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• Applicability of RATA in combination with JAR in product development was explored. • Attribute selection frequency increased when a rating-task was included. • Correspondence analysis revealed similar spatial configurations between the four conditions. • In RATA-JAR, rating and JAR were correlated for most attributes, but not for all. • The added value of RATA-JAR compared to RATA and CATA-JAR depends on objective. Combining rapid sensory profiling methods Check-All-That-Apply (CATA) or Rate-All-That-Apply (RATA) with Just-About-Right (JAR) might provide insights on attribute presence and intensity, which can be useful during product development. We explored combining RATA and JAR in comparison to CATA, CATA-JAR and RATA. Participants (n=73) were randomly divided into four conditions: CATA (n=18), RATA (n=18), CATA-JAR (n=19) and RATA-JAR (n=18). Participants received three different bean snacks in random order. Each condition included 24 product attributes and questions about the length and difficulty of the questionnaire. Attribute selection frequency differed significantly for all three products between conditions RATA-JAR (71.3%-78.5%), RATA (74.3%-78.2%) and CATA (52.3%-54.9%), CATA-JAR (54.5%-60.1%). The percentage of attributes with a significant difference between samples, differed slightly between conditions (RATA-JAR: 29.2% versus CATA: 29.2%, CATA-JAR: 37.5%, RATA: 33.3%). Correspondence analysis based on the selection frequency contingency tables showed large similarities between conditions. The rating-task was not correlated to the JAR-task for all attributes. In RATA-JAR significantly more attributes were selected as ‘too little’ compared to CATA-JAR. JAR can provide additional input for product optimization when combined with CATA or RATA. RATA-JAR adds value when information on intensity and the liking of that intensity is needed. However, the added benefit of RATA-JAR compared to CATA-JAR is questionable and might even distort the input for product development, as the selection of the majority of attributes might make it more difficult to pinpoint which attributes are essential. Future research with more participants and products from different categories is recommended.
Stokkom et al. (Sat,) studied this question.
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