A Population Balance Model to Describe the Evolution of Sublethal Injury
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 da...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/10/7/1674 |
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| author | Simen Akkermans Davy Verheyen Cindy Smet Jan F. M. Van Impe |
| author_facet | Simen Akkermans Davy Verheyen Cindy Smet Jan F. M. Van Impe |
| author_sort | Simen Akkermans |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:38:53Z |
| format | Article |
| id | doaj.art-09beda6771a549b79c6ef9af170aa01c |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T09:38:53Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj.art-09beda6771a549b79c6ef9af170aa01c2023-11-22T03:48:54ZengMDPI AGFoods2304-81582021-07-01107167410.3390/foods10071674A Population Balance Model to Describe the Evolution of Sublethal InjurySimen Akkermans0Davy Verheyen1Cindy Smet2Jan F. M. Van Impe3BioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, 9000 Ghent, BelgiumBioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, 9000 Ghent, BelgiumBioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, 9000 Ghent, BelgiumBioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, 9000 Ghent, BelgiumThe 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.https://www.mdpi.com/2304-8158/10/7/1674food safetypredictive microbiologymathematical modelsmicrobial inactivationsublethal injury |
| spellingShingle | Simen Akkermans Davy Verheyen Cindy Smet Jan F. M. Van Impe A Population Balance Model to Describe the Evolution of Sublethal Injury Foods food safety predictive microbiology mathematical models microbial inactivation sublethal injury |
| title | A Population Balance Model to Describe the Evolution of Sublethal Injury |
| title_full | A Population Balance Model to Describe the Evolution of Sublethal Injury |
| title_fullStr | A Population Balance Model to Describe the Evolution of Sublethal Injury |
| title_full_unstemmed | A Population Balance Model to Describe the Evolution of Sublethal Injury |
| title_short | A Population Balance Model to Describe the Evolution of Sublethal Injury |
| title_sort | population balance model to describe the evolution of sublethal injury |
| topic | food safety predictive microbiology mathematical models microbial inactivation sublethal injury |
| url | https://www.mdpi.com/2304-8158/10/7/1674 |
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