Publication: Variabilidad de la inactivación microbiana y de la fase de latencia de los microorganismos supervivientes a un proceso de acidificación
Loading...
Files
Official URL
Full text at PDC
Publication Date
2016-08-12
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad Complutense de Madrid
Citations
Exportar
Abstract
A la industria alimentaria se le exigen productos seguros, nutritivos, apetecibles y de uso cómodo y rápido. Aunar todos esos calificativos en un solo alimento es ardua tarea. Valgan dos ejemplos. Un tratamiento conservante intenso, de buenas perspectivas sanitarias, suele conllevar una pérdida de valor nutritivo y unas características sensoriales poco atractivas. El manejo de los alimentos para transformarlos en productos listos pare el consumo implica la asunción de ciertos riesgos microbiológicos, mayores que los asumidos en productos sin manipulación. ¿Cómo responder ante el incremento de riesgos y peligros que se ciernen sobre los “nuevos alimentos”? Una alternativa que ha ganado correligionarios es la microbiología predictiva. Es una herramienta útil, a disposición de cualquier entidad interesada en los alimentos, que predice, mediante modelos matemáticos, el comportamiento microbiano bajo ciertas condiciones. La mayoría de los modelos disponibles predicen valores únicos (a cada valor de la variable independiente le corresponde un único valor de la dependiente); han demostrado su eficacia durante décadas a base de tratamientos sobredimensionados para salvaguardar la calidad microbiológica de los alimentos y predicen una media, sin considerar la variabilidad. Considérese un valor de reducción decimal, D, de 1 minuto. Si el producto contiene 103 ufc/g, un envase de 1 Kg que haya pasado por un tratamiento 6D, contendrá 1 célula viable. Hasta aquí la predicción de un modelo clásico. Ahora piénsese en una producción industrial, miles de envases de 1 Kg/h. ¿Quién puede creerse que en todos ellos habrá 1 microorganismo superviviente? ¿No es más creíble que en unos no quedará ningún viable, en muchos 1, en otros 2, 3 y quizás en los menos 5 ó 6? Los modelos que no consideran la variabilidad microbiana predicen con precisión la tasa de crecimiento pero han fracasado en la predicción de la fase de latencia...
The food industry demands safe, nutritious, appealing products that are quick and easy to use. Meeting all these needs in a single food is often arduous task. It is worth giving a couple of examples. An intense preservative treatment, which has good health perspectives, often leads to a loss of nutritional value and not very attractive sensory characteristics. The food handling to transform them into products ready for consumption involves the assumption of greater microbiological risks than those taken in foods without such transformations. How can the increasing risks and hazards that threaten the "new food" be responded to? One answer that has justifiably gained a followers is predictive microbiology. It is a very useful tool, available to any entity interested in food, capable of using mathematical models to predict microbial behavior under certain conditions. Most models generated so far predict static -unique- values. To a value of an independent variable corresponds a unique vale of the dependent variable. These static models that have proven successful for decades based on oversized treatments to safeguard the microbiological quality of food, predict an average, without considering the dispersion of data and its variability. For example, consider a value D of 1 minute. If the product contains 10 microorganisms per gram, a package of 1 Kg that has gone through a treatment of 6D, will contain 1 cell. Hitherto, this was the prediction of the classical model. Now, think of an industrial production of thousands of packs of 1 kg per hour. Who can believe that in all of them will be 1 surviving microorganism? Is not it more believable that some will not be viable at all, and in many there will be 1, in others 2 or 3 or perhaps at least 5 or 6? Moreover, models of microbial growth developed regardless of microbial variability predict with sufficient accuracy the growth rate but have failed in the prediction of lag phase. It is obvious that this phase of microbial growth is critical to estimating the shelf life of many products and if you actually conform to the best before date, without incurring economic losses to shortening it or assuming risks of uncertainty, you have to improve the predict models...
The food industry demands safe, nutritious, appealing products that are quick and easy to use. Meeting all these needs in a single food is often arduous task. It is worth giving a couple of examples. An intense preservative treatment, which has good health perspectives, often leads to a loss of nutritional value and not very attractive sensory characteristics. The food handling to transform them into products ready for consumption involves the assumption of greater microbiological risks than those taken in foods without such transformations. How can the increasing risks and hazards that threaten the "new food" be responded to? One answer that has justifiably gained a followers is predictive microbiology. It is a very useful tool, available to any entity interested in food, capable of using mathematical models to predict microbial behavior under certain conditions. Most models generated so far predict static -unique- values. To a value of an independent variable corresponds a unique vale of the dependent variable. These static models that have proven successful for decades based on oversized treatments to safeguard the microbiological quality of food, predict an average, without considering the dispersion of data and its variability. For example, consider a value D of 1 minute. If the product contains 10 microorganisms per gram, a package of 1 Kg that has gone through a treatment of 6D, will contain 1 cell. Hitherto, this was the prediction of the classical model. Now, think of an industrial production of thousands of packs of 1 kg per hour. Who can believe that in all of them will be 1 surviving microorganism? Is not it more believable that some will not be viable at all, and in many there will be 1, in others 2 or 3 or perhaps at least 5 or 6? Moreover, models of microbial growth developed regardless of microbial variability predict with sufficient accuracy the growth rate but have failed in the prediction of lag phase. It is obvious that this phase of microbial growth is critical to estimating the shelf life of many products and if you actually conform to the best before date, without incurring economic losses to shortening it or assuming risks of uncertainty, you have to improve the predict models...
Description
Tesis inédita de la Universidad Complutense de Madrid, Facultad de Veterinaria, Departamento de Nutrición, Bromatología y Tecnología de los Alimentos, leída el 08/02/2016