Evaluating the Performance of Check-All-That-Apply (CATA) Questionnaires for Remote Emotional and Sensory Characterization in Home-Use T

The growing demand for rapid, consumer-centric sensory methodologies has driven the application of Check-AllThat-Apply (CATA) questions beyond the food domain. This study investigated the methodological performance, functionality, and discriminatory power of the CATA approach for the combined sensory and emotional profiling of non-food matrices via a digital Home-Use Test (HUT) protocol. Two distinct topical cosmetic formulations, featuring altered olfactory profiles and lipid bases, were evaluated by a consumer panel within an ecological, home-use environment. Sensory and emotional data, captured using a predefined ballot of 21 attributes, were collected remotely. Differences in attribute and conceptual citation frequencies between formulations were statistically evaluated using Cochran’s Q test (α = 0.05). The results demonstrated that the remote CATA method achieved robust and statistically significant discrimination between the non-food formulations. Formulation A wasstrongly associated with desirable characteristics and positive emotional prompts, including pleasant color, sweet fragrance, and a smooth feeling, whereas Formulation B was primarily linked to mild fragrance and visible oiliness. Neither sample elicited associations with unusual color or sandy texture. These findings validate the suitability of CATA for capturing complex multisensory and emotional interactions outside traditional laboratory settings. Furthermore, this research demonstrates that digital CATA-based HUT frameworks offer a reliable, scalable, and cost-effective alternative to traditional descriptive analysis, capturing authentic consumer behavior under real-world usage conditions without requiring trained panels. While there is already widespread adherence to the CATA method and extensive application for food and beverage products in general. This study successfully extends its functional framework to food and non-food matrices, proving it to be an equally powerful tool for industrial screening, product optimization, and sensory-driven innovation for industries.