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Neural network modelling of the fate of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise prepared with citric acid.

Author(s) : XIONG R., XIE G., EDMONDSON A. S., et al.

Type of article: Article

Summary

Fifty-four mayonnaise recipes were generated by the central composite design and tested for microbiological safety at two temperatures (5 and 22 °C). The content of oil: (150-350 ml), egg yolk (10-35 g), citric acid (4.98% w/v) (10-40 g), salt (0-3 g), mustard (0-2 g), sugar (0-1 g) and white pepper (0.25 g) varied. The fate of Salmonella enterica serovar Enteritidis PT4 in mayonnaise products was investigated by both viable count and presence/absence tests and modelled by neural networks. This study demonstrated that feed-forward neural networks were incapable of modelling the survival/growth curves of S. Enteritidis PT4 as a one-step-procedure model, but were capable of modelling the presence/absence of the organism.

Details

  • Original title: Neural network modelling of the fate of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise prepared with citric acid.
  • Record ID : 2004-1838
  • Languages: English
  • Source: Food Control The International Journal of HACCP and Food Safety - vol. 13 - n. 8
  • Publication date: 2002/12

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