Research facilities often compare historical to newly obtained data sets. The question arises: can data from recent studies be compared to older studies in which different data collection devices were used? The objective of this study was to determine if data capture device type had a significant influence on sensory descriptive analysis results.
The objective of this study was to determine if data capture device type had a significant influence on sensory descriptive analysis results. 12 trained assessors evaluated 4 snack bar products in triplicate on each of three devices (iPod, iPad, external monitor). Four-way univariate analysis of variance detected no significant product by device interaction in 19 of 20 attributes. Products were
Two panels evaluated 6 whole grain breads in duplicate. The consumer panel (n=93), drawn from an active database, used 32 CATA sensory and emotion terms to describe samples, and gave hedonic responses. The FCM® trained descriptive panel (n=12), drawn from a pool of trained assessors, used a common lexicon of 57 defined sensory attributes. Multivariate sensory spaces for CATA and
Descriptive analysis was conducted by a trained panel on potato varieties, forty in 2010 and forty-four in 2014. The panel evaluated 52 well-defined sensory attributes on line- scales anchored at 0 and 100. Our objective was to determine if there were groups of homogeneous attributes, or “meta-attributes.”
Data collection methods and devices have changed over the years. Research facilities often compare historical to newly obtained data sets however, the question remains; can data be compared when different data collection devices were used? This study determined that descriptive analysis test results are comparable across three different devices, an iPod, an iPad and a computer monitor.
Studies that investigate drivers of consumer liking involve both descriptive sensory and consumer data collection. These two components can be run in parallel to reduce project time, but at significant expense.
Training sessions often yield a limited dataset, which in turn restricts available analyses. Gathering ideal data sets for analysis might be at odds with imperatives of training regimen. Raw data is too voluminous to consider in numerical form. Humans have excellent ability for pattern recognition. Multifunctional graphs can reveal both macro and micro structures in the data.
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
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.
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