Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration
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 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.
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