Podrobná bibliografie
| Název: |
Identifying drivers of liking for Korean traditional rice wine (Yakju) across different age groups by penalty analysis based on the CATA method. |
| Autoři: |
Lee, Sanghyeok, Kwak, Han Sub, Jung, Ji‐yoon, Kim, Sang Sook, Lee, Youngseung |
| Zdroj: |
Journal of the Institute of Brewing; 2021, Vol. 127 Issue 3, p286-295, 10p |
| Témata: |
RICE wines, PARTIAL least squares regression, YOUNG consumers, AGE groups, LIKES & dislikes, CONSUMER preferences, SWEETNESS (Taste), APRICOT |
| Abstrakt: |
The Korean traditional rice wine, yakju, has been gaining popularity, especially among young consumers. However, research addressing the consumer acceptance of yakju, based on age, has been limited. This study aimed to identify the drivers of liking and disliking for yakju, across different age groups, by comparing the results of a penalty analysis (PA) based on the check‐all‐that‐apply (CATA) method, with those of the partial least squares (PLS) regression. Overall liking and CATA attributes of 12 commercial yakju samples were assessed by 166 consumers. The liking patterns and drivers of liking, obtained in each age group (20's and 30‐50's), were similar in some attributes, though not completely identical. Both groups identified sweetness and bitterness as 'must have' and 'must not have' attributes, respectively. Younger consumers preferred yakju with a green apricot flavour while the 30‐50's age group disliked the flavour of alcohol in it. There was strong agreement between the PLS regression and the CATA‐based PA, although the latter was more appropriate at the individual consumer level. It is concluded that the combined use of the two sensory methodologies would be preferable for the analysis of consumer perception, and for the optimisation of yakju manufacturing. © 2021 The Institute of Brewing & Distilling [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of the Institute of Brewing is the property of Institute of Brewing & Distilling and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: |
Complementary Index |