Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study.

Autor:	Gümüş AB; First and Emergency Aid Program, Vocational School of Health Services, Kırıkkale University, Kırıkkale 71450, Türkiye., Açık M; Department of Nutrition and Dietetics, Faculty of Health Sciences, Fırat University, Elazığ 23200, Türkiye., Durmaz SE; Department of Nutrition and Dietetics, Faculty of Health Sciences, Kırıkkale University, Kırıkkale 71450, Türkiye.
Jazyk:	angličtina
Zdroj:	Preventive nutrition and food science [Prev Nutr Food Sci] 2024 Jun 30; Vol. 29 (2), pp. 199-209.
DOI:	10.3746/pnf.2024.29.2.199
Abstrakt:	This study aimed to compare the nutritional quality of beverages sold in Türkiye according to their labeling profiles. A total of 304 nonalcoholic beverages sold in supermarkets and online markets with the highest market capacity in Türkiye 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. Competing Interests: AUTHOR DISCLOSURE STATEMENT The authors declare no conflict of interest. (Copyright © 2024 by The Korean Society of Food Science and Nutrition.)
Databáze:	MEDLINE
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