Sensory quality control is an essential mechanism for ensuring the sensory integrity of a product is not compromised. By conducting sensory testing throughout the distilling process it becomes possible to detect and reject faulty raw ingredients and intermediate products before these advance to the next stages, resulting in further contamination.
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
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
The cost and time required for training descriptive analysis panels is often cited as a major barrier to the routine application of descriptive sensory analysis. Compusense FCM® was developed as a method to accelerate the training of descriptive panels and to provide a mechanism for calibration that would stabilize descriptive analysis data over time and across panels.
Training targets were established using descriptive analysis profiles of 20 commercial red wines produced by a well-trained, experienced determination panel. After recruitment, screening and a basic sensory orientation of ten 2 h common training sessions, 16 inexperienced panelists were divided by lottery into two panels. The control panel received a more conventional performance debriefing at the end of each training
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.
Compusense FCM® (feedback calibration method) has been shown to be an effective tool to train descriptive analysis panels. The key to making this method work is providing “true” information, feedback, to panellists at the time they evaluate the attribute. This permits immediate calibration of the response. If the feedback is either trivial or incorrect, the panellist may be confused and the
Two panels, one composed of experienced red wine panellists (Panel T), the other of panellists without experience in sensory analysis (Panel U), were recruited and trained to evaluate white wine. Each used the Wine Aroma Wheel to develop white wine lexicons over five 2.5h training sessions. Panels T and U used 110 and 76 line scale attributes, respectively. Each panel
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.