Why use component-based methods in sensory science?

•Components methods are useful for exploration of data.•Component methods are useful for interpretation of large data sets.•Components methods can be generalized to and used for many different purposes.•Component method can be used to confirm hypotheses.•There is a strong link between many different component methods. 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.