Hostname: page-component-848d4c4894-75dct Total loading time: 0 Render date: 2024-06-01T09:52:30.526Z Has data issue: false hasContentIssue false

Building nutritionally meaningful classification for grocery product groups: the LoCard Food Classification process

Published online by Cambridge University Press:  18 April 2024

Noora Kanerva*
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland
Satu Kinnunen
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland
Jaakko Nevalainen
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland Faculty of Social Sciences (Health Sciences), Tampere University, Kanslerinrinne 1, 33100 Tampere, Finland
Henna Vepsäläinen
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland
Mikael Fogelholm
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland
Hannu Saarijärvi
Affiliation:
Faculty of Management and Business, Tampere University, Kanslerinrinne 1, 33100 Tampere, Finland
Jelena Meinilä
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland
Maijaliisa Erkkola
Affiliation:
Department of Food and Nutrition, University of Helsinki, PO Box 66, 00014 Helsinki, Finland
*
*Corresponding author: Noora Kanerva, email noora.kanerva@helsinki.fi

Abstract

Analysing customer loyalty card data is a novel method for assessing nutritional 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 from the retailer’s original hierarchical structure into a structure that is suitable for the use of nutrition and health research. We created LoCard Food Classification (LCFC) and examined how it reflects the nutritional quality of the grocery product groups. Nutritional quality was considered the main criterion guiding the reclassification of the 3574 grocery product groups. Information on the main ingredient of the product group, purpose of use and carbon footprint was also used at the more granular levels of LCFC. The main challenge in the reclassification was a lack of detailed information on the type of products included in each group, and some of the groups included products that have opposite health effects. The final LCFC has four hierarchical levels, and it is openly available online. After reclassification, the product groups were linked with the Finnish food composition database, and the nutrient profile was assessed by calculating the Nutrient-Rich Food Index (NRFI) for each product group. sd in NRFI decreased from 0·21 of the least granular level to 0·08 of the most granular level of LCFC indicating that the most granular level of LCFC has more homogeneous nutritional quality. Studies that apply LCFC to examine loyalty card data with health and environmental outcomes are needed to further demonstrate its validity.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Nutrition Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Fardet, A, Rock, E, Bassama, J, et al. (2015) Current food classifications in epidemiological studies do not enable solid nutritional recommendations for preventing diet-related chronic diseases: the impact of food processing. Adv Nutr 6, 629638.10.3945/an.115.008789CrossRefGoogle Scholar
European Food Safety Authority (2015) The food classification and description system FoodEx 2 (revision 2). EFSA Support Publ 12, 804E.Google Scholar
Charrondiere, U, Stadlmayr, B, Haytowitz, D, et al. (2012) Guidelines for Checking Food Composition Data Prior to the Publication of a User Table/Database Version 1.0. Rome, Italy: FAO.Google Scholar
Finglas, PM, Berry, R & Astley, S (2014) Assessing and improving the quality of food composition databases for nutrition and health applications in Europe: the contribution of EuroFIR. Adv Nutr 5, 608S614S.10.3945/an.113.005470CrossRefGoogle ScholarPubMed
Bandy, L, Adhikari, V, Jebb, S, et al. (2019) The use of commercial food purchase data for public health nutrition research: a systematic review. PLoS One 14, e0210192.10.1371/journal.pone.0210192CrossRefGoogle ScholarPubMed
Jenneson, VL, Pontin, F, Greenwood, DC, et al. (2022) A systematic review of supermarket automated electronic sales data for population dietary surveillance. Nutr Rev 80, 17111722.10.1093/nutrit/nuab089CrossRefGoogle ScholarPubMed
Vuorinen, AL, Erkkola, M, Fogelholm, M, et al. (2020) Characterization and correction of bias due to nonparticipation and the degree of loyalty in large-scale Finnish Loyalty Card Data on grocery purchases: cohort study. J Med Internet Res 22, e18059.10.2196/18059CrossRefGoogle ScholarPubMed
Sørensen, KK, Nielsen, EP, Møller, AL, et al. (2021) Food purchases in households with and without diabetes based on consumer purchase data. Prim Care Diabetes 16, 574580.10.1016/j.pcd.2022.04.001CrossRefGoogle Scholar
