Name
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 consumeru0027 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 consumeru0027s desires and there is scope for future developments tailored to a wider range of drink attributes. |
Year | Venue | Field |
|---|---|---|
2017 | SMC | Computer science,Atmospheric measurements,Artificial intelligence,Sensory system,Statistics,Perception,Machine learning,Orange (colour) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josie McCulloch | 1 | 13 | 4.67 |
| Svetlin Isaev | 2 | 1 | 0.77 |
| Khaled Bachour | 3 | 130 | 8.80 |
| Mohannad Jreissat | 4 | 0 | 0.34 |
| Christian Wagner | 5 | 56 | 18.39 |
| Charalampos Makatsoris | 6 | 7 | 2.58 |