[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123610-en":3,"doc-seo-123610-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123610,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","An intercontinental machine learning analysis of factors explaining consumer awareness of food risk - Research","Food safety concerns vary across countries and cultures, yet identifying the factors that explain similarities and differences in consumer awareness is challenging. Using a questionnaire administered in seven countries across four continents, the study analyzes responses to find potential explanatory drivers. Because classical statistics may struggle with complex datasets, Random Forest machine learning predicts target answers from other items and ranks the most informative questions. A Random Forest regression then tests correlations, highlighting carbon footprint and calorie estimates for foods, especially beef and chicken.","City Research Online  \nCity St George’s, University of London  \nCitation: Tonda, A. , Reynolds, C. & Thomopoulos, R. (2023) . An intercontinental machine learning analysis of factors explaining consumer awareness of food risk.  \nFuture Foods, 7, 100233. doi: 10.1016/j.fufo.2023.100233 This is the published version of the paper.  \nThis version of the publication may differ from the final published version. To cite this item please consult the publisher's version.  \nPermanent repository link: [https://openaccess.city.ac.uk/id/eprint/30877/](https://openaccess.city.ac.uk/id/eprint/30877/)  \nLink to published version: [https://doi.org/10.1016/j.fufo.2023.100233](https://doi.org/10.1016/j.fufo.2023.100233)  \n[Copyright and Reuse:](Copyright and Reuse: Copyright and Moral Rights remain with the author)[ Copyright and Moral Rights remain with the author](Copyright and Reuse: Copyright and Moral Rights remain with the author)([s](s)) and/or copyright holders. Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge, unless otherwise indicated, 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 is not changed in any way. For full details of reuse please refer to City Research Online policy.  \nCity Research Online:  [http://openaccess.city.ac.uk/](http://openaccess.city.ac.uk/  publications@citystgeorges.ac.uk)[ ](http://openaccess.city.ac.uk/  publications@citystgeorges.ac.uk)[ publications@citystgeorges.ac.uk](http://openaccess.city.ac.uk/  publications@citystgeorges.ac.uk)  \nFuture Foods 7 (2023) 100233  \nContents lists available at ScienceDirect  \nFuture Foods  \njournal [homepage: 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 ","cbCailFZE4P35Rnc","https://ap.wps.com/l/cbCailFZE4P35Rnc","pdf",1536401,1,10,"English","en",105,"# Introduction\n## Background: surveys and digital data collection\n## Food risk perception across geographic scales\n## Current focus: novel proteins and COVID-19 effects","[{\"question\":\"What data source and geographic coverage does the study use?\",\"answer\":\"The analysis is based on a questionnaire administered in seven countries across four continents, covering Argentina, Brazil, Colombia, Ghana, India, Peru, and the United Kingdom.\"},{\"question\":\"Why does the study use machine learning instead of only classical statistics?\",\"answer\":\"The paper argues that classical statistical approaches can be limited for complex datasets, so machine learning can handle both categorical and numerical values more effectively.\"},{\"question\":\"Which factors emerge as the most significant explanatory variables for food safety concerns?\",\"answer\":\"The most significant variables relate to carbon footprint and calorie estimates for food products, especially beef and chicken, indicating links between food safety concern and environmental and nutritional awareness.\"}]","An intercontinental machine learning analysis of factors explaining consumer awareness of food risk - 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