Alternatives to data averaging of consumer preference data

The relationship between a consumer preference data set and a corresponding sensory profile on eight cooked wheat noodles with different formulas was examined using several multivariate techniques. Individual consumer hedonic responses (100 noodle/pasta consumers) and eight appearance and texture sensory attributes were collected. The consumer preference data were treated in two different ways: mean values averaged across all consumers or principal components extracted from individual responses. The mean preference scores were submitted to both principal component stepwise regression and partial least squares regression (PLS1), whereas the summarized major preference components were subjected to canonical correlation analysis, as well as partial least squares regression (PLS2). The results suggested that in case of complex consumer data, using mean value can only capture the most manifest trends in consumer preference patterns, while studying individual responses and by further categorizing major preference patterns provide an opportunity to discover the hidden information that are masked by data averaging.