Before revision:  A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections ( i.e., Multiblock-VIOP or MB-VIOP) is explained in this paper.
After revision: For multivariate data analysis involving only two input matrices, the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices by calculating latent variables; however, a method for improving the interpretation of these latent variables by assessing the importance of the input variables was not available up to now. A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections ( i.e., Multiblock-VIOP or MB-VIOP) is explained in this paper.