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

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...

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Bibliographic Details
Published in:Food control 2002-12, Vol.13 (8), p.525-533
Main Authors: Xiong, R, Xie, G, Edmondson, A.S, Meullenet, J-F
Format: Article
Language:English
Subjects:
Citric acid
egg yolk
lipid content
Mayonnaise
microbiological quality
Modelling
mustard
Neural network
neural networks
pepper
recipes
Salmonella
Salmonella enteritidis
sugars
temperature
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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 among the different recipes. 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.
ISSN:0956-7135
1873-7129
DOI:10.1016/S0956-7135(02)00040-3