Check‐all‐that‐applies as an alternative for descriptive analysis to establish flavors driving liking in strawberries

Abstract

Check‐all‐that‐apply (CATA) is a rapid sensory profiling tool that can be applied by consumers, saving time and money in comparison to descriptive analysis (DA), and providing insight into the consumer. Limited research has validated CATA against DA in strawberries, and subsequently compared the sensory attributes driving liking. The aim of this research is to compare the results obtained from DA to those established via CATA using untrained consumers, and to assess any differences in attributes identified to drive liking. Trained panelists (n = 12, minimum 60 hr each panelist) and untrained consumers (n = 131) were provided with six strawberry samples (three duplicate cultivars). The trained panel applied DA techniques to profile each strawberry cultivar, and the untrained consumer panel used CATA to select all attributes applying to each sample. A second untrained consumer panel (n = 139) rated their liking of the same sample set on a hedonic general labeled magnitude scale. Results revealed CATA produced moderately comparable product configurations to DA (RV coefficient = 0.760), with similarities in descriptors associated with liking. This research has established CATA as a time and cost‐efficient alternative for the DA methodology, however, when precise definitions and subsequent quantification of the sensory attributes of products are required, DA is a more robust and reliable evaluation tool. Practical applications Rapid sensory analysis tools are becoming increasingly popular for use in industry. The use of consumers applying a check‐all‐that‐apply approach may be employed to understand consumer preferences. This research may be applied to further understand use of the consumer to profile the sensory characteristics of products when compared to a trained sensory panel. In addition, the results may validate the potential replacement of a trained panel for an untrained consumer panel in some situations where a detailed sensory profile, with precise definitions and subsequent quantification of sensory attributes, is not essential.

Use of rating scales versus check‐all‐that‐apply ballots in quantifying strain‐specific Cannabis aroma

“…The same researchers later found (Oliver, Cicerale, Pang, & Keast, ) that CATA analysis by untrained consumers produced product configurations that were “moderately comparable” to those arrived at via descriptive analysis by trained panelists. Nevertheless, the CATA results were “able to adequately distinguish between cultivars” (Oliver et al, , p. 5), even though fewer attributes were significantly applied (when compared to the QDA method). Oliver et al () concluded that CATA is a time‐ and cost‐effective alternative method, although QDA may be preferable where precise quantification of specific attributes is paramount.…”

“…It required that QDA panelists with extensive prior experience in strawberry analysis undergo additional training with product variation across different growing regions, followed by training to calibrate their judgments to specific physical standards. The same researchers later found (Oliver, Cicerale, Pang, & Keast, 2018b) that CATA analysis by untrained consumers produced product configurations that were "moderately comparable" to those arrived at via descriptive analysis by trained panelists. Nevertheless, the CATA results were "able to adequately distinguish between cultivars" (Oliver et al, 2018b, p. 5), even though fewer attributes were significantly applied (when compared to the QDA method).…”

“…Nevertheless, the CATA results were “able to adequately distinguish between cultivars” (Oliver et al, , p. 5), even though fewer attributes were significantly applied (when compared to the QDA method). Oliver et al () concluded that CATA is a time‐ and cost‐effective alternative method, although QDA may be preferable where precise quantification of specific attributes is paramount.…”

“…Consumer-based methods, such as those employed here and elsewhere (Gilbert & DiVerdi, 2018;Oliver et al, 2018b), are being used with increasing frequency to generate sensory characterizations of products (Varela & Ares, 2012). CATA ballots are perceived as being relatively nondemanding for consumers to complete, yet this advantage may fade as the number of terms on the ballot increases.…”

Sensory product characterization by consumers using check‐all‐that‐apply questions: Investigations linked to term development using kiwifruit as a case study

“…The same tendency was observed for "firm core" (22% applicability and 67% discrimination) vs. "hard core" (18% applicability and 100% discrimination). This pointed to the use of "firm" rather than "hard", and could fit with the sensory sensation of hardness in fruit being more often captured by the former term (e.g., Causse et al, 2010;Oliver, Cicerale, Pang, & Keast, 2018a;Shiu, Slaughter, Boyden, & Barrett, 2016;Threlfall, Hines, & Clark, 2016).…”