Multicollinearity question

[mpalanski]

I have a question concerning how to handle multicollinearity in PLS:

One of the ways to help handle multicollinearity in multiple regression is to regress one independent variable onto another independent variable to try to parcel out unique effects.

Can I do something similar in PLS by drawing a line from one independent variable to another? I notice that when I do this the r-squared values in the dependent variables do not change, but several more beta weights leading to the dependent variables now become significant.

If I were running multiple regression, I would see this as a sign that I have at least partially accounted for multicollinearity. Is it the same principle in PLS?

PS I already did choose to standardize the variables in the PLS Algorithm dialogue box.