Journal of Applied Microbiology, Vol.122, No.4, 1048-1056, 2017
Modelling the effect of pH and water activity in the growth of Aspergillus fumigatus isolated from corn silage
AimsThe aim of this work was to use mathematical kinetic modelling to assess the combined effects of a(W,) pH, O-2 availability and temperature on the growth rate and time to growth of Aspergillus fumigatus strains isolated from corn silage. Methods and ResultsA full factorial design was used in which two factors were assayed: pH and a(W). The a(W) levels assayed were 080, 085, 090, 092, 094, 096, 098 and 099. The levels of pH assayed were 35, 4, 45, 5, 6, 7, 75 and 8. The assay was performed at normal oxygen tension at 25 and 37 degrees C, and at reduced oxygen tension at 25 degrees C. Two strains of A. fumigatus isolated from corn silage were used. Kinetic models were built to predict growth of the strain under the assayed conditions. The cardinal models gave a good quality fit for radial growth rate data. The results indicate that the environmental conditions which take place during silage production, while limiting the growth of most micro-organisms, would not be able to control A. fumigatus. Moreover, pH levels in silage, far from limiting its growth, are also close to its optimum. Carbon dioxide at 5% in the environment did not significantly affect its growth. ConclusionsA need for a further and controlled acidification of the silage exists, as no growth of A. fumigatus was observed at pH 35, as long as the organoleptic characteristics of the silage are not much compromised. Significance and Impact of the StudyAspergillus fumigatus is one of the major opportunistic pathogens able to cause illness such as allergic bronchopulmonary aspergillosis, aspergilloma and invasive aspergillosis to rural workers. Exposure of animals to A. fumigatus spores can result in infections, particularly in those organs exposed to external invasion, such as the airways, mammary gland and uterus at birth.
Keywords:cardinal parameters model;environmental mycology;fungi;predictive modelling;predictive mycology