Descriptive analysis is applied to a diverse range of complex, real-world food and consumer products because the information it provides about those products is unrivalled in its richness. A common lexicon allows the descriptive sensory panel to reference sensory attributes of products undergoing study in a highly specific and consistent manner. When combined with best practices it eliminates ambiguity of
Descriptive analysis provides valuable information about the sensory properties of consumer products, but this information lacks the temporal dimensionality of real-world sensory experiences. Type II error occurs when the descriptive sensory panel fails to differentiate between products known to be discriminable. Findlay (2000) reported no meaningful reduction in beta risk when descriptive analysis on manipulated salad dressings was augmented by
Descriptive analysis provides valuable information about the sensory properties of consumer products, but this information lacks the temporal dimensionality of real-world sensory experiences. Type II error occurs when the descriptive sensory panel fails to differentiate between products known to be discriminable. Findlay (2000) reported no meaningful reduction in beta risk when descriptive analysis on manipulated salad dressings was augmented by
The performance of descriptive panels is typically determined by post-hoc data analysis. Poor panel performance is measured after the fact and often arrives too late to help the panel leader during training sessions. The feedback calibration method (FCM) optimizes proficiency by ensuring efficient panel training. A previously trained panel (Panel T) and an untrained panel (Panel U) developed and refined
Training targets can be established from product profiles that provide an objective representation of the underlying sensory characteristics of a group of products. If available to a panel leader, these training targets can be used to calibrate a panel and measure the accuracy of their responses.