Vepsäläinen, H, Nevalainen, J, Kinnunen, S, et al. (2021) Do we eat what we buy? Relative validity of grocery purchase data as an indicator of food consumption in the LoCard study. Br J Nutr 128, 17801788.10.1017/S0007114521004177CrossRefGoogle ScholarPubMed
Lintonen, T, Uusitalo, L, Erkkola, M, et al. (2020) Grocery purchase data in the study of alcohol use – a validity study. Drug Alcohol Depend 214, 108145.10.1016/j.drugalcdep.2020.108145CrossRefGoogle Scholar
Erkkola, M, Kinnunen, SM, Vepsäläinen, HR, et al. (2022) A slow road from meat dominance to more sustainable diets: an analysis of purchase preferences among Finnish loyalty-card holders. PLOS Sustain Transform 1, e0000015.10.1371/journal.pstr.0000015CrossRefGoogle Scholar
Teng, AM, Jones, AC, Mizdrak, A, Signal, L, et al. (2019) Impact of sugar-sweetened beverage taxes on purchases and dietary intake: systematic review and meta-analysis. Obes Rev 20, 11871204. https://doi.org/10.1111/obr.12868.CrossRefGoogle ScholarPubMed
Vall Castelló, J & Lopez Casasnovas, G (2020) Impact of SSB taxes on sales. Econ Hum Biol 36, 100821.10.1016/j.ehb.2019.100821CrossRefGoogle ScholarPubMed
Meinilä, J, Hartikainen, H, Tuomisto, HL, et al. (2022) Food purchase behaviour in a Finnish population: patterns, carbon footprints and expenditures. Public Health Nutr 25, 32653277.10.1017/S1368980022001707CrossRefGoogle Scholar
Willett, W Rockström, J, Loken, B, et al. (2019) Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 39, 447492.10.1016/S0140-6736(18)31788-4CrossRefGoogle Scholar
Beaglehole, R, Bonita, R, Horton, R, et al. (2011) Priority actions for the non-communicable disease crisis. Lancet 377, 14381447.10.1016/S0140-6736(11)60393-0CrossRefGoogle ScholarPubMed
Pauler, G & Dick, A (2006) Maximizing profit of a food retailing chain by targeting and promoting valuable customers using Loyalty Card and Scanner Data. Eur J Oper Res 174, 12601280.10.1016/j.ejor.2005.03.028CrossRefGoogle Scholar
Zhong, R, Xu, X & Wang, L (2017) Food supply chain management: systems, implementations, and future research. Ind Manage Data Syst 117, 20852114.10.1108/IMDS-09-2016-0391CrossRefGoogle Scholar
Carlson, A, Lino, M & Fungwe, TV (2007) The Low-Cost, Moderate-Cost, and Liberal Food Plans, 2007. CNPP Reports 45850. United States Department of Agriculture, Center for Nutrition Policy and Promotion. https://fns-prod.azureedge.us/sites/default/files/resource-files/FoodPlans2007AdminReport.pdf (accessed June 2022).Google Scholar
Todd, JE, Mancino, L, Leibtag, E, et al. (2010) Methodology Behind the Quarterly Food-at-Home Price Database. Technical Bulletin No. 1926. United States Department of Agriculture, Economic Research Service. http://www.ers.usda.gov/publications/pub-details/?pubid=47567 (accessed June 2022).Google Scholar
Peltner, J & Thiele, S (2018) Convenience-based food purchase patterns: identification and associations with dietary quality, sociodemographic factors and attitudes. Public Health Nutr 21, 558570.10.1017/S1368980017003378CrossRefGoogle ScholarPubMed
Monteiro, CA, Levy, RB, Claro, RM, et al. (2010) A new classification of foods based on the extent and purpose of their processing. Cad Saude Publica 26, 20392049.10.1590/S0102-311X2010001100005CrossRefGoogle ScholarPubMed
Drewnowski, A (2010) The Nutrient Rich Foods Index helps to identify healthy, affordable foods. Am J Clin Nutr 91, 1095S1101S.10.3945/ajcn.2010.28450DCrossRefGoogle ScholarPubMed
Brewster, PJ, Durward, CM, Hurdle, JF, et al. (2019) The Grocery Purchase Quality Index-2016 performs similarly to the healthy eating index-2015 in a national survey of household food purchases. J Acad Nutr Diet 119, 4556.10.1016/j.jand.2018.08.165CrossRefGoogle Scholar
Vadiveloo, MK, Juul, F, Sotos-Prieto, M, et al. (2022) Perspective: novel approaches to evaluate dietary quality: combining methods to enhance measurement for dietary surveillance and interventions. Adv Nutr 13, 10091015.10.1093/advances/nmac007CrossRefGoogle ScholarPubMed
Wu, J, Fuchs, K, Lian, J, et al. (2021) Estimating dietary intake from grocery shopping data-a comparative validation of relevant indicators in Switzerland. Nutrients 14, 159. https://doi.org/10.3390/nu14010159.CrossRefGoogle ScholarPubMed
Nevalainen, J, Erkkola, M, Saarijärvi, H, et al. (2018) Large-scale loyalty card data in health research. Digit Health 4, 2055207618816898.Google ScholarPubMed
Nordic Council of Ministers (2013) Nordic Nutrition Recommendations. Part 1. Summary, Principles and Use. Nord 2013/009, 5th ed. Denmark: Norden.Google Scholar
Reinivuo, H, Hirvonen, T, Ovaskainen, ML, et al. (2010) Dietary survey methodology of FINDIET 2007 with a risk assessment perspective. Public Health Nutr 13, 915919.10.1017/S1368980010001096CrossRefGoogle ScholarPubMed
