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Because classical statistics can struggle with complex data, machine learning is used: a Random Forest predicts answers, ranks the most influential questions, and Random Forest regression tests correlations. Results highlight carbon footprints and calories for beef and chicken as key explanatory variables, suggesting links to environmental and nutritional awareness and potential differences in food education.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/an-intercontinental-machine-learning-analysis-of-factors-explaining-consumer-awareness-of-food-risk/291180/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/an-intercontinental-machine-learning-analysis-of-factors-explaining-consumer-awareness-of-food-risk/291180.png","ImageObject",300,407,{"name":42,"@type":43},"Aurelia","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What data and countries were used to study consumer awareness of food risk?","Question",{"text":63,"@type":64},"The analysis is based on a questionnaire administered in seven countries across four continents: Argentina, Brazil, Colombia, Ghana, India, Peru, and the United Kingdom.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Why does the paper use machine learning instead of only classical statistical methods?",{"text":68,"@type":64},"Classical approaches can be limited for complex datasets. 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An intercontinental machine learning analysis of factors explaining consumer awareness of  \nfood risk. FUTURE FOODS, 7, doi: 10. 1016/j.fufo.2023.100233 This is the accepted version of the paper.  \nThis version of the publication may differ from the final published version.  \nPermanent repository link: [https://openaccess.city.ac.uk/id/eprint/30877/](https://openaccess.city.ac.uk/id/eprint/30877/)[ ](https://openaccess.city.ac.uk/id/eprint/30877/)Link to published version: [https://doi.org/10.1016/j.fufo.2023.100233](https://doi.org/10.1016/j.fufo.2023.100233)  \nCopyright: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to.  \nReuse: Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content isnot changed in any way.  \n\n| City Research Online: | [http://openaccess.city.ac.uk/](http://openaccess.city.ac.uk/) | [publications@city.ac.uk](publications@city.ac.uk) |\n| --- | --- | --- |\n|  |  |  |\n\nFuture Foods 7 (2023) 100233  \nContents lists available at ScienceDirect  \nFuture Foods  \njournal [homepage:](homepage: www.elsevier.com/locate/fufo)[ www.elsevier.com/locate/fufo](homepage: www.elsevier.com/locate/fufo)  \n| An intercontinental machine learning analysis of factors explaining consumer awareness of food risk |  | |\n| --- | --- | --- |\n| Alberto Tondaa,b, Christian Reynolds c, Rallou Thomopoulosd,∗ a UMR 518 MIA-PS, INRAE, AgroParisTech, University of Paris-Saclay, France\u003Cbr>b UAR 3611 ISC-PIF, CNRS, France\u003Cbr>c Centre for Food Policy, City, University of London, United Kingdom dIATE, University of Montpellier, INRAE, Institut Agro, France |  |  |\n| a r t i c l e i n f o\u003Cbr>Keywords: Food habits\u003Cbr>Food knowledge Random forest Risk perception Survey Classification Regression | a b s t r a c t |  |\n|  | Food safety is a common concern at the household level, with important variations across different countries and cultures. Nevertheless, identifying the factors that best explain similarities and differences in consumer awareness pertaining to this topic is not straightforward. Starting from a questionnaire administered in seven countries from four continents (Argentina, Brazil, Colombia, Ghana, India, Peru, and the United Kingdom), we present an analysis of the answers related to food safety concerns, aimed at identifying possible explanatory factors. As classical statistical approaches can be limited when dealing with complex datasets, we propose an analysis with machine learning techniques, that can take into account both categorical and numerical values. With the questionnaire as a base, we task a machine learning algorithm, Random Forest, with predicting consumers’ answers to the target questions using information from all other answers. Once the algorithm is trained, it becomes possible to obtain a ranking of the questions considered the most important for the prediction, with the top-ranked questions likely representing explanatory factors. Top-ranked questions are then analyzed using a Random Forest regression algorithm, to test possible correlations. The results show that the most significant explanatory variables of safety concerns seem to be estimates of carbon footprints and calories associated with food products, and primarily with beef and chicken meat. These results tend to indicate that people who are most concerned about food safety are also those who are highly aware of environmental and nutritional impacts of food, hinti","cbCaidVrAwpn3zmV","https://ap.wps.com/l/cbCaidVrAwpn3zmV","pdf",1671653,11,"English","# Introduction\n## Food safety and consumer awareness across countries\n## Digital data collection and analysis\n## Prior research on food risk perceptions","[{\"question\":\"What data and countries were used to study consumer awareness of food risk?\",\"answer\":\"The analysis is based on a questionnaire administered in seven countries across four continents: Argentina, Brazil, Colombia, Ghana, India, Peru, and the United Kingdom.\"},{\"question\":\"Why does the paper use machine learning instead of only classical statistical methods?\",\"answer\":\"Classical approaches can be limited for complex datasets. The study applies machine learning so the model can handle both categorical and numerical values when predicting responses.\"},{\"question\":\"Which factors emerged as most significant for explaining food safety concerns?\",\"answer\":\"The most significant explanatory variables are estimates of carbon footprints and calories associated with food products, especially beef and chicken meat.\"}]","An intercontinental machine learning analysis of factors explaining consumer awareness of food risk | PDF",28]