Selecting the number of components for PLS is a bit trickier than for PCA. For instance, one reason is that quantities such as "explained variance" are more complex since you have both the $\mathbf X$ and $\mathbf Y$ parts of the model contributing to the variation explained.
Thus for PLS, cross-validation tends to be the default method for selecting the number of components. In SAS this can be implemented using the CV feature of its PROC PLS function.
Here is a link to the SAS website where they refer to it in their function.
SAS also provides some documentation on exactly what they are doing in the cross-validation procedure they have implemented here if you would like to know the details.
In this document, you have an example of how to do all of this in practice.