The sensory profile of strawberries is documented as being influenced by cultivar, maturation stage, and environment. Thus, internationally bred strawberries grown in Australia will have a flavor profile adapted to the Australian environment. The objective of this research was to devise a strawberry lexicon relevant to the sensory characteristics of commercially available strawberries grown in Australia. A quantitative descriptive analysis (QDA) trained panel developed a lexicon consisting of two appearance, eight aroma, ten flavor, and five textural/mouthfeel attributes to describe the strawberries. The panel subsequently applied the developed lexicon to describe the sensory attributes of six strawberry cultivars of two flowering types, grown in Australia and harvested at two different maturation stages. A principal component analysis (PCA) determined three dimensions of which the attributes explained 86% of the variance. There were significant differences identified between strawberries, attributable to flowering type, cultivar and maturation stage, validating the developed lexicon to discriminate strawberries. Practical applications Strawberries are a commonly consumed berry, with an abundance of cultivars grown worldwide. The sensory profile of strawberries is dependent upon the cultivar and environmental conditions of which the strawberry is grown. This research has identified differences attributable to maturation stage, cultivar, and flowering type. Therefore, the development of a sensory lexicon via this research, applicable to the environmental conditions where cultivars are grown, will assist ongoing breeding programs in developing strawberries expressing flavors shown to be associated with liking.

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.

The results of this research could be applied to breeding programs, to ensure newly bred cultivars express characteristics that were identified as well-liked amongst consumers. In addition, this research provides evidence for marketing strawberries by cultivar, to assist consumers in identifying those strawberries for which they have a preference.

Napping has recently been gaining popularity as a rapid descriptive profiling method, primarily for the reduction in cost and time when compared with traditional descriptive analysis. Questions remain regarding the accuracy of data from untrained consumers and how this differs from a trained panel. The aim of this research is to compare results obtained from a Quantitative Descriptive Analysis (QDA) strawberry‐trained panel with untrained consumers applying Napping combined with ultra‐flash profile (UFP). Six strawberry samples (three duplicate cultivars) were assessed. Untrained consumers (n = 131) used Napping to separate strawberries based on their similarities and differences. Trained panelists (n = 12, minimum 60 hr training), applied QDA and Napping on two separate occasions. Results revealed Napping with UFP to produce product configurations comparable to QDA (RV coefficients of 0.936 and 0.898 between QDA and Napping via a trained panel, and a consumer panel, respectively), with similar descriptive terms to describe products. With a reduction in the number of consumers applying Napping with UFP, however, the similarity between methodologies declined. Furthermore, a lack of common understanding of attribute definitions limited its application. Therefore, Napping with UFP applied by a larger pool of assessors has been deemed an appropriate alternative to QDA when time is limited. Practical application The comparison of the Napping with ultra‐flash profile (UFP) methodology to traditional descriptive analysis will provide insight into consumer perception, and how this differs from an expertly trained panel. Upon evaluating this technique, Napping with UFP using untrained consumers may prove to be a valid instrument in objectively profiling the flavor of non‐homogenous agricultural products, potentially proving a viable alternative to the traditional QDA methods in future research of these products.

A comparison of TDS to traditional descriptive analysis indicate that TDS provides additional information to QDA™ regarding the lingering component of eating. The QDA™ results however provide more precise detail regarding singular attributes. Therefore, the TDS methodology has an application in industry when it is important to understand the lingering profile of products. However, this methodology should not be employed as a replacement to traditional descriptive analysis methods.