Quantitative descriptive analysis and principal component analysis for sensory characterization of ultrapasteurized milk.
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.
Chapman
K W
KW
Dept. of Food Science, Cornell University, Ithaca, NY 14853, USA. kwc3@cornell.edu
Lawless
H T
HT
Boor
K J
KJ
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
D008055:Lipids; D007785:Lactose