Microbiología predictiva: una ciencia en auge
Palabras clave:
Microbiología de alimentos, modelos predictivos, factores de crecimiento, algoritmos matemáticos, superficies de respuesta, APPCC, seguridad alimentaria, análisis de riesgos, PCCResumen
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].
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