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Growth of Byssochlamys Nivea in Pineapple Juice Under the Effect of Water Activity and Ascospore Age
The study of thermal resistant mould, including Byssochlamys nivea, is of extreme importance since it has been associated with fruit and fruit products. The aim of this work is to analyze the influence of water activity (a(w)) and ascospore age (I) on the growth of Byssochlamys nivea in pineapple ju...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Sociedade Brasileira de Microbiologia
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3768946/ https://www.ncbi.nlm.nih.gov/pubmed/24031622 http://dx.doi.org/10.1590/S1517-83822011000100025 |
Sumario: | The study of thermal resistant mould, including Byssochlamys nivea, is of extreme importance since it has been associated with fruit and fruit products. The aim of this work is to analyze the influence of water activity (a(w)) and ascospore age (I) on the growth of Byssochlamys nivea in pineapple juice. Mold growth was carried out under different conditions of water activity (a(w)) (0.99, 0.96, 0.95, 0.93, 0.90) and ascospore age (I) (30, 51, 60, 69, 90 days). Growth parameters as length of adaptation phase (λ), maximum specific growth rate (µ(max)) and maximum diameter reached by the colony (A) were obtained through the fit of the Modified Gompertz model to experimental data (measuring radial colony diameter). Statistica 6.0 was used for statistical analyses (significance level α = 0.05). The results obtained clearly showed that water activity is statistically significant and that it influences all growth parameters, while ascospore age does not have any statistically significant influence on growth parameters. Also, these data showed that by increasing a(w) from 0.90 to 0.99, the λ value substantially decreased, while µ(max) and A values rose. The data contributed for the understanding of the behavior of B. nivea in pineapple juice. Therefore, it provided mathematical models that can well predict growth parameters, also helping on microbiological control and products’ shelf life determination. |
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