0 or 7.4). The survival curves of all bacteria (Enterococcus faecium, Escherichia coli, Listeria monocytogenes, Salmonella Senftenberg 775W, Salmonella Typhimurium, and Staphylococcus aureus) tested under isothermal treatments were nearly linear. For the most heat-resistant microorganism (E. faecium), the estimated DT-values at pH 7.4 were at least 100 times those of the second most thermotolerant microorganism (Salmonella Senftenberg 775W). The heat resistance of E. faecium was up to 30 times lower at pH 4.0 than at pH 7.4. However, E. faecium was still the most heat-resistant microorganism under nonisothermal treatments at both pH values. Inactivation under nonisothermal conditions was not accurately estimated from heat resistance parameters of isothermal treatments when microbial adaptation or sensibilization occurred during the heating up lag phases. The under-prediction of the number of survivors might be greater than 15 log CFU within the nonisothermal treatment conditions investigated. Therefore, the nonisothermal survival curves of the most heat-resistant microorganisms were fitted with the following equation: log S(t) = -(t/delta)P. This equation accurately described the survival curves of all the bacteria tested. We observed a linear relationship between the log of the scale parameter (delta) and the log of the heating rate. A p value characteristic of each microorganism and pH tested was calculated. Two equations capable of predicting the inactivation rate of all bacteria tested under nonisothermal treatments at pH 7.4, 5.5, or 4.0 were developed. The model was evaluated in skim milk and apple juice. The results of this study could be used to help minimize public health risks and to extend the shelf life of those foods requiring long heating up lag phases during processing.
Hassani
Mounir
M
Departamento Producción Animal y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad de Zaragoza, 50013 Zaragoza, Spain.
Condón
Santiago
S
Pagán
Rafael
R
D015169:Colony Count, Microbial; D016984:Enterococcus faecium / Q000254:growth & development; D004926:Escherichia coli / Q000254:growth & development; D005506:Food Contamination / Q000032:analysis*; D005511:Food Handling / Q000379:methods*; D005516:Food Microbiology; D005519:Food Preservation / Q000379:methods*; D006358:Hot Temperature; D006863:Hydrogen-Ion Concentration; D008089:Listeria monocytogenes / Q000254:growth & development; D011237:Predictive Value of Tests; D012475:Salmonella / Q000254:growth & development; D012486:Salmonella typhimurium / Q000254:growth & development; D013211:Staphylococcus aureus / Q000254:growth & development