Wine finish, a temporal wine property, is composed of the flavours, tastes, and mouthfeels that linger after swallowing wine and is used an indicator of wine quality. To investigate the impact of ethanol on wine finish evolution, wines with different matrix composition were created, then evaluated using Temporal Check-All-That-Apply (TCATA) methodology.
TDS data can be characterized by a series of TDS dyads that represent the selection of a single dominant attribute, which is updated over time. As such, TDS data describes the presence of different dominant attributes in food products.
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.”
It is well documented that the position of attributes in a Check-All-That-Apply (CATA) question can bias responses. As positional biases cannot be eliminated, they are balanced across products via experimental designs, ensuring each attribute appears with equal frequency in each position for each product. But what is the best way to allocate attribute list orders?
Panelist training is essential for a successful analytical sensory analysis, like Difference from Control (DFC). Panelist performance requires feedback, calibration and motivation. Typically, panelists are recruited, screened, trained and qualified before becoming part of an ongoing DFC quality panel. This study compared the impact of training on two groups of vodka quality panelists. A pool of panelists, inexperienced in vodka
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.
Sensory descriptive analysis of whisky is a valuable tool for understanding the sensory properties of products, the impact of process, aging and blending. When descriptive analysis (DA) is calibrated it can be used to compare products over time and origin. Traditionally, calibration was achieved by lengthy training of panellists and the precision of their results was limited. This made DA
Thurstonian-derived models are used widely for interpretation of sensory discrimination test results. Estimates of d´ are a signal-to-noise ratios, for which measurement sensitivity provides important context. Sensitivity is often defined descriptively (e.g. employees) rather than quantitatively (e.g. employees with sensitivity 1.2±0.2 for the relevant product category). We sought to select discrimination panelists based on quantified sensitivity estimates, and to investigate
Sequential profiling extends traditional descriptive sensory analysis by collecting data at several pre-established times during evaluation. Intensities in product properties are quantified at each time point, permitting product changes to be captured. Evaluation is conducted with panellists who are trained to identify and scale sensory attributes, which removes ambiguity.
Sensory professionals continue to face the challenge of quantifying and accounting for differences in individual panelist performance within their difference testing programs. In this presentation we discuss how recent developments in Thurstonian modeling can be used to track panelist performance over a series of (potentially non-replicated) difference tests within the same product category.