title: Quantitative descriptive analysis and principal component analysis for sensory characterization of ultrapasteurized milk.
abstract: Quantitative descriptive analysis was used to describe the key attributes of nine ultrapasteurized (UP) milk products of various fat levels, including two lactose-reduced products, from two dairy plants. Principal components analysis identified four significant principal components that accounted for 87.6% of the variance in the sensory attribute data. Principal component scores indicated that the location of each UP milk along each of four scales primarily corresponded to cooked, drying/lingering, sweet, and bitter attributes. Overall product quality was modeled as a function of the principal components using multiple least squares regression (R2 = 0.810). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring UP fluid milk product attributes that are important to consumers.
authors.affiliation: Dept. of Food Science, Cornell University, Ithaca, NY 14853, USA. kwc3@cornell.edu
authors.forename: K W
authors.initials: KW
authors.lastname: Chapman
authors.forename: H T
authors.initials: HT
authors.lastname: Lawless
authors.forename: K J
authors.initials: KJ
authors.lastname: Boor
mesh_terms: D000818:Animals; D003258:Consumer Behavior; D005511:Food Handling; D005519:Food Preservation; D005524:Food Technology; D006358:Hot Temperature; D007785:Lactose / Q000032:analysis; D016018:Least-Squares Analysis; D008055:Lipids / Q000032:analysis*; D008892:Milk / Q000737:chemistry*; D008962:Models, Theoretical; D011786:Quality Control; D012044:Regression Analysis; D013223:Statistics as Topic; D013649:Taste
chemical_list: D008055:Lipids; D007785:Lactose