Data Relationships and Multivariate Applications

  • Harry T. Lawless
  • Hildegarde Heymann
Part of the Food Science Text Series book series (FSTS)


Multivariate statistics have found great application in all areas of quantitative sensory science. In this chapter we will briefly describe the two major work horses in the field: principal component analysis (PCA) and canonical variate analysis (CVA). PCA should be used with mean data and CVA with raw data, namely data including replicate observations. We also discuss generalized Procrustes analysis (GPA) which is used with free-choice profiling data as well as in any situation where one may want to compare the data spaces associated with multiple data measurements on the same products. Lastly we discuss (as a preliminary to further in-depth discussion in  Chapter 19) internal and external preference mapping. We conclude by stressing that multivariate analyses should always be performed in conjunction with univariate analyses.


Principal Component Analysis Product Space Sensory Specialist Canonical Variate Analysis Internal Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Harry T. Lawless
    • 1
  • Hildegarde Heymann
    • 2
  1. 1.Department of Food ScienceCornell UniversityIthacaUSA
  2. 2.Department of Viticulture and EnologyUniversity of California – DavisDavisUSA

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