Dataset Information

Labelling Assessment of Greek "Quality Label" Prepacked Cheeses as the Basis for a Branded Food Composition Database.

ABSTRACT: A labelling assessment study of Greek prepacked "quality label" cheeses was conducted with a view to provide an overview of the whole category. In total, 158 prepacked products belonging to 19 "quality label" cheeses were identified in the Greek market. Among them, Feta had the highest share followed by Kasseri, Graviera Kritis, Kefalograviera and Ladotyri Mitilinis with 81, 16, 15, 11 and 9 products found in the market, respectively. For the rest of the 14 cheeses, the share was limited, ranging from 1 to 4. All labelling indications, nutritional information, claims and other labelling data were recorded and analysed in relation to their compliance against European food law requirements. The results of the analysis showed that for only 6 of the 19 cheeses, all products fully complied with EU labelling legislation. Among the 14 mandatory labelling requirements, the lowest overall compliance was observed for allergens declaration (65%). The analysis of the nutritional data showed a remarkable variability between cheeses and products. Differences in the nutritional characteristics were more pronounced among soft, semi-hard, hard and whey cheese. The above data were entered into an archival database. Application of global harmonisation and standardisation guidelines and tools lead to the initialisation of a branded food composition database (BFCD), conceptualising a specialised database for "quality label" foods.

SUBMITTER: Katsouri E

PROVIDER: S-EPMC8747277 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Front-of-package nutrition labelling (FOPNL) is known as an effective tool that can encourage healthier food choices and food reformulation. A very interesting type of FOPNL is grading schemes. Our objective was to compare two market-implemented grading schemes-European Nutri-Score (NS) and Australian Health Star Rating (HSR), using large Slovenian branded foods database. NS and HSR were used for profiling 17,226 pre-packed foods and drinks, available in Slovenian food supply dataset (2020). Alignment between models was evaluated with agreement (% of agreement and Cohen's Kappa) and correlation (Spearman rho). The 12-month nationwide sales-data were used for sale-weighing, to address market-share differences. Study results indicated that both models have good discriminatory ability between products based on their nutritional composition. NS and HSR ranked 22% and 33% of Slovenian food supply as healthy, respectively. Agreement between NS and HSR was strong (70%, κ = 0.62) with a very strong correlation (rho = 0.87). Observed profiling models were most aligned within food categories Beverages and Bread and bakery products, while less aligned for Dairy and imitates and Edible oils and emulsions. Notable disagreements were particularly observed in subcategories of Cheese and processed cheeses (8%, κ = 0.01, rho = 0.38) and Cooking oils (27%, κ = 0.11, rho = 0.40). Further analysis showed that the main differences in Cooking oils were due to olive oil and walnut oil, which are favoured by NS and grapeseed, flaxseed and sunflower oil that are favoured by HSR. For Cheeses and cheese products, we observed that HSR graded products across the whole scale, with majority (63%) being classified as healthy (≥3.5 *), while NS mostly graded lower scores. Sale-weighting analyses showed that offer in the food supply does not always reflect the sales. Sale-weighting increased overall agreement between profiles from 70% to 81%, with notable differences between food categories. In conclusion, NS and HSR were shown as highly compliant FOPNLs with few divergences in some subcategories. Even these models do not always grade products equally high, very similar ranking trends were observed. However, the observed differences highlight the challenges of FOPNL ranking schemes, which are tailored to address somewhat different public health priorities in different countries. International harmonization can support further development of grading type nutrient profiling models for the use in FOPNL, and make those acceptable for more stake-holders, which will be crucial for their successful regulatory implementation.

Project description:indicator front-of-package nutrition labelling schemes are gaining momentum. In Europe, an example of such a scheme is Nutri-Score, which was first introduced in France. Supported by additional research, the scheme has the potential to expand into other countries. Such a scenario opens a series of questions related to the use of Nutri-Score in the territories with pre-existing food labelling schemes. A key question is whether different nutrition labelling schemes would provide conflicting information for consumers when applied to same foods. The goal of our study was, therefore, to evaluate the alignment of different front-of-package nutrition labelling schemes. The study was conducted using cross-sectional data on the composition of selected categories of prepacked foods with high penetration nutrition/health claims and symbols in the Slovenian food supply. We evaluated a variety of existing front-of-package nutrition labelling schemes: three interpretive nutrition rating systems (Nutri-Score, Health Star Rating (HSR), Traffic light system), four health symbols (Protective Food symbol, Choices, Finnish heart, and Keyhole symbol), and also three nutrient profile models developed for other purposes (Office of Communications (United Kingdom, Ofcom), World Health Organization Regional office for Europe (WHOE) and Food Standards Australia New Zealand (FSANZ)). Overall, our results indicate that interpretive nutrition rating systems (i.e., Nutri-Score) are mostly less strict than the nutrient profiles of tested health symbols. A risk of conflicting information would happen in a scenario where food is eligible to carry a health symbol, but is at the same time rated to have lower nutritional quality by an accompanying interpretive nutrition rating system. When Protective Food symbol and Nutri-Score are used together, this would occur for 5% of foods in our sample. To avoid such risks, schemes for health symbols could be adapted to be stricter than interpretive nutrition rating systems used in the same territory/market, but such adaptations are challenging and should be well planned. While our study showed that, in most cases, Nutri-Score is a less strict model than tested health symbols, the rating-system approach might offer useful support and incentive for food producers towards gradual food reformulation.

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Labelling Assessment of Greek "Quality Label" Prepacked Cheeses as the Basis for a Branded Food Composition Database.

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