Linking sensory perceptions and physical properties of orange drinks

McCulloch, Josie and Isaev, Svetlin and Bachour, Khaled and Jreissat, Mohannad and Wagner, Christian and Makatsoris, Charalampos (2017) Linking sensory perceptions and physical properties of orange drinks. In: IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2017), 5-8 October 2017, Banff, Canada. (Submitted)

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (462kB) | Preview

Abstract

This paper investigates if sensory perceptions of orange drinks (e.g., acidity, thickness, wateriness) can be linked to physical measurements (e.g., pH, particle size, density). Using this information, manufactured drinks can be tailored according to consumer' desires by, for example, the consumer providing a sensory description of their preferred drink. Sensory perceptions of different juices are collected in a survey and used to determine 1) if consumers can distinguish between different drinks using the provided sensory descriptors, and 2) if the perceptions match to physical measurements of the drinks. Results show that most of the given sensory descriptors are useful in describing differences in orange drinks. Additionally, the perceived wateriness and thickness of the drinks can be predicted from measurements. However, the perceived acidity could not be reliably predicted. The results show that personally tailored orange beverages can be manufactured according to some of the consumer's desires and there is scope for future developments tailored to a wider range of drink attributes.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ISSN 0884-3627.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
Depositing User: Mcculloch, Josie
Date Deposited: 30 Nov 2017 13:37
Last Modified: 30 Nov 2017 19:02
URI: http://eprints.nottingham.ac.uk/id/eprint/48445

Actions (Archive Staff Only)

Edit View Edit View