Cross-Classification Analysis of Food Products Based on Nutritional Quality and Degree of Processing

Author:

Abreu Sandra123ORCID,Liz Martins Margarida456ORCID

Affiliation:

1. School of Life Sciences and Environment, University of Trás-os-Montes, and Alto Douro (UTAD), 5000-801 Vila Real, Portugal

2. Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal

3. Laboratory for Integrative and Translational Research in Population Health, 4050-600 Porto, Portugal

4. Polytechnic Institute of Coimbra, Coimbra Health School (ESTeSC), 3045-093 Coimbra, Portugal

5. GreenUPorto—Sustainable Agrifood Production Research Centre, 4200-465 Vairão, Portugal

6. Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), 5000-801 Vila Real, Portugal

Abstract

This study aims to compare the classification of foods available in the Portuguese market using Nutri-Score and NOVA classifications and to analyse their ability to discriminate the fat, saturated fat, sugar, and salt content of foods. A sample of 2682 food products was collected. The nutritional quality of foods was established using the Nutri-Score, classifying them into five categories (from A to E). The NOVA classification was used to classify foods according to the degree of food processing into unprocessed/minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods (UPF). The nutritional content of food products was classified using a Multiple Traffic Light label system. It was observed that 73.7% of UPF were classified as Nutri-Score C, D, and E, 10.1% as Nutri-Score A, and 16.2% as Nutri-Score B. Nutri-Score was positively correlated with NOVA classification (ρ = 0.140, p < 0.001) and with the Multiple Traffic Lights system (ρTotal Fat = 0.572, ρSaturated Fat = 0.668, ρSugar = 0.215, ρSalt = 0.321, p < 0.001). NOVA classification negatively correlated with the Multiple Traffic Lights system for total fat (ρ = −0.064, p < 0.001). Our findings indicate the presence of many UPFs in all Nutri-Score categories. Since food processing and nutritional quality are complementary, both should be considered in labelling.

Publisher

MDPI AG

Subject

Food Science,Nutrition and Dietetics

Reference51 articles.

1. GBD Risk Factors Collaborators (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet, 396, 1223–1249.

2. Micha, R., Shulkin, M.L., Penalvo, J.L., Khatibzadeh, S., Singh, G.M., Rao, M., Fahimi, S., Powles, J., and Mozaffarian, D. (2017). Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: Systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE, 12.

3. Food based dietary patterns and chronic disease prevention;Schulze;BMJ,2018

4. Institute for Health Metrics and Evaluation (2022, November 02). Global Burden of Disease Study 2019: GBD Results Tool|GHDx. Available online: http://ghdx.healthdata.org/gbd-results-tool.

5. World Health Organization (2019). Guiding Principles and Framework Manual for Front-of-Pack Labelling for Promoting Healthy Diets, WHO.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3