Sensory professionals seeking guidance in best practices often turn to publications from standards organizations such as ISO and ASTM. A review of guides related to sensory equivalence testing will be presented. In several cases the power approach is prescribed for determining equivalency, but this approach is problematic. It attempts to control beta risk in the difference test and declare samples
This abstract is unavailable. Please contact us for additional information. Castura, J.C. (2010). Similarity testing & equivalence testing. In ASTM E-18.04 Seminar on Discrimination Methods. April 22. St. Louis, MO, USA.
A so-called donkey voter selects candidates according to position on an election ballot. Are untrained sensory panellists similarly influenced by position when responding to choose-all-that-apply (CATA) questions? In sensory and consumer testing, lists of choices, conventionally presented in fixed order, allow panellists to indicate sensory perceptions without requirements for scaling. Results help in understanding products and drivers of hedonic response.
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
Typically, in multivariate analysis, the relationship between products is dependent upon the samples chosen. Once the sensory space is properly defined, products may be removed and inserted without disturbing the underlying sensory dimensions. This powerful approach allows sensory scientists to predict and model product behavior.
The performance of descriptive panels is typically determined by post-hoc data analysis. Poor panel performance is determined after the fact and arrives too late to help the panel leader in training. The Feedback Calibration Method (FCM®) is an effective method for training descriptive panellists. FCM optimizes proficiency by ensuring efficient panel training.
In an era of global market competition, wine companies realize the need to understand better consumer preferences and respond to their needs effectively. At the 11th Australian Wine Industry Technical Conference Terry Lee presented a paper (Lesschaeve et al. 2002) on the use of preference mapping to define successfully the sensory preferences of wine consumers. The current study proposes a
Descriptive sensory analysis is one of the most powerful tools available to the sensory scientist. Regardless of the individual approach to descriptive analysis there are the common steps of identifying the attributes that describe the product, bringing a panel to agreement on the descriptors that are used, establishing a working scale that captures the range of intensities and practicing the
Descriptive analysis is one of the most powerful tools available to sensory scientists. However, regardless of the approach being used to analyze the sensory attributes of products, descriptive panels require significant training before the panel members, individually, and the panels collectively, become a reliable sensory instrument. There is great panel-to-panel variability and the training style of panel leaders can have