Development of the Food Label Information Program: A Comprehensive Canadian Branded Food Composition Database

Mavra Ahmed; Alyssa Schermel; Jennifer Lee; Madyson Weippert; Beatriz Franco-Arellano; Mary L'Abbé

doi:10.3389/fnut.2021.825050

Development of the Food Label Information Program: A Comprehensive Canadian Branded Food Composition Database

Front Nutr. 2022 Feb 3:8:825050. doi: 10.3389/fnut.2021.825050. eCollection 2021.

Authors

Mavra Ahmed¹, Alyssa Schermel¹, Jennifer Lee¹, Madyson Weippert¹, Beatriz Franco-Arellano^{1

2}, Mary L'Abbé¹

Affiliations

¹ Department of Nutritional Sciences, Temerty Faculty School of Medicine, University of Toronto, Toronto, ON, Canada.
² Faculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.

Abstract

Objectives: Traditional methods for creating food composition databases struggle to cope with the large number of products and the rapid pace of turnover in the food supply. This paper introduces Food Label Information Program (FLIP), a big data approach to the evaluation of the Canadian food supply and presents the latest methods used in the development of this database.

Methods: The Food Label Information Program (FLIP) is a database of Canadian food and beverage package labels by brand name. The latest iteration of the FLIP, FLIP 2020, was developed using website "scraping" to collect food labeling information (e.g., nutritional composition, price, product images, ingredients, brand, etc.) on all foods and beverages available on seven major Canadian e-grocery retailer websites between May 2020 and February 2021.

Results: The University of Toronto's Food Label Information Program (FLIP) 2020 was developed in three phases: Phase 1, database development and enhancements; Phase 2, data capture and management of food products and nutrition information; Phase 3, data processing and food categorizing. A total of 74,445 products available on websites of seven retailers and 2 location-specific duplicate retailers were collected for FLIP 2020. Of 57,006 food and beverage products available on seven retailers, nutritional composition data were available for about 60% of the products and ingredients were available for about 45%. Data for energy, protein, carbohydrate, fat, sugar, sodium and saturated fat were present for 54-65% of the products, while fiber information was available for 37%. Food products were classified under multiple categorization systems, including Health Canada's Table of Reference Amounts, Health Canada's sodium categories for guiding benchmark sodium levels, sugar-focused categories and categories specific to various global nutrient profiling models.

Conclusions: FLIP is a powerful tool for evaluating and monitoring the Canadian food supply environment. 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 Canadian food supply.

Keywords: Optical Character Recognition; artificial intelligence; branded food database; food composition; nutrition facts table; packaged foods; web-scraping.