Use of univariate and multivariate statistical procedures on quantitative descriptive sensory data - de Leon, Alma Alviar (Bell & Howell Information and Learning Company, 1993)
Sensory data were collected from ready-to-eat (RTE) cereal breakfast foods and asparagus using quantitative descriptive analysis. The collated data were subjected to univariate and multivariate statistical procedures. Trained panelists using wide a array of RTE breakfast cereals generated 16 sensory descriptors covering appearance, texture, flavor and after-taste. ANOVA in conjunction with MANOVA of the RTE breakfast cereal data showed that each of seven panelist can proficiently discriminate between RTE cereal breakfast foods. Principal component analysis, based on panelists scores, effectively segregated 3 of the panelists and considered them as outliers. When scores of the outliers were deleted and the remaining data were pooled and subjected to ANOVA, 13 of the 16 descriptors were classified as discriminators. Factor analysis effectively reduced the 13 discriminators into 3 orthogonal factors with as little loss of information as possible. Finally based on the sensory characteristics of RTE cereal breakfast foods, discriminant analysis was able to group them into their respective sugar categories. Quantitative descriptive analysis (QDA) done on green and white (blanched) asparagus (Asparagus officinalis L.) by trained panelists generated 11 flavor descriptors which were categorized as aromatic, up-front taste and after-taste. Through factor analysis the 11 flavor descriptors were further reduced to five orthogonal factors namely Factor 1, component related to green and leafy vegetables, Factor 2, component related to yellow vegetables, Factor 3, the after-taste component, Factor 4, the sulfury component and Factor 5, the sweet pea component. Slopes of individual panelists with time was used to determine who among the panelists were entirely different in their flavor perception.--Abstract.