Cargando…

Combination of the Check-All-That-Apply (CATA) Method and Just-About-Right (JAR) Scale to Evaluate Korean Traditional Rice Wine (Yakju)

This study aimed to compare a variant of the check-all-that-apply (CATA) method, CATA with just-about-right (JAR) scales (CATA-JAR), with the CATA and rate-all-that-apply (RATA) methods for evaluating 12 Korean traditional rice wines (yakju). All consumers (n = 312) assessed each sample on a 9-point...

Descripción completa

Detalles Bibliográficos
Autores principales: Lee, Sanghyeok, Kwak, Han Sub, Kim, Sang Sook, Lee, Youngseung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394306/
https://www.ncbi.nlm.nih.gov/pubmed/34441672
http://dx.doi.org/10.3390/foods10081895
Descripción
Sumario:This study aimed to compare a variant of the check-all-that-apply (CATA) method, CATA with just-about-right (JAR) scales (CATA-JAR), with the CATA and rate-all-that-apply (RATA) methods for evaluating 12 Korean traditional rice wines (yakju). All consumers (n = 312) assessed each sample on a 9-point hedonic scale and were asked to fill out the CATA, RATA, or CATA-JAR questionnaire using a 5-point JAR scale. The frequency and percentage of terms with significant differences among CATA-JAR samples were significantly higher than those for the CATA method. The regression vector (RV) between the sample and term configurations of the three methods were all over 0.84, indicating that all methods were similar in terms of product and term usage. Regarding the stability of the sample configurations, CATA-JAR could derive a stable value with the lowest number of consumers (n = 25). For the CATA-JAR method, significant penalties for each attribute and product were successfully calculated using the t-test and bootstrapping technique, to identify any attribute detrimental to liking for each product. Overall, considering its better performance in discriminating products and stability, the CATA-JAR method may be used when comparing samples with subtle differences in attributes.