This paper discusses the advantages of using so-called component-based methods in sensory science. For instance, principal component analysis (PCA) and partial least squares (PLS) regression are used widely in the field; we will here discuss these and other methods for handling one block of data, as well as several blocks of data. Component-based methods all share a common feature: they define linear combinations of the variables to achieve data compression, interpretation, and prediction. The common properties of the component-based methods are listed and their advantages illustrated by examples. The paper equips practitioners with a list of solid and concrete arguments for using this methodology.<\/p>\n\n\n\n
N\u00e6s, T., Varela, P.. Castura, J.C., Bro, R., & Tomic, O. (2023). Why use component-based methods in sensory science? Food Quality and Preference<\/em>, 112, 105028. https:\/\/doi.org\/10.1016\/j.foodqual.2023.105028<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" This paper discusses the advantages of using so-called component-based methods in sensory science. For instance, principal component analysis (PCA) and partial least squares (PLS) regression are used widely in the field; we will here discuss these and other methods for handling one block of data, as well as several blocks of data. Component-based methods all share a common feature: they<\/p>\n