Estimating microbial growth parameters from non-isothermal data: a case study with Clostridium perfringens.
Microbial growth parameters are usually calculated from the fit of a growth model to a set of isothermal growth data gathered at several temperatures. In principle at least, it is also possible to derive them from non-isothermal ('dynamic') growth data. This requires the numerical solution of a rate model whose coefficients are nested terms that include the temperature profile. The methodology is demonstrated with simulated non-isothermal growth data on which random noise had been superimposed to emulate the scatter found in experimental microbial counts. The procedure has been validated by successful retrieval of the known generation parameters from the simulated growth curves. The method was then applied to experimental non-isothermal growth data of C. perfringens cells in cooled ground beef. The growth data collected under one cooling regime were used to predict the organism's growth patterns under different temperature histories. The practicality of the method is currently limited because of the relatively large scatter found in experimental microbial growth data and the relatively low frequency at which they are collected. But if and when the scatter could be reduced and the counts taken at short time intervals, the method could be used to determine the growth model in situ thus enabling to translate the changing temperature during processing, transportation or storage into a corresponding growth curve of the organism in question.
Smith-Simpson
Sarah
S
Food Risk Analysis Initiative, Food Science Department, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ 08901, USA.
Corradini
Maria G
MG
Normand
Mark D
MD
Peleg
Micha
M
Schaffner
Donald W
DW
D003016:Clostridium perfringens / Q000254:growth & development*; D015169:Colony Count, Microbial; D003257:Consumer Product Safety; D005516:Food Microbiology; D006801:Humans; D007700:Kinetics; D008433:Mathematics; D008954:Models, Biological; D011237:Predictive Value of Tests; D013696:Temperature