Identifying ideal product composition of chocolate‐flavored milk using preference mapping. Issue 7 (22nd June 2021)

• Record Type: Journal Article
• Authors: Mahato, Dipendra Kumar; Oliver, Penelope; Keast, Russell; Liem, Djin Gie; Russell, Catherine Georgina; Mohebbi, Mohammadreza; Cicerale, Sara; Mahmud, MM Chayan; Gamlath, Shirani
• Is Part Of: Journal of food science. Volume 86:Issue 7(2021)
• Page Start: 3205
• Page End: 3218
• Publication Date: 2021-06-22
• Subjects: chocolate milk -- consumer acceptance -- preference mapping -- sensory analysis; Food -- Periodicals; Food -- Research -- Periodicals; Food -- Periodicals; Research -- Periodicals; Levensmiddelen; Voeding; 664
• DOI: 10.1111/1750-3841.15817
• Languages: English
• ISSNs: 0022-1147

Abstract

Abstract : Abstract: The World Health Organization (WHO) recommends cutting down added sugar in processed foods and beverages. There is a growing concern for increased calorie intake from added sugar in flavored milk. To understand how to effectively reduce added sugar in flavored milk, the influence of other nutritional components such as percentage of fat, protein, and salt need to be accounted for as they are the determinants for color, flavor and mouthfeel attributes that ultimately influence consumer liking. Therefore, this study aims to identify the ideal product composition of commercial chocolate flavored milk samples and to link the existing variations to consumers' liking data using External Preference Mapping (EPM). Thirty‐four commercial chocolate milk samples systematically varying in compositional profile (sugar, fat, protein, salt, stabilizer and sweetener) were analysed by Principal Component Analysis (PCA) with a subset of 10 chocolate milk samples varying in product composition (fat, protein, salt and sugar) selected for consumer evaluation. Consumers ( n = 235) evaluated these 10 chocolate milk samples for overall liking using a 9‐point hedonic scale. Significant differences ( p < 0.0001) were observed in the hedonic ratings of these 10 samples, and consumers were grouped into four clusters using Hierarchical Cluster Analysis (HCA), each cluster showing a preference for different products. EPM was applied to the consumer liking data to reveal the ideal product composition of chocolate milk acceptability. Chocolate milk samples 4 (OAK chocolate) and 6 (OAK thick death by chocolate) with 10.6%–10.9% sugar, 3.4%–3.5% fat, 3.5%–3.6% protein and 0.05% salt were identified to have the highest appeal satisfying between 70% and 80% of consumers assessed. Practical Application: The results of this research could be applied for the reformulation of a new product to ensure that the product composition including sugar concentration is critical to influencing the sensory perception and consumer acceptance of the product. In addition, the results provide an ideal product composition along with sugar concentration that can be used as a standard or control mark for sugar reduction target.