Background In the NOVA classification system, descriptive criteria are used to assign foods to one of four groups based on processing-related criteria. Although NOVA is widely used, its robustness and functionality remain largely unexplored. We determined whether this system leads to consistent food assignments by users. Methods French food and nutrition specialists completed an online survey in which they assigned foods to NOVA groups. The survey comprised two lists: one with 120 marketed food products with ingredient information and one with 111 generic food items without ingredient information. We quantified assignment consistency among evaluators using Fleiss’ κ (range: 0–1, where 1 = 100% agreement). Hierarchical clustering on principal components identified clusters of foods with similar distributions of NOVA assignments. Results Fleiss’ κ was 0.32 and 0.34 for the marketed foods (n = 159 evaluators) and generic foods (n = 177 evaluators), respectively. There were three clusters within the marketed foods: one contained 90 foods largely assigned to NOVA4 (91% of assignments), while the two others displayed greater assignment heterogeneity. There were four clusters within the generic foods: three clusters contained foods mostly assigned to a single NOVA group (69–79% of assignments), and the fourth cluster comprised 28 foods whose assignments were more evenly distributed across the four NOVA groups. Conclusions Although assignments were more consistent for some foods than others, overall consistency among evaluators was low, even when ingredient information was available. These results suggest current NOVA criteria do not allow for robust and functional food assignments.

BackgroundFood-Based Dietary Guidelines (FBDGs) are developed to promote healthier eating patterns, but increasing food prices may make healthy eating less affordable. The aim of this study was to design a range of cost-minimized nutritionally adequate health-promoting food baskets (FBs) that help prevent both micronutrient inadequacy and diet-related non-communicable diseases at lowest cost.MethodsAverage prices for 312 foods were collected within the Greater Copenhagen area. The cost and nutrient content of five different cost-minimized FBs for a family of four were calculated per day using linear programming. The FBs were defined using five different constraints: cultural acceptability (CA), or dietary guidelines (DG), or nutrient recommendations (N), or cultural acceptability and nutrient recommendations (CAN), or dietary guidelines and nutrient recommendations (DGN). The variety and number of foods in each of the resulting five baskets was increased through limiting the relative share of individual foods.ResultsThe one-day version of N contained only 12 foods at the minimum cost of DKK 27 (€ 3.6). The CA, DG, and DGN were about twice of this and the CAN cost ~DKK 81 (€ 10.8). The baskets with the greater variety of foods contained from 70 (CAN) to 134 (DGN) foods and cost between DKK 60 (€ 8.1, N) and DKK 125 (€ 16.8, DGN). Ensuring that the food baskets cover both dietary guidelines and nutrient recommendations doubled the cost while cultural acceptability (CAN) tripled it.ConclusionUse of linear programming facilitates the generation of low-cost food baskets that are nutritionally adequate, health promoting, and culturally acceptable.

The category of “ultraprocessed” foods in the NOVA food classification scheme is ostensibly based on industrial processing. We compared NOVA category assignments with the preexisting family of Nutrient Rich Food (NRF) indices, first developed in 2004. The NRF indices are composed of 2 subscores: the positive NR based on protein, fiber, and vitamins and minerals, and the negative LIM subscore based on saturated fat, added sugars, and sodium. The 378 foods that were components of the widely used Fred Hutchinson Cancer Center food frequency questionnaire were assigned to NOVA categories and scored using multiple NRF indices. Contrary to published claims, NOVA was largely based on the foods' content of saturated fat, added sugars, and sodium. There were strong similarities between NOVA categories and NRF scores that were largely driven by the nutrients to limit. Nutrient density led to higher increased NRF scores but had less impact on NOVA categories. As a result, the NOVA scheme misclassified some nutrient-rich foods. We conclude that the NOVA classification scheme adds little to the preexisting nutrient profiling models. The purported links between NOVA categories and health outcomes could have been obtained using preexisting NRFn.3 nutrient density metrics.

Unhealthy eating is more prevalent among women and people with a low socioeconomic status. Policies that affect the price of food have been proposed to improve diet quality. The study's objective was to compare the impact of food price policies on the nutritional quality of food baskets chosen by low-income and medium-income women. Experimental economics was used to simulate a fruit and vegetable subsidy and a mixed policy subsidizing healthy products and taxing unhealthy ones. Food classification was based on the Score of Nutritional Adequacy of Individual Foods, Score of Nutrients to Be Limited nutrient profiling system. Low-income (n = 95) and medium-income (n = 33) women selected a daily food basket first at current prices and then at policy prices. Energy density (ED) and the mean adequacy ratio (MAR) were used as nutritional quality indicators. At baseline, low-income women selected less healthy baskets than medium-income women (less fruit and vegetables, more unhealthy products, higher ED, lower MAR). Both policies improved nutritional quality (fruit and vegetable quantities increased, ED decreased, the MAR increased), but the magnitude of the improvement was often lower among low-income women. For instance, ED decreased by 5.3% with the fruit and vegetable subsidy and by 7.3% with the mixed subsidy, whereas decreases of 13.2 and 12.6%, respectively, were recorded for the medium-income group. Finally, both policies improved dietary quality, but they increased socioeconomic inequalities in nutrition.

Background/objectivesIn response to the European regulation on nutrition and health claims, France proposed in 2008 the SAIN,LIM profiling system that classifies foods into four classes based on a nutrient density score called ‘SAIN’, a score of nutrients to limit called ‘LIM’, and one primary threshold on each score. We present here the SENS algorithm, a new nutrient profiling system adapted from the SAIN,LIM to be operational for simplified nutrition labelling in line with the European regulation on food information to consumers.Subjects/methodsThe main changes made to SAIN,LIM to get SENS were to introduce food categories and sub-categories (‘Beverages’, ‘Added Fats’ and ‘Other Solid Foods’ sub-categorised into ‘cereals’, ‘cheese’, ‘other dairy products’, ‘eggs’, ‘fish’ and ‘others’), reduce the number of nutrients, introduce category-specific nutrients and category-specific weighting for some nutrients, replace French recommendations with European reference intakes, and add secondary thresholds. Each food and non-alcoholic beverage from the 2013-CIQUAL French composition database (n = 1065) was assigned one SENS class. Distribution of foods according to the four SENS classes was described by food groups (n = 26).ResultsThe SENS classification was consistent with the recommendations to consume large amounts of whole grains, vegetables and fruits, and moderate intake of fats, sugars, meats, caloric beverages and salt. For most groups (19/26), foods were distributed across at least three SENS classes.ConclusionsThe SENS is a nutrition-sensitive system that discriminates foods between and within food categories. It preserves the strengths of the initial SAIN,LIM while making it operational for simplified nutrition labelling in Europe.

In response to the inclusion in the French Act on the modernization of the health system of a principle of a simplified nutrition labelling system, two nutrient profiling systems have been developed to classify foods according to their nutritional composition: the 5-colour labelling system (5-C), recently adopted by the French Ministry of Health and the French Simplified Nutritional Labelling System (SENS), adapted from the SAIN,LIM, developed by the ANSES. The aim of this study was to compare rankings yielded under these three systems based on recipe data (n = 101), half of them from an online meal planner developed by The French National Nutrition and Health Program (n = 51) and half from meals served in elementary schools (n = 50). Side-by-side comparison of the three system using the kappa statistic of agreement showed coefficients varying from 0.44 to 0.57, with only 35% of the recipes identically classified by the three systems. Discrepancies were identified between the three French nutrient profiling systems (SAIN,LIM, SENS and 5-C), although all of them have been developed for the purpose of classifying foods according to their nutritional composition

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