Food safety aspects on ethnic foods: toxicological and microbial risks

Fusco, Vincenzina; Besten, H.M.W. den; Logrieco, Antonio F.; Rodríguez, Fernando Pérez; Skandamis, Panagiotis; Stessl, Beatrix; Teixeira, Paula

doi:10.1016/j.cofs.2015.11.010

Cited by 24 publications

(14 citation statements)

References 53 publications

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“…Issues particularly regarded deficiencies in traceability and poor hygiene conditions (Armani et al, 2012;Armani et al, 2015;D'Amico et al, 2014;Giorgi et al 2012). Moreover, a copious number of recalls of ethnic foods have occurred worldwide due to their contamination with biological agents, bacterial toxins, mycotoxins, and hazardous chemicals, also including and allergens (Lee et al, 2014;Fusco et al, 2015). Over the last years, the European Rapid Alert System for Food and Feed (RASFF) has especially notified several cases of contamination by the foodborne pathogens Salmonella spp.…”

Section: Introductionmentioning

confidence: 99%

Ethnic seafood products sold on the Italian market: labelling assessment and biological, chemical and physical risk characterization

2019

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Section: Introductionmentioning

confidence: 99%

Ethnic seafood products sold on the Italian market: labelling assessment and biological, chemical and physical risk characterization

2019

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“…Aside from inspection bias, another possible explanation for associations between neighborhood ethnicity and non-compliance is the role of ethnic cuisine. Some studies have reported higher numbers of CHVs at outlets serving ethnic cuisine [ 49 , 50 , 51 , 52 ]; however, it is unclear whether the location of such food outlets relates to the underlying population composition. For example, are Chinese restaurants more commonly located in areas with higher numbers of Chinese residents?…”

Section: Discussionmentioning

confidence: 99%

Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach

Oldroyd

Morris

Birkin

2021

IJERPH

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Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only 14% of local authorities are up to date with food business inspections, exposing consumers to unknown levels of risk. Given the scarcity of local authority resources, this paper presents a data-driven approach to predict compliance for newly opened businesses and those awaiting repeat inspections. This work capitalizes on the theory that food outlet compliance is a function of its geographic context, namely the characteristics of the neighborhood within which it sits. We explore the utility of three machine learning approaches to predict non-compliant food outlets in England and Wales using openly accessible socio-demographic, business type, and urbanness features at the output area level. We find that the synthetic minority oversampling technique alongside a random forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves and identifies 84% of total non-compliant outlets in a test set of 92,595 (sensitivity = 0.843, specificity = 0.745, precision = 0.274). The originality of this work lies in its unique and methodological approach which combines the use of machine learning with fine-grained neighborhood data to make robust predictions of compliance.

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“…We use clustering to divide these 310 respondents into two and then three smaller, non-overlapping groups. We do that by a method called k-means clustering [22,23], one of the many 'flavors' of clustering. The specific clustering method is not the key point here, but rather the notion that the clustering method is a heuristic, allowing us to easily divide this 'booming buzzing confusion' into a group of similar patterns.…”

Section: Step 5: Creating Individual-level Models and Developing Mind-sets By Clustering Coefficientsmentioning

confidence: 99%

The Dollar Value of Ideas Surrounding Ethnic Foods: A Mind Genomics Cartography

2022

Nutr Res Food Sci J

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310 respondents each evaluated 60 unique vignettes (combinations of messages) about ethnic foods, the messages presenting information about the country of origin, when and why the food is eaten, and the benefit if the ethnic food goes 'mainstream', and the issues about food safety. Respondents each rated unique sets of 60 the vignettes, first assigning purchase price, and second recording their emotion on reading the vignette. Three mind-sets emerged, based upon the dollar value of the messages. Further analysis demonstrated the interaction of elements, showing how the specific ethnic source drove the dollar value of other elements. The approach is presented as a model for the easy, rapid, and affordable creations of databases of the mind of people as they experience issues of everyday life.

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Food safety aspects on ethnic foods: toxicological and microbial risks

Cited by 24 publications

References 53 publications

Ethnic seafood products sold on the Italian market: labelling assessment and biological, chemical and physical risk characterization

Ethnic seafood products sold on the Italian market: labelling assessment and biological, chemical and physical risk characterization

Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach

The Dollar Value of Ideas Surrounding Ethnic Foods: A Mind Genomics Cartography

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