ISHS Acta Horticulturae 476: International Symposium on Applications of Modelling as InnovativeTechnique in the Agri-Food Chain. MODEL-IT
DESCRIPTION OF BACTERIAL GROWTH PARAMETERS AS A FUNCTION OF TEMPERATURE, PH AND SALT CONCENTRATION WITH ARTIFICIAL NEURAL NETWORKS |
| Authors: | A.H. Geeraerd, C.H. Herremans, C. Cenens, J.F. Van Impe |
| Keywords: | predictive microbiology, artificial neural networks, chilled food products |
| Abstract:
The consumer and catering interest for chilled, prepared food products and food components increases considerably. In contrast with classical sterilisation processes, a trade-off between safety and quality is attained by killing pathogenic vegetative cells by a pasteurisation process. The shelf life of this type of product is determined by the evolution of surviving micro-organisms which can spoil the product or possibly even cause pathogenic effects. The field of predictive microbiology aims at developing mathematical models for these non-linear inactivation and growth processes. The main factors influencing the microbial stability of chilled, prepared food products are temperature, pH, and % NaCl. The temperature in particular may vary significantly throughout the complete production and distribution chain. Therefore, to predict accurately the shelf life of this type of product, dynamic mathematical models (i.e., using differential equations) are needed [see, e.g., Van Impe et al. (1992) or Baranyi et al. (1993)]. | |
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