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. 2017 Apr 21;9(4):406.
doi: 10.3390/nu9040406.

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

Emilie Combet  1 , Antonis Vlassopoulos  2 , Famke Mölenberg  3   4 , Mathilde Gressier  5 , Lisa Privet  6 , Craig Wratten  7 , Sahar Sharif  8 , Florent Vieux  9 , Undine Lehmann  10 , Gabriel Masset  11
Affiliations

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

Emilie Combet et al. Nutrients. .

Abstract

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.

Keywords: food composition database; food supply; nutrient profiling; reformulation.

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Conflict of interest statement

The research presented herein was funded by Nestec SA, Lausanne, Switzerland the funder provided support in the form of salaries for authors U.L., G.M., A.V., and F.M. The funder did not have any additional role in the study design, data collection and analysis, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘Author Contributions’ section. The funder agreed on the publication of the present study.

Figures

Figure 1
Figure 1
Overall pass rate (proportion of products meeting the Nestlé Nutritional Profiling System criteria) across all databases.
Figure 2
Figure 2
Pass rate for the 11 major categories (proportion of products meeting the NNPS criteria) across all databases (weighted average, min, max).
Figure 3
Figure 3
Average minimum change (%) required in NNPS fail products to achieve NNPS standards (circle), and overall modelled change (%) in all products amenable to reformulation (squares), across all five national datasets (weighted average, min, max). Circles: change required in NNPS Fail products for the specific nutrient; squares: overall change in the average nutrient content across all products analysed.
See this image and copyright information in PMC

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