Sensory evaluation, which uses human senses, is broadly classified into analytical sensory evaluation, which obtains objective evaluation results from a trained panel, and preference-based sensory evaluation, which obtains subjective results from general consumers. On the other hand, in recent years, many new methods (Rappid Methods) have been adopted that can perform evaluation analysis with a certain degree of accuracy while reducing the time and cost of panel training and evaluation. One of the Rappid Methods, the Free Choice Profile (FCP), is a method in which evaluators evaluate samples based on their own evaluation terms, and has the advantage of directly capturing consumers' natural perceptions and expressions while omitting prior panel training. The data obtained by each individual using different terms and scales is integrated using multivariate analysis and generalized Procrustes analysis (GPA), and interpreted as a common perceptual space among samples.
This time, in order to perform GPA analysis, which is not included in the JMP analysis menu, and analyze FCP data, we created a script using a generation AI and made the analysis possible by linking JMP and Python. We would like to present the results and interpretation of the FCP data.
