Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink

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dc.authoridAcik, Murat/0000-0002-3104-6306
dc.authoridBAYINDIR GUMUS, AYLIN/0000-0002-1311-2429
dc.contributor.authorGumus, Aylin Bayindir
dc.contributor.authorAcik, Murat
dc.contributor.authorDurmaz, Sevinc Eser
dc.date.accessioned2025-01-21T16:41:24Z
dc.date.available2025-01-21T16:41:24Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThis study aimed to compare the nutritional quality of beverages sold in T & uuml;rkiye according to their labeling profiles. A total of 304 nonalcoholic beverages sold in supermarkets and online markets with the highest market capacity in T & uuml;rkiye were included. Milk and dairy products, sports drinks, and beverages for children were excluded. The health star rating (HSR) was used to assess the nutritional quality of beverages. The nutritional quality of beverages was evaluated using a decision tree model according to the HSR score based on the variables presented on the beverage label. Moreover, confusion matrix tests were used to test the model's accuracy. The mean HSR score of beverages was 2.6 +/- 1.9, of which 30.2% were in the healthy category (HSR >= 3.5). Fermented and 100% fruit juice beverages had the highest mean HSR scores. According to the decision tree model of the training set, the predictors of HSR quality score, in order of importance, were as follows: added sugar (46%), sweetener (28%), additives (19%), fructose-glucose syrup (4%), and caffeine (3%). In the test set, the accuracy rate and F1 score were 0.90 and 0.82, respectively, suggesting that the prediction performance of our model had the perfect fit. According to the HSR classification, most beverages were found to be unhealthy. Thus, they increase the risk of the development of obesity and other diseases because of their easy consumption. The decision tree learning algorithm could guide the population to choose healthy beverages based on their labeling information.
dc.description.sponsorshipWe thank the supermarket staff for their assistance dur-ing the study's data collection.
dc.identifier.doi10.3746/pnf.2024.29.2.199
dc.identifier.endpage209
dc.identifier.issn2287-1098
dc.identifier.issn2287-8602
dc.identifier.issue2
dc.identifier.pmid38974584
dc.identifier.scopus2-s2.0-85197700531
dc.identifier.scopusqualityQ2
dc.identifier.startpage199
dc.identifier.urihttps://doi.org/10.3746/pnf.2024.29.2.199
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24883
dc.identifier.volume29
dc.identifier.wosWOS:001334389400012
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherKorean Soc Food Science Nutrition
dc.relation.ispartofPreventive Nutrition and Food Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectbeverages; food quality; nutrient intake; nutritive value; sugar-sweetened beverages
dc.titleHealth Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink
dc.typeArticle

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