Predictive microbiology: a rising science
In recent years, researchers on food microbiology started to use mathematical and statistical tools more frequently. These tools are important to obtain a mathematical model able to describe the evolution of microorganisms in food. Researchers have applied the models to food industries in order to d...
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Universidad Antonio Nariño
2021
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| Acceso en línea: | http://revistas.uan.edu.co/index.php/ingeuan/article/view/351 http://repositorio.uan.edu.co/handle/123456789/3924 |
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| _version_ | 1812648570652721152 |
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| author | Yarce, Cristhian J. |
| author_facet | Yarce, Cristhian J. |
| author_sort | Yarce, Cristhian J. |
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| description | In recent years, researchers on food microbiology started to use mathematical and statistical tools more frequently. These tools are important to obtain a mathematical model able to describe the evolution of microorganisms in food. Researchers have applied the models to food industries in order to determine a priori the process conditions that lead to the activation and deactivation of microorganisms. It is worth noting that microorganisms can be harmful both to consumers as well as the food´s nutritional properties. Therefore, determining the susceptible conditions is important to prevent the consequences. The mathematical models frequently used include polynomials, logarithmic, exponential and differential equations. I distinguish three classes: primary models, secondary and tertiary. These models are important for reaching robust and reliable predictions regarding the behavior of microorganisms in food. This article presents a revision of microbiological predictive models, applied to the food field. The models presented often use the most studied parameters in predictive microbiology: temperature and pH. |
| format | info:eu-repo/semantics/publishedVersion |
| id | repositorio.uan.edu.co-123456789-3924 |
| institution | Repositorio Digital UAN |
| language | spa |
| publishDate | 2021 |
| publisher | Universidad Antonio Nariño |
| record_format | dspace |
| spelling | repositorio.uan.edu.co-123456789-39242024-10-09T23:26:23Z Predictive microbiology: a rising science Microbiología predictiva: una ciencia en auge Yarce, Cristhian J. Microbiología de alimentos modelos predictivos factores de crecimiento algoritmos matemáticos superficies de respuesta APPCC seguridad alimentaria análisis de riesgos PCC Food microbiology predicitve models rising factors APPCC food security risk analysis PCC In recent years, researchers on food microbiology started to use mathematical and statistical tools more frequently. These tools are important to obtain a mathematical model able to describe the evolution of microorganisms in food. Researchers have applied the models to food industries in order to determine a priori the process conditions that lead to the activation and deactivation of microorganisms. It is worth noting that microorganisms can be harmful both to consumers as well as the food´s nutritional properties. Therefore, determining the susceptible conditions is important to prevent the consequences. The mathematical models frequently used include polynomials, logarithmic, exponential and differential equations. I distinguish three classes: primary models, secondary and tertiary. These models are important for reaching robust and reliable predictions regarding the behavior of microorganisms in food. This article presents a revision of microbiological predictive models, applied to the food field. The models presented often use the most studied parameters in predictive microbiology: temperature and pH. En las últimas dos décadas, para el estudio de la microbiología de alimentos, se han incluido como herramientas de análisis, el uso de la matemática y la estadística; tales conocimientos se combinan para desarrollar modelos matemáticos que describan la evolución de los microorganismos en los alimentos [1]. Para los modelos predictivos hay una gran variedad de estudios aplicados en diferentes matrices e industrias alimenticias [2-4]; estos buscan determinar a priori las condiciones de proceso (pH, la temperatura, la actividad de agua, el tiempo de agitación, entre otros), en las cuales hay activación, desactivación, crecimiento o muerte de los microorganismos que pueden ser perjudiciales tanto para el ser humano como para las propiedades organolépticas y nutricionales de un alimento [5, 6], de esta manera establecer puntos de control que eviten tales resultados [7, 8]. Los modelos matemáticos incluyen ecuaciones de diversos tipos como las polinómicas, logarítmicas, exponenciales, diferenciales, hasta llegar a modelos que incluyan ecuaciones de redes neuronales artificiales; también se clasifican en modelos primarios, secundarios o terciarios; que después de ser consolidados y aplicados logran unas predicciones robustas y seguras; sobre el comportamiento de los microorganismos en alimentos [9]. 2021-06-16T13:53:05Z 2021-06-16T13:53:05Z 2014-09-08 info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article http://purl.org/coar/version/c_970fb48d4fbd8a85 http://revistas.uan.edu.co/index.php/ingeuan/article/view/351 http://repositorio.uan.edu.co/handle/123456789/3924 spa http://revistas.uan.edu.co/index.php/ingeuan/article/view/351/293 Acceso abierto Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 application/pdf Universidad Antonio Nariño 2346-1446 2145-0935 INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 3 Núm. 6 (2013) |
| spellingShingle | Microbiología de alimentos modelos predictivos factores de crecimiento algoritmos matemáticos superficies de respuesta APPCC seguridad alimentaria análisis de riesgos PCC Food microbiology predicitve models rising factors APPCC food security risk analysis PCC Yarce, Cristhian J. Predictive microbiology: a rising science |
| title | Predictive microbiology: a rising science |
| title_full | Predictive microbiology: a rising science |
| title_fullStr | Predictive microbiology: a rising science |
| title_full_unstemmed | Predictive microbiology: a rising science |
| title_short | Predictive microbiology: a rising science |
| title_sort | predictive microbiology a rising science |
| topic | Microbiología de alimentos modelos predictivos factores de crecimiento algoritmos matemáticos superficies de respuesta APPCC seguridad alimentaria análisis de riesgos PCC Food microbiology predicitve models rising factors APPCC food security risk analysis PCC |
| url | http://revistas.uan.edu.co/index.php/ingeuan/article/view/351 http://repositorio.uan.edu.co/handle/123456789/3924 |
| work_keys_str_mv | AT yarcecristhianj predictivemicrobiologyarisingscience AT yarcecristhianj microbiologiapredictivaunacienciaenauge |
