Building nutritionally meaningful product groups for loyalty card data: the LoCard Food Classification process

Abstract

Analysing customer loyalty card data is a novel method for assessing dietary quality and changes in a population’s food consumption. However, prior to its use, the thousands of grocery products available in stores must be reclassified into appropriate categories suitable for the use of nutrition and health research. This paper depicts how such a classification is compiled and how it reflects the nutritional quality of the food classes. Healthfulness was considered the main criterion guiding the reclassification of the 3574 grocery product groups. In addition, the main ingredient of the product group, type of food and purpose of use, and carbon footprint were considered in the reclassification process. The classified food groups were linked with the national food composition database, and the nutrient profile was assessed by calculating the Nutrient Rich Food Index (NRFI) for each product group. Our four-level classification hierarchy had 38 food groups at its broadest level (Class 1). Only 1% (n=38) of the grocery product groups were left unclassified. Standard deviation in NRFI decreased from 0.21 to 0.08 from the broadest to the finest level of classification. We conclude it is possible to assign a great majority of the grocery product groups to classes based on their nutritional quality. However, the challenge is classification of product groups that lack detailed information on their contents or include main ingredients that have opposite health effects, such as products including both plant- and animal-based proteins.

The dynamics in food selection stemming from price awareness and perceived income adequacy: a cross-sectional study using 1-year loyalty card data

“…We had information on the food group level, made by the retailer. Because these food groupings are not aligned with health or environmental impacts, we have conducted an excessive regrouping by a group of professional nutritionists to better serve the purpose of our studies [ 25 ]. We classified the food groups in 4 classes.…”

“…We used nutritional profiling for a quantitative analysis of the nutrient density as a proxy for healthiness of purchases [ 25 ]. We calculated the Nutrient Rich Food Index (NRFI) for each food following principles of Drewnowski and Fulgoni [ 27 ].…”

“…Recommended values used for the 11 nutrients were from the Finnish Nutrition Recommendations, which were similar to the Nordic Nutrition Recommendations 2012 [ 28 ], except salt which being ≤5 g in the Finnish recommendations (≤6 g in the Nordic Nutrition Recommendations 2012). The calculation has been described in detail elsewhere [ 25 ]. Finally, NRFI for the food group per individual was obtained by summing up the NRFIs for the food items multiplied by the volume of the purchase.…”