Nutritional characterisation of foods: science-based approach to nutrient profiling. Summary report of an ILSI Europe workshop held in April 2006.
ABSTRACT The background of the workshop was the proposed EU legislation to regulate nutrition and health claims for foods in Europe. This regulation will require the development of a science-based nutrient profiling system in order to determine which foods or categories of foods will be permitted to make nutrition or health claims. Nutrient profiling can also be used to categorize foods, based on an assessment of their nutrient composition according to scientific principles. Today, various nutrient profiling schemes are available to classify foods based on their nutritional characteristics. The aim of the workshop was to discuss the work developed by ILSI Europe's expert group and to explore wider scientific aspects of nutrient profiling, including their relative effectiveness, strengths and weaknesses. In particular, the focus of the workshop was on scientific approaches to the development of nutrient profiles for the purpose of regulating nutrition and health claims. The 76 workshop participants were scientists from European academic institutions, research institutes, food standards agencies, food industry and other interested parties, all of whom contributed their thinking on this topic. The workshop reached a degree of agreement on several central points. Most participants favored a food category approach rather than an 'across the board' system for nutrient profiling. Most also felt that nutrient profiling schemes should focus on disqualifying nutrients, while taking into due account relevant qualifying nutrients. Levels of each nutrient should be clearly defined for all food categories to be profiled. Reference amounts selected for further considerations were: (1) per 100 g/100 ml, (2) legislated reference amounts, and (3) per 100 kcal. The majority of workshop participants agreed that nutrient profiling schemes should allow for a two-step decision process; step (1) identify which nutrients to take into account, and step (2) define the thresholds for these nutrients. All participants agreed that an objective validation should be conducted before implementation of nutrient profiling. This would include determination of sensitivity and specificity using "indicator foods" selected on their potential to affect major health issues. The management of any adopted system needs to allow it to be dynamic over time and revise the system when new scientific knowledge emerges. The majority of participants favored a food category approach rather than an 'across the board' system. Further work is required to identify the final list of qualifying and disqualifying nutrients for any food category that may be identified and for the selection of optimal reference amounts. It is essential that key stakeholders continue to communicate and work together on the complex issues of nutrient profiling.
SourceAvailable from: PubMed Central[Show abstract] [Hide abstract]
ABSTRACT: Breakfast cereals exhibit a wide variability in nutritional quality, and differences are not easily grasped by consumers. A simplified nutritional information system might contribute to help consumers make healthier food choices. A five-category colour label based on the Food Standards Agency Nutrient profiling system (FSA score) has been proposed in France to be implemented on the front-of-pack of foods (the five-colour nutrition label - 5-CNL). Objectives were to evaluate the ability of the 5-CNL to discriminate nutritional quality between types of breakfast cereals, within a category and in equivalent products, as well as its ability to change through product reformulation. Nutritional information was collected through an Internet and supermarket research for N = 433 breakfast cereals (N = 380 complete data included in the analyses). Breakfast cereals were categorized according to common attributes in terms of processing and/or ingredients used. The FSA score and 5-CNL category allocation were computed for each cereal. Nutrient content and FSA score were compared across types of cereals. Distribution within the 5-CNL categories was assessed across types of cereals and for equivalent products. Impact of reformulation (reduction of 5 and 10% in simple sugar, saturated fat and sodium) on the 5-CNL category allocation was compared to original allocation with Bapkhar's tests of homogeneity of marginal distribution. Variability in nutritional quality of breakfast cereals was high, as reflected by the FSA score (range -7- 22 for a theoretical range of -15-40) and the 5-CNL (all five categories represented). The 5-CNL allowed for discrimination across types of cereals, within categories of breakfast cereals and for equivalent products (at least 3 categories of the 5-CNL represented). Reformulation scenarios allowed for significant change in 5-CNL allocation: 5% reduction in sugar lead to a modification of the label for 4.21% of products while a reduction of 10% of sugar, saturated fat and sodium lead to a modification of the label for 19.2% of products. The 5-CNL adequately discriminates between breakfast cereals. It would therefore be an adequate tool for consumer information on nutritional quality of foods in the French context.BMC Public Health 12/2015; 15(1):1522. DOI:10.1186/s12889-015-1522-y · 2.32 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: An increase in dietary intake along with food marketing and changes in the local, national, and global food systems are considered by many as the primary global drivers of the obesity and noncommunicable disease (NCD) pandemics . Empirical evidence from health research  has linked long-term change in BMI to consumption of specific food categories. Abundant consumer research exists on the various ways by which the different components of marketing strategies individually and jointly impact food choice (see  for a review). However, solid theoretical and empirical foundations remain absent for which marketing business practices cause which changes in food purchase, consumption, and diet and with which obesity and diet-related health consequences. At the same time, facing the pandemic of obesity and diet-related chronic disease, policy makers, researchers, and practitioners have longed for a surveillance system that can describe and monitor food marketing activities at various levels to support research and decision making. More research about food marketing and health outcomes can then be conducted to better inform policy and intervention aimed at preventing child and adult obesity and their chronic disease sequel. Yet, there is not much solid insight on how such a system should be implemented.Diet Quality: An Evidence-Based Approach, Volume 2, 1st edited by Victor R. Preedy, Lan-Anh Hunter, Vinood B. Patel, 07/2013: chapter Part IV: pages 383-396; Springer New York., ISBN: 978-1-4614-7314-5
[Show abstract] [Hide abstract]
ABSTRACT: Nutrient profiling is used to classify foods according to their nutritional composition for various reasons, including the regulation of food labelling and advertising of foods to children. When applied to a representative sample of foods on the market, nutrient profiling has the potential to also be used to assess and monitor changes in the food market. In this study, we assessed whether data from a food composition database can be used to substitute or supplement data taken from food labels to conduct analyses using nutrient profile models. Study was performed using the Office of Communications (London, United Kingdom) nutrient profile model (Ofcom model), and Food Standards Australia New Zealand (Canberra, Australia) nutrient profile model (FSANZ model). When applying both nutrient profile models to a full sample of foods in various food categories using nutrition composition data from the nutrition declaration or from the food composition database we observed a moderate to good level of agreement between both classifications. This can be further improved, for example, by excluding some food categories or by using the energy value as an indicator of a specific product’s proper match with one in the food composition database.Journal of food and nutrition research 01/2015; 54(2). DOI:10.13140/2.1.2096.0000 · 0.44 Impact Factor