European Parliament and the Council of the European Union (2006) Regulation (EC) No 1924/2006 of the European Parliament and of the Council of 20 December 2006 on Nutrition and Health Claims Made on Foods. http://data.europa.eu/eli/reg/2006/1924/oj/eng (accessed June 2022).Google Scholar
Uusitalo, L, Nevalainen, J, Rahkonen, O, et al. (2022) Changes in alcohol purchases from grocery stores after authorising the sale of stronger beverages: the case of the Finnish alcohol legislation reform in 2018. Nord Stud Alcohol Drugs 39, 589604.10.1177/14550725221082364CrossRefGoogle ScholarPubMed
Hartikainen, H & Pulkkinen, H (2016) Summary of the Chosen Methodologies and Practices to Produce GHGE-Estimates for an Average European Diet. Finland: Natural Resources Institute Finland (Luke).Google Scholar
Drewnowski, A & Fulgoni, VL (2014) Nutrient density: principles and evaluation tools. Am J Clin Nutr 99, 1223S1228S.10.3945/ajcn.113.073395CrossRefGoogle ScholarPubMed
Fulgoni, VL, Keast, DR & Drewnowski, A (2009) Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J Nutr 139, 15491554.10.3945/jn.108.101360CrossRefGoogle ScholarPubMed
Valsta, L, Kaartinen, N, Tapanainen, H, et al. (2019) Ravitsemus Suomessa – FinRavinto 2017 -tutkimus (Nutrition in Finland – The National FinDiet 2017 Survey). Report 12/2018. Helsinki, Finland: Institute for Health and Welfare (THL).Google Scholar
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (accessed October 2022).Google Scholar
Astrup, A & Monteiro, CA (2022) Does the concept of ‘ultra-processed foods’ help inform dietary guidelines, beyond conventional classification systems? NO. Am J Clin Nutr 116, 14821488.10.1093/ajcn/nqac123CrossRefGoogle ScholarPubMed
United Nations (2018) Classification of Individual Consumption According to Purpose (COICOP) 2018. Department of Economic and Social Affairs, Statistical Papers Series M No 99. New York, USA; UN. ––https://unstats.un.org/unsd/classifications/unsdclassifications/COICOP_2018_-_pre-edited_white_cover_version_-_2018–12–26.pdf (accessed September 2023).Google Scholar
Rafferty, A & Walthery, P (2014) Introductory Guide to the Living Cost and Food Survey. UK Data Service, University of Essex and University of Manchester. https://ukdataservice.ac.uk/app/uploads/livingcostsfoodsurvey.pdf (accessed September 2023).Google Scholar
Clark, SD, Shute, B, Jenneson, V, et al. (2021) Dietary patterns derived from UK supermarket transaction data with nutrient and socioeconomic profiles. Nutrients 13, 1481.10.3390/nu13051481CrossRefGoogle ScholarPubMed
Britten, P, Lyon, J, Weaver, CM, et al. (2006) MyPyramid food intake pattern modeling for the Dietary Guidelines Advisory Committee. J Nutr Educ Behav 38, S143152.10.1016/j.jneb.2006.08.004CrossRefGoogle ScholarPubMed
U.S. Department of Health and Human Services & U.S. Department of Agriculture (2005) Dietary Guidelines for Americans, 2005. Washington, DC: Government Printing Office.Google Scholar
Braesco, V, Souchon, I, Sauvant, P, et al. (2022) Ultra-processed foods: how functional is the NOVA system? Eur J Clin Nutr 76, 12451253.10.1038/s41430-022-01099-1CrossRefGoogle ScholarPubMed
Andrés-Hernández, L, Blumberg, K, Walls, RL, et al. (2022) Establishing a common nutritional vocabulary – from food production to diet. Front Nutr 9, 928837.10.3389/fnut.2022.928837CrossRefGoogle ScholarPubMed
Clark, M, Springmann, M, Rayner, M, et al. (2022) Estimating the environmental impacts of 57 000 food products. Proc Natl Acad Sci U S A 119, e2120584119.Google ScholarPubMed
Volpe, R & Okrent, A (2012) Assessing the Healthfulness of Consumers’ Grocery Purchases. Economic Information Bulletin 262129. United States Department of Agriculture, Economic Research Service. https://www.ers.usda.gov/webdocs/publications/43680/33405_eib102.pdf?v=2344.3 (accessed June 2022).Google Scholar
Drewnowski, A & Fulgoni, V III (2008) Nutrient profiling of foods: creating a nutrient-rich food index. Nutr Rev 66, 2339.10.1111/j.1753-4887.2007.00003.xCrossRefGoogle ScholarPubMed
Muir, S, Dhuria, P, Roe, E, et al. (2023) UK government’s new placement legislation is a ‘good first step’: a rapid qualitative analysis of consumer, business, enforcement and health stakeholder perspectives. BMC Med 21, 33.Google ScholarPubMed
UK Government (2021) The Food (Promotion and Placement) (England) Regulations 2021. London: UK Government. https://www.legislation.gov.uk/uksi/2021/1368/contents (accessed September 2023).Google Scholar
Supplementary material: File

Kanerva et al. supplementary material

Kanerva et al. supplementary material
Download Kanerva et al. supplementary material(File)
File 234.4 KB