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

Abstract

ObjectivesTraditional 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.MethodsThe 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.ResultsThe 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.ConclusionsFLIP 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.

Nutritional quality and price of plant‐based dairy and meat analogs in the Canadian food supply system

“…Data from the University of Toronto Food Label Information and Price (FLIP) 2020 database, which contains web‐scraped product information of over 70,000 food and beverage products sold in seven Canadian grocery retailers, representing more than 80% of the Canadian grocery market share, were analyzed. A detailed description of FLIP 2020 is explained elsewhere (Ahmed et al., 2021). Briefly, the FLIP 2020 database was collected between May 2020 and February 2021 and contains various product information, including product name, ingredients list, Nutrition Facts table (NFt), and price (undiscounted and discounted price at the time of data collection).…”

Food Composition Databases (FCDBs): A Bibliometric Analysis

“…Moreover, the food industry could rely on FCDBs for product development and marketing, whereas consumers could make informed food choices and have increased trust in the validity of food product nutritional labels. The Food Label Information Program (FLIP) from the University of Toronto was a good example [ 6 ]. It provided comprehensive food product nutrition information (from package labels) for Canadian pre-packaged food and beverages.…”

“…The data stored in FCDBs is used by nutritionists, dietitians, and researchers to assess the nutritional quality of diets, plan meals, and evaluate how food intake is associated with health [ 5 ]. Food manufacturers can also utilize FCDBs to develop and label products [ 6 ], and policymakers can use them to derive dietary guidelines and regulations [ 7 ].…”

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

“…The assessment of dietary intake relies heavily on nutritional data sourced from food composition tables or databases, which is a crucial aspect of evaluating the nutritional value of food. However, given the ever-expanding variety of food products and the rapid evolution of the food supply chain, traditional methods are struggling to keep pace in maintaining up-to-date food composition databases ( 59 ). As big data techniques are increasingly used by various fields in non-profits, science, business, and government to collect, store, process, and analyze data, this section introduces the AI approach to managing and evaluating food composition and food labels ( 60 , 61 ).…”

“…The project demonstrated the ability to autonomously collect data from online markets, leading to the development of a precise, transparent, detailed, and adaptable food composition database. This database is essential for monitoring the constantly changing food and beverage industry landscape ( 59 ). This practical example thus underlines the pivotal role AI plays in ensuring the integrity and accessibility of data crucial for the sustainability and transparency of food systems.…”