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  • Title: Modeling growth rate and assessing aflatoxins production by Aspergillus flavus as a function of water activity and temperature on polished and brown rice.
    Author: Mousa W, Ghazali FM, Jinap S, Ghazali HM, Radu S.
    Journal: J Food Sci; 2013 Jan; 78(1):M56-63. PubMed ID: 23301606.
    Abstract:
    UNLABELLED: The aim of this study was to model the radial growth rate and to assess aflatoxin production by Aspergillus flavus as a function of water activity (a(w) 0.82 to 0.92) and temperature (12 to 42 °C) on polished and brown rice. The growth of the fungi, expressed as colony diameter (mm) was measured daily, and the aflatoxins were analyzed using HPLC with a fluorescence detector. The growth rates were estimated using the primary model of Baranyi, which describes the change in colony radius as a function of time. Total of 2 secondary models were used to describe the combined effects of a(w) and temperature on the growth rates. The models were validated using independent experimental data. Linear Arrhenius-Davey model proved to be the best predictor of A. flavus growth rates on polished and brown rice followed by polynomial model. The estimated optimal growth temperature was around 30 °C. A. flavus growth and aflatoxins were not detected at 0.82 a(w) on polished rice while growth and aflatoxins were detected at this a(w) between 25 and 35 °C on brown rice. The highest amounts of toxins were formed at the highest a(w) values (0.90 to 0.92) at a temperature of 20 °C after 21 d of incubation on both types of rice. Nevertheless, the consistencies of toxin production within a wider range of a(w) values occurred between 25 to 30 °C. Brown rice seems to support A. flavus growth and aflatoxin production more than the polished rice. PRACTICAL APPLICATION: The developed models can be used to estimate to what extent the change in grain ecosystem conditions affect the storage stability and safety of grains without the need for running long-standing storage study. By monitoring the intergranular relative humidity and temperature at different locations in the storage facility and inputting these data into the models, it is directly possible to assess either the conditions are conductive for the growth of A. flavus or aflatoxin production.
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