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Many advances have occurred in the field of smart meat packaging, and the potential for these to be used as tools that respond to challenges faced by industry is exciting. Here, we review packaging solutions to several immediate concerns, encompassing dark cutting, purge and yield losses, product traceability and provenance, packaging durability, microbial spoilage and safety, colour stability, environmental impacts, and the preservation of eating quality. Different active and intelligent packaging approaches to each of these were identified and are discussed in terms of their usefulness - to processors, retailers and/or consumers. From this, it became apparent that prior to selecting a packaging solution, industry should first define their criteria for success (e.g. How much purge is too much? What is a reasonable shelf-life to facilitate product turnover? Is the customer willing to pay for this?), and understand that packaging is not the sole solution, but acts as part of a holistic response to these issues., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

Nutrient profiling (NP) models rate the nutritional quality of individual foods, based on their nutrient composition. Their goal is to identify nutrient-rich foods, generally defined as those that contain more nutrients than calories and are low in fat, sugar and salt. NP models have provided the scientific basis for evaluating nutrition and health claims and regulating marketing and advertising to children. The food industry has used NP methods to reformulate product portfolios. To help define what we mean by healthy foods, NP models need to be based on published nutrition standards, mandated serving sizes and open-source nutrient composition databases. Specifically, the development and testing of NP models for public health should follow the seven decision steps outlined by the European Food Safety Authority. Consistent with this scheme, the nutrient-rich food (NRF) family of indices was based on a variable number of qualifying nutrients (from six to fifteen) and on three disqualifying nutrients (saturated fat, added sugar, sodium). The selection of nutrients and daily reference amounts followed nutrient standards for the USA. The base of calculation was 418·4 kJ (100 kcal), in preference to 100 g, or serving sizes. The NRF algorithms, based on unweighted sums of percent daily values, subtracted negative (LIM) from positive (NRn) subscores (NRn - LIM). NRF model performance was tested with respect to energy density and independent measures of a healthy diet. Whereas past uses of NP modelling have been regulatory or educational, voluntary product reformulation by the food industry may have most impact on public health.

The food industry holds great potential for driving consumers to adopt healthy food choices as (re)formulation of foods can improve the nutritional quality of these foods. Reformulation has been identified as a cost-effective intervention in addressing non-communicable diseases as it does not require significant alterations of consumer behaviour and dietary habits. Nutrient profiling (NP), the science of categorizing foods based on their nutrient composition, has emerged as an essential tool and is implemented through many different profiling systems to guide reformulation and other nutrition policies. NP systems should be adapted to their specific purposes as it is not possible to design one system that can equally address all policies and purposes, e.g. reformulation and labelling. The present paper discusses some of the key principles and specificities that underlie a NP system designed for reformulation with the example of the Nestlé nutritional profiling system. Furthermore, the impact of reformulation at the level of the food product, dietary intakes and public health are reviewed. Several studies showed that food and beverage reformulation, guided by a NP system, may be effective in improving population nutritional intakes and thereby its health status. In order to achieve its maximum potential and modify the food environment in a beneficial manner, reformulation should be implemented by the entire food sector. Multi-stakeholder partnerships including governments, food industry, retailers and consumer associations that will state concrete time-bound objectives accompanied by an independent monitoring system are the potential solution.

In this paper, I first provide definitions of nutrient profiling and of a nutrient profile model. I set out the purposes of nutrient profiling: both general and specific. I give two examples of nutrient profile models that have been developed for regulatory purposes by the Food Standards Agency (FSA) in the UK and the WHO for its European Region - the UK FSA/Ofcom and the WHO-Euro models - and compare the way the models are constructed and function, how they have been developed, the extent to which they have been tested and validated and their use in regulation. Finally I draw some conclusions about the future use of nutrient profiling for regulatory purposes. I argue that its full potential has yet to be realised and give some reasons why. I pose some urgent research questions with respect to nutrient profiling.

The primary goal of front of pack (FOP) labelling is to help consumers make healthier choices through communication. A secondary goal is to encourage producers to improve the nutritional composition of their products. Evidence has shown that (FOP) labelling can help consumers to make healthier food choices and has been an incentive for producers to improve product composition. As FOP labelling is seen as an important tool to improve food environments for public health purposes, the WHO supports initiatives of governments to implement an FOP labelling system. Based on the experiences of a wide range of countries over many years, possible success factors for such an FOP system have been defined, six of which are discussed in the present paper and used to evaluate the Dutch Choices Programme that was started in 2006. In the course of time a large number of producers joined the programme and the logo was recognised by more than 90 % of the consumers, but by 2016 the Dutch consumer organisation argued on the basis of their own research that a quarter of the consumers did not understand the colour coding of the logo and as a result the Dutch government decided to no longer support this logo and to introduce a nutrition app. The challenge that remains is to find a system that consumers understand well and that still encourages manufacturers of food to improve product composition. New technology-based data collecting initiatives might provide the right tools to develop such a system.

Nutrient profiling aims to classify or rank foods according to their nutritional composition to assist policies aimed at improving the nutritional quality of foods and diets. The present paper reviews a French approach of nutrient profiling by describing the SAIN,LIM system and its evolution from its early draft to the simplified nutrition labelling system (SENS) algorithm. Considered in 2010 by WHO as the 'French model' of nutrient profiling, SAIN,LIM classifies foods into four classes based on two scores: a nutrient density score (NDS) called SAIN and a score of nutrients to limit called LIM, and one threshold on each score. The system was first developed by the French Food Standard Agency in 2008 in response to the European regulation on nutrition and health claims (European Commission (EC) 1924/2006) to determine foods that may be eligible for bearing claims. Recently, the European regulation (EC 1169/2011) on the provision of food information to consumers allowed simplified nutrition labelling to facilitate consumer information and help them make fully informed choices. In that context, the SAIN,LIM was adapted to obtain the SENS algorithm, a system able to rank foods for simplified nutrition labelling. The implementation of the algorithm followed a step-by-step, systematic, transparent and logical process where shortcomings of the SAIN,LIM were addressed by integrating specificities of food categories in the SENS, reducing the number of nutrients, ordering the four classes and introducing European reference intakes. Through the French example, this review shows how an existing nutrient profiling system can be specifically adapted to support public health nutrition policies.