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• W3185071217 endingPage "1674" @default.
• W3185071217 startingPage "1674" @default.
• W3185071217 abstract "The detection and quantification of sublethal injury (SI) of pathogenic microorganisms has become a common procedure when assessing the efficiency of microbial inactivation treatments. However, while a plethora of studies investigates SI in function of time, no suitable modelling procedure for SI data has been proposed thus far. In this study, a new SI model structure was developed that relies on existing microbial inactivation models. This model is based on the description of inactivation kinetics between the subpopulations of healthy, sublethally injured and dead cells. The model was validated by means of case studies on previously published results, modelled by different inactivation models, i.e., (i) log-linear inactivation; (ii) biphasic inactivation; and (iii) log-linear inactivation with tailing. Results were compared to those obtained by the traditional method that relies on calculating SI from independent inactivation models on non-selective and selective media. The log-linear inactivation case study demonstrated that the SI model is equivalent to the use of independent models when there can be no mistake in calculating SI. The biphasic inactivation case study illustrated how the SI model avoids unrealistic calculations of SI that would otherwise occur. The final case study on log-linear inactivation with tailing clarified that the SI model provides a more mechanistic description than the independent models, in this case allowing the reduction of the number of model parameters. As such, this paper provides a comprehensive overview of the potential and applications for the newly presented SI model." @default.
• W3185071217 created "2021-08-02" @default.
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• W3185071217 date "2021-07-20" @default.
• W3185071217 modified "2023-10-15" @default.
• W3185071217 title "A Population Balance Model to Describe the Evolution of Sublethal Injury" @default.
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• W3185071217 doi "https://doi.org/10.3390/foods10071674" @default.
• W3185071217 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8304568" @default.
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• W3185071217 hasPublicationYear "2021" @default.
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