REDUCING THE NOISE CONTAINED IN DESCRIPTIVE SENSORY DATA

A data reduction protocol was designed to minimize distortion inherent in sensory data. Following removal of nonexistent attributes and treatment levels, extreme value analysis and distribution comparisons combined with graphical respresentations, facilitated elimination of inconsistent (with respect to overall consensus) panelists. Application of a calibration factor showed superresponsive panelists (those with intensity values consistently higher than other panelists) were among the most accurate and thus were retained in spite of their tendency to produce extreme value data. Panelists that consistently produced a narrow variance around the overall mean and rarely produced extreme values were classified as noncomittal and removed. Analysis of variance calls for a split plot design; blocks (sessions) and treatments in main plot, and panelists in subplot. In general, the subplot can be ignored. These methods are suggested for evaluating panelists' training needs; and for eliminating data that distorts the statistical analysis.