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Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure
Richard Andrew Harrington1, Vyas Adhikari1, Mike Rayner1
1Nuffield Department of Population Health, University of Oxford Nuffield Department of Population Health, Oxford, UK.
A new big data approach, foodDB, tracks UK food products weekly. Cheaper ready meals have less fat, sugar, and salt, with many products changing or leaving the market rapidly.
Area of Science:
- Food Science
- Big Data Analytics
- Nutritional Epidemiology
Background:
- Traditional food composition tables are outdated due to the dynamic food market.
- A novel big data approach is needed to analyze the food and drink marketplace effectively.
Purpose of the Study:
- Introduce foodDB, a big data system for food market analysis.
- Illustrate the research potential of foodDB through various analyses.
Main Methods:
- Collected weekly nutrition and availability data from six major UK supermarket websites (Nov 2017-June 2018).
- Utilized a Python-based automated codebase for data extraction.
- Managed over 3 million observations of 128,000+ distinct products.
Main Results:
- Analysis of 97,000+ products in March 2018 showed lower-priced ready meals had significantly less fat, saturates, sugar, and salt (p<0.001).
- Longitudinal study of 903 pizzas revealed 10.8% reformulated and 29.9% were new or discontinued within 6 months.
Conclusions:
- foodDB is a robust tool for monitoring the food and drink market.
- The system enables detailed analysis of nutritional quality, marketing, product reformulation, and market dynamics.
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