Planes de control

Ensuring product quality and safety, as well as consumer health

The food industry is confronted with a variety of microbiological hazards. On the one hand, spoilage micro-organisms that affect the quality of the product, and on the other hand, pathogenic micro-organisms (bacteria or viruses) that can cause a health problem for the final consumer.

Added to this complexity is the ever-increasing problem of antimicrobial resistance, which reduces the biocidal efficacy of disinfectants and the effectiveness of antibiotics in treating possible poisonings.

At AINIA we give you value

  • We identify the inherent microbiological risk in the production processes.

  • We anticipate the risk of a product and process under certain conditions

  • We use tools included in the legislative framework

Microbiological risk prediction models

As the behaviour of a micro-organism under certain conditions is predictable, there exist mathematical models that are capable of predicting it by taking into account different determinants. This methodology has been recognised in the European regulation on microbiological criteria for foodstuffs (RG 2073:2005).

Microbiological models can help optimise the conditions for obtaining food by intrinsic parameters (pH, salt, additive concentration) or extrinsic parameters (storage temperature, humidity or headspace atmosphere)

By using microbiological models in the production environment we can predict how much of a given pathogen or toxin will be present after a preservation process or treatment. In this way:

  • We know how changes in process conditions influence the final microbiological quality of the product

  • We assess the inherent microbiological risk of a given event, e.g. a line stoppage

  • Contributing to more effective HACCP management

In addition, by implementing different tools we are able to
perform simulations according to specific conditions, by providing an estimate of the risks associated with the food, process and pathogen under study.

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Amparo de Benito
microbiological models

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