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A Predictive Model for L isteria monocytogenes in UHT Dairy Products with Various Fat Content during Cold Storage
Author(s) -
Lobacz Adriana,
Kowalik Jaroslaw
Publication year - 2015
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/jfs.12163
Subject(s) - food science , food safety , product (mathematics) , variance (accounting) , statistics , mathematics , microbiology and biotechnology , chemistry , biology , business , geometry , accounting
The growth of L isteria monocytogenes was determined in Ultra‐High‐Temperature (UHT) dairy products (2% milk, 12 and 30% cream) at temperature range of 3–15C. Microbiological data were fitted to primary models (the B aranyi model, the modified G ompertz and logistic functions). The goodness‐of‐fit of primary models was analyzed by calculating mean square error and A kaike's information criterion. B aranyi model yielded the most accurate adjustment and the growth rates generated by this model were used for further mathematical analyses. Analysis of variance was used to check if the fat content significantly ( P < 0.05) influences the behavior of L . monocytogenes . No statistical differences were noted in the behavior of the pathogen. Microbiological growth data were combined and secondary modeling was performed using R atkowsky, A rrhenius and polynomial models. The latter model gave the best description and was further validated using accuracy ( A f ) and bias ( B f ) factors, as well as data from ComBase database and COMBASE Predictor. Practical Applications Mathematical models that describe the behavior of microorganisms, especially foodborne pathogens, in a particular product or group of food products with similar characteristics, pose a perspective of using predictive microbiology in order to increase the food safety. Application of predictive models is in agreement with C odex A limentarius C ommission and UE regulations in a risk analysis area. Presented results can be used by food manufacturers in food product development process, as well as a tool to support food safety assurance systems. Moreover, predictive models find practical application in Hazard Analysis and Critical Control Point (HACCP) plans providing useful information on the determination of critical control points ( CCPs ) and the estimation of critical limits at CCPs . The assessment and management of safety, quality and shelf life of food products can be facilitated by application of mathematical predictive models.