• Microbiology;
  • microorganisms;
  • predictive modelling;
  • sea bass;
  • statistics


The purpose of this paper was to estimate microbial growth through predictive modelling as a key element in determining the quantitative microbiological contamination of sea bass stored in ice and cultivated in different seasons of the year. In the present study, two different statistical models were used to analyse changes in microbial growth in whole, ungutted sea bass (Dicentrarchus labrax) stored in ice. The total counts of aerobic mesophilic and psychrotrophic bacteria, Pseudomonas sp., Aeromonas sp., Shewanella putrefaciens, Enterobacteriaceae, sulphide-reducing Clostridium and Photobacterium phosphoreum were determined in muscle, skin and gills over an 18-day period using traditional methods and evaluating the seasonal effect. The results showed that specific spoilage bacteria (SSB) were dominant in all tissues analysed, but were mainly found in the gills. Predictive modelling showed a seasonal effect among the fish analysed. The application of these models can contribute to the improvement of food safety control by improving knowledge of the microorganisms responsible for the spoilage and deterioration of sea bass.