Quantitative descriptive analysis and principal component analysis for sensory characterization of ultrapasteurized milk

J Dairy Sci. 2001 Jan;84(1):12-20. doi: 10.3168/jds.S0022-0302(01)74446-3.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Consumer Behavior
  • Food Handling*
  • Food Preservation
  • Food Technology
  • Hot Temperature*
  • Lactose / analysis
  • Least-Squares Analysis
  • Lipids / analysis*
  • Milk / chemistry*
  • Models, Theoretical
  • Quality Control
  • Regression Analysis
  • Statistics as Topic
  • Taste*


  • Lipids
  • Lactose