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TEMPERATURE DEPENDENT GROWTH CHARACTERISTICS AND A PREDICTIVE MATHEMATICAL MODEL OF BACILLUS CEREUS IN WET NOODLES
Author(s) -
LEE JIYOUNG,
KIM BOYEON,
NA BYUNGJIN,
HA SANGDO
Publication year - 2011
Publication title -
journal of food safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.427
H-Index - 43
eISSN - 1745-4565
pISSN - 0149-6085
DOI - 10.1111/j.1745-4565.2010.00268.x
Subject(s) - gompertz function , bacillus cereus , cereus , mathematics , polynomial and rational function modeling , polynomial , statistics , coefficient of determination , growth model , quadratic function , linear regression , function (biology) , food science , chemistry , biology , quadratic equation , mathematical analysis , geometry , genetics , mathematical economics , evolutionary biology , bacteria
We examined the growth characteristics and developed a mathematical model to predict the growth kinetics of Bacillus cereus in fresh wet noodles. Noodles were incubated with B. cereus at 5, 10, 20 and 30C. The primary model that we developed to obtain the specific growth rate (SGR) and lag time (LT) fit well ( r 2 = 0.9621–0.9977) with the Gompertz equation. The SGR of B. cereus increased with increasing temperature. A secondary polynomial model was obtained by SAS regression analysis and calculated as: SGR = −0.0128 + 0.00229*T + 0.0003*T 2 . The appropriateness of this secondary polynomial model was verified by the mean square error (MSE = 0.0001 for SGR model), bias factor (B f = 0.9012 for SGR model), accuracy factor (A f = 1.3333 for SGR model) and coefficient of determination ( r 2 = 0.9955 for SGR). This secondary polynomial model is therefore in good agreement with the validation calculations and may be used as a practical predictive model for the growth of B. cereus in wet noodles as a function of storage temperature. PRACTICAL APPLICATIONS Wet noodles carry a potential risk of food poisoning by B. cereus. The predictive model developed here can increase confidence and improve the shelf life and safety of foods. Moreover, the results obtained using the predicted model developed here were in good agreement with the observed values. Therefore, the developed model may be used as a practical predictive model for the growth of B. cereus in wet noodles as a function of storage temperature.