An intercontinental machine learning analysis of factors explaining consumer awareness of food risk
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 quest...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Future Foods |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666833523000199 |
| _version_ | 1797800966888620032 |
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| author | Alberto Tonda Christian Reynolds Rallou Thomopoulos |
| author_facet | Alberto Tonda Christian Reynolds Rallou Thomopoulos |
| author_sort | Alberto Tonda |
| collection | DOAJ |
| description | 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, hinting at differences in food education as a possible underlying explanation for the data. |
| first_indexed | 2024-03-13T04:43:15Z |
| format | Article |
| id | doaj.art-4692c7decbd040b48e1ae50bf4c1f7e6 |
| institution | Directory Open Access Journal |
| issn | 2666-8335 |
| language | English |
| last_indexed | 2024-03-13T04:43:15Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Future Foods |
| spelling | doaj.art-4692c7decbd040b48e1ae50bf4c1f7e62023-06-19T04:29:59ZengElsevierFuture Foods2666-83352023-06-017100233An intercontinental machine learning analysis of factors explaining consumer awareness of food riskAlberto Tonda0Christian Reynolds1Rallou Thomopoulos2UMR 518 MIA-PS, INRAE, AgroParisTech, University of Paris-Saclay, France; UAR 3611 ISC-PIF, CNRS, FranceCentre for Food Policy, City, University of London, United KingdomIATE, University of Montpellier, INRAE, Institut Agro, France; Corresponding author.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, hinting at differences in food education as a possible underlying explanation for the data.http://www.sciencedirect.com/science/article/pii/S2666833523000199Food habitsFood knowledgeRandom forestRisk perceptionSurveyClassification |
| spellingShingle | Alberto Tonda Christian Reynolds Rallou Thomopoulos An intercontinental machine learning analysis of factors explaining consumer awareness of food risk Future Foods Food habits Food knowledge Random forest Risk perception Survey Classification |
| title | An intercontinental machine learning analysis of factors explaining consumer awareness of food risk |
| title_full | An intercontinental machine learning analysis of factors explaining consumer awareness of food risk |
| title_fullStr | An intercontinental machine learning analysis of factors explaining consumer awareness of food risk |
| title_full_unstemmed | An intercontinental machine learning analysis of factors explaining consumer awareness of food risk |
| title_short | An intercontinental machine learning analysis of factors explaining consumer awareness of food risk |
| title_sort | intercontinental machine learning analysis of factors explaining consumer awareness of food risk |
| topic | Food habits Food knowledge Random forest Risk perception Survey Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2666833523000199 |
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