Modelos microbiológicos

Agri-food companies must ensure that the products they market are safe and stable throughout the distribution chain

To ensure consumer safety, food companies need resources that minimise the risks of pathogenic or spoilage micro-organisms developing in their products.

When developing new products, it is essential to study how micro-organisms evolve under the influence of various external factors, alone or in combination. If the company is aware of the evolution, it can define the inactivation and preservation measures for the product in question.

At AINIA we add value



  • Optimisation of inactivation and preservation processes

  • Minimisation of microbiological contamination in products

  • Designing more effective disinfection strategies


microbiological models

Using mathematical models, we quantitatively assess the behaviour (growth, survival or inactivation) of micro-organisms present in a food at different stages of processing, packaging and distribution. This helps to develop inactivation treatments, new product formulations and safer preservation and packaging conditions.

From a selection of relevant microbiota, we can obtain cultures of the target microorganisms or their resistance forms (spores). We determine the environmental variables (degree of acidity, humidity or atmosphere) whose complementary and synergistic effects exert a greater or lesser degree of protection against microbial spoilage. We design experiments to define the microorganism’s behaviour (growth, inactivation, toxin production, alteration…) and the effect that environmental parameters have on its evolution. This knowledge allows us to determine the best conservation or inactivation strategy.

In addition, we use predictive modelling to determine the efficacy of additives/strategies against target micro-organisms. This allows us to optimise application rates. We can also determine the effect of product/preservation factors and define the application parameters for disinfection treatments.

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microbiological models

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