Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure
Objectives Traditional methods for creating food composition tables struggle to cope with the large number of products and the rapid pace of change in the food and drink marketplace. This paper introduces foodDB, a big data approach to the analysis of this marketplace, and presents analyses illustra...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2019-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/6/e026652.full |
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| author | Peter Scarborough Mike Rayner Richard Andrew Harrington Vyas Adhikari |
| author_facet | Peter Scarborough Mike Rayner Richard Andrew Harrington Vyas Adhikari |
| author_sort | Peter Scarborough |
| collection | DOAJ |
| description | Objectives Traditional methods for creating food composition tables struggle to cope with the large number of products and the rapid pace of change in the food and drink marketplace. This paper introduces foodDB, a big data approach to the analysis of this marketplace, and presents analyses illustrating its research potential.Design foodDB has been used to collect data weekly on all foods and drinks available on six major UK supermarket websites since November 2017. As of June 2018, foodDB has 3 193 171 observations of 128 283 distinct food and drink products measured at multiple timepoints.Methods Weekly extraction of nutrition and availability data of products was extracted from the webpages of the supermarket websites. This process was automated with a codebase written in Python.Results Analyses using a single weekly timepoint of 97 368 total products in March 2018 identified 2699 ready meals and pizzas, and showed that lower price ready meals had significantly lower levels of fat, saturates, sugar and salt (p<0.001). Longitudinal analyses of 903 pizzas revealed that 10.8% changed their nutritional formulation over 6 months, and 29.9% were either discontinued or new market entries.Conclusions foodDB is a powerful new tool for monitoring the food and drink marketplace, the comprehensive sampling and granularity of collection provides power for revealing analyses of the relationship between nutritional quality and marketing of branded foods, timely observation of product reformulation and other changes to the food marketplace. |
| format | Article |
| id | doaj-art-eedd2fa7446245c3b32ee74bc76841a1 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-eedd2fa7446245c3b32ee74bc76841a12025-02-01T16:30:09ZengBMJ Publishing GroupBMJ Open2044-60552019-06-019610.1136/bmjopen-2018-026652Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructurePeter Scarborough0Mike Rayner1Richard Andrew Harrington2Vyas Adhikari3British Heart Foundation Health Promotion Research Group, Department of Public Health, University of Oxford, Oxford, UKNuffield Department of Population Health, University of Oxford, Oxford, UKNuffield Department of Population Health, University of Oxford Nuffield Department of Population Health, Oxford, UKNuffield Department of Population Health, University of Oxford Nuffield Department of Population Health, Oxford, UKObjectives Traditional methods for creating food composition tables struggle to cope with the large number of products and the rapid pace of change in the food and drink marketplace. This paper introduces foodDB, a big data approach to the analysis of this marketplace, and presents analyses illustrating its research potential.Design foodDB has been used to collect data weekly on all foods and drinks available on six major UK supermarket websites since November 2017. As of June 2018, foodDB has 3 193 171 observations of 128 283 distinct food and drink products measured at multiple timepoints.Methods Weekly extraction of nutrition and availability data of products was extracted from the webpages of the supermarket websites. This process was automated with a codebase written in Python.Results Analyses using a single weekly timepoint of 97 368 total products in March 2018 identified 2699 ready meals and pizzas, and showed that lower price ready meals had significantly lower levels of fat, saturates, sugar and salt (p<0.001). Longitudinal analyses of 903 pizzas revealed that 10.8% changed their nutritional formulation over 6 months, and 29.9% were either discontinued or new market entries.Conclusions foodDB is a powerful new tool for monitoring the food and drink marketplace, the comprehensive sampling and granularity of collection provides power for revealing analyses of the relationship between nutritional quality and marketing of branded foods, timely observation of product reformulation and other changes to the food marketplace.https://bmjopen.bmj.com/content/9/6/e026652.full |
| spellingShingle | Peter Scarborough Mike Rayner Richard Andrew Harrington Vyas Adhikari Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure BMJ Open |
| title | Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure |
| title_full | Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure |
| title_fullStr | Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure |
| title_full_unstemmed | Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure |
| title_short | Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure |
| title_sort | nutrient composition databases in the age of big data fooddb a comprehensive real time database infrastructure |
| url | https://bmjopen.bmj.com/content/9/6/e026652.full |
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