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Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases

Nutrient profiling ranks foods based on their nutrient composition, with applications in multiple aspects of food policy. We tested the capacity of a category-specific model developed for product reformulation to improve the average nutrient content of foods, using five national food composition dat...

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Autores principales: Combet, Emilie, Vlassopoulos, Antonis, Mölenberg, Famke, Gressier, Mathilde, Privet, Lisa, Wratten, Craig, Sharif, Sahar, Vieux, Florent, Lehmann, Undine, Masset, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409745/
https://www.ncbi.nlm.nih.gov/pubmed/28430118
http://dx.doi.org/10.3390/nu9040406
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author Combet, Emilie
Vlassopoulos, Antonis
Mölenberg, Famke
Gressier, Mathilde
Privet, Lisa
Wratten, Craig
Sharif, Sahar
Vieux, Florent
Lehmann, Undine
Masset, Gabriel
author_facet Combet, Emilie
Vlassopoulos, Antonis
Mölenberg, Famke
Gressier, Mathilde
Privet, Lisa
Wratten, Craig
Sharif, Sahar
Vieux, Florent
Lehmann, Undine
Masset, Gabriel
author_sort Combet, Emilie
collection PubMed
description Nutrient profiling ranks foods based on their nutrient composition, with applications in multiple aspects of food policy. We tested the capacity of a category-specific model developed for product reformulation to improve the average nutrient content of foods, using five national food composition datasets (UK, US, China, Brazil, France). Products (n = 7183) were split into 35 categories based on the Nestlé Nutritional Profiling Systems (NNPS) and were then classified as NNPS ‘Pass’ if all nutrient targets were met (energy (E), total fat (TF), saturated fat (SFA), sodium (Na), added sugars (AS), protein, calcium). In a modelling scenario, all NNPS Fail products were ‘reformulated’ to meet NNPS standards. Overall, a third (36%) of all products achieved the NNPS standard/pass (inter-country and inter-category range: 32%–40%; 5%–72%, respectively), with most products requiring reformulation in two or more nutrients. The most common nutrients to require reformulation were SFA (22%–44%) and TF (23%–42%). Modelled compliance with NNPS standards could reduce the average content of SFA, Na and AS (10%, 8% and 6%, respectively) at the food supply level. Despite the good potential to stimulate reformulation across the five countries, the study highlights the need for better data quality and granularity of food composition databases.
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spelling pubmed-54097452017-05-03 Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases Combet, Emilie Vlassopoulos, Antonis Mölenberg, Famke Gressier, Mathilde Privet, Lisa Wratten, Craig Sharif, Sahar Vieux, Florent Lehmann, Undine Masset, Gabriel Nutrients Article Nutrient profiling ranks foods based on their nutrient composition, with applications in multiple aspects of food policy. We tested the capacity of a category-specific model developed for product reformulation to improve the average nutrient content of foods, using five national food composition datasets (UK, US, China, Brazil, France). Products (n = 7183) were split into 35 categories based on the Nestlé Nutritional Profiling Systems (NNPS) and were then classified as NNPS ‘Pass’ if all nutrient targets were met (energy (E), total fat (TF), saturated fat (SFA), sodium (Na), added sugars (AS), protein, calcium). In a modelling scenario, all NNPS Fail products were ‘reformulated’ to meet NNPS standards. Overall, a third (36%) of all products achieved the NNPS standard/pass (inter-country and inter-category range: 32%–40%; 5%–72%, respectively), with most products requiring reformulation in two or more nutrients. The most common nutrients to require reformulation were SFA (22%–44%) and TF (23%–42%). Modelled compliance with NNPS standards could reduce the average content of SFA, Na and AS (10%, 8% and 6%, respectively) at the food supply level. Despite the good potential to stimulate reformulation across the five countries, the study highlights the need for better data quality and granularity of food composition databases. MDPI 2017-04-21 /pmc/articles/PMC5409745/ /pubmed/28430118 http://dx.doi.org/10.3390/nu9040406 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Combet, Emilie
Vlassopoulos, Antonis
Mölenberg, Famke
Gressier, Mathilde
Privet, Lisa
Wratten, Craig
Sharif, Sahar
Vieux, Florent
Lehmann, Undine
Masset, Gabriel
Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases
title Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases
title_full Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases
title_fullStr Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases
title_full_unstemmed Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases
title_short Testing the Capacity of a Multi-Nutrient Profiling System to Guide Food and Beverage Reformulation: Results from Five National Food Composition Databases
title_sort testing the capacity of a multi-nutrient profiling system to guide food and beverage reformulation: results from five national food composition databases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409745/
https://www.ncbi.nlm.nih.gov/pubmed/28430118
http://dx.doi.org/10.3390/nu9040406
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