- Predictive Modelling of Lactobacillus casei KN291 Survival in Fermented Soy Beverage
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Zieli&# , Koło&# , Goryl Antoni , Ilona Motyl
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J. Microbiol. 2014;52(6):535-535.
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DOI: https://doi.org/10.1007/s12275-014-0700-4
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In the article by Dorota et al. published in Journal of Microbiology 2014; 52, 169-179. First three authors’ first name and last name has been changed. Corrected name is Dorota Zielinska, Danuta Kolozyn-Krajewska, Antoni Goryl.
- Predictive Modelling of Lactobacillus casei KN291 Survival in Fermented Soy Beverage
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Zieli&# , Koło&# , Goryl Antoni , Ilona Motyl
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J. Microbiol. 2014;52(2):169-178. Published online February 1, 2014
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DOI: https://doi.org/10.1007/s12275-014-3045-0
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Abstract
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The aim of the study was to construct and verify predictive growth and survival models of a potentially probiotic bacteria in fermented soy beverage. The research material included natural soy beverage (Polgrunt, Poland) and the strain of lactic acid bacteria (LAB) – Lactobacillus casei KN291. To construct predictive models for the growth and survival of L. casei KN291 bacteria in the fermented soy beverage we design an experiment which allowed the collection of CFU data. Fermented soy beverage samples were stored at various temperature conditions (5, 10, 15, and 20°C) for 28 days. On the basis of obtained data concerning the survival of L. casei KN291 bacteria in soy beverage at different temperature and time conditions, two non-linear models (r2= 0.68–0.93) and two surface models (r2=0.76–0.79) were constructed; these models described the behaviour of the bacteria in the product to a satisfactory extent. Verification of the surface models was carried out utilizing the validation data - at 7°C during 28 days. It was found that applied models were well fitted and charged with small systematic errors, which is evidenced by accuracy factor - Af, bias factor - Bf and mean squared error - MSE. The constructed microbiological growth and survival models of L. casei KN291 in fermented soy beverage enable the estimation of products shelf life period, which in this case is defined by the requirement for the level of the bacteria to be above 106 CFU/cm3. The constructed models may be useful as a tool for the manufacture of probiotic foods to estimate of their shelf life period.
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Citations
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