Valerie J. Davidson

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Fuzzy models to recognize consumer preferences were developed as part of an automated inspection system for biscuits. Digital images were used to estimate physical features of chocolate chip cookies including size, shape, baked dough color, and fraction of top surface area that was chocolate chips. Polls were conducted to determine consumer ratings of(More)
We develop a prioritization framework for foodborne risks that considers public health impact as well as three other factors (market impact, consumer risk acceptance and perception, and social sensitivity). Canadian case studies are presented for six pathogen-food combinations: Campylobacter spp. in chicken; Salmonella spp. in chicken and spinach;(More)
This paper describes morbidity and mortality parameters for Campylobacter spp., Salmonella spp., enterohaemorrhagic Escherichia coli, Listeria spp., norovirus infections and their primary associated sequelae [Guillain-Barré syndrome (GBS), haemolytic uraemic syndrome, reactive arthropathies and Reiter's syndrome]. Data from a period of 4 years were obtained(More)
The study used a structured expert elicitation survey to derive estimates of the foodborne attributable proportion for nine illnesses caused by enteric pathogens in Canada. It was based on a similar study conducted in the United States and focused on Campylobacter, Escherichia coli O157:H7, Listeria monocytogenes, nontyphoidal Salmonella enterica, Shigella(More)
The study used a structured expert elicitation survey to derive estimates of food-specific attribution for nine illnesses caused by enteric pathogens in Canada. It was based on a similar survey conducted in the United States and focused on Campylobacter spp., Escherichia coli O157:H7, Listeria monocytogenes, nontyphoidal Salmonella enterica, Shigella spp.,(More)
The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for(More)
An artificial neural network (ANN) model was developed to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7 in a mayonnaise-type system. Temperature, pH, acetic acid, sucrose and salt were the inputs to a three-layer back-propagation neural network. The ANN model was trained using the data-set of McKellar et al. [2002. A(More)