If you decide to perform a PCA model you'll find that most standard procedures such as the MATLAB built-in SVD or the PCA m-file of the PLS Toolbox do not handle missing data. There are at least two ways to circumvent this. All standard chemometric software such as Unscrambler, SIMCA, etc. have sub-optimal algorithms for handling missing data but the N-way Toolbox does provide means for estimating an optimal two-way PCA model with missing entries. Simply rearrange, the data to a two-way array and fit a PARAFAC model to these data using orthogonality on the first and second mode. This will produce a PCA model. PARAFAC as such, is not made to fit two-way data. For example, you can not have orthogonality in all modes in PARAFAC and we need that here. Instead of specifying the constraints as [1 1] becaus of the two modes we specify the constraints as [1 1 0] and that should do the trick, even though we only input a two-way array.

Another possibility is to use the m-file MDPCA of the PLS_Toolbox which directly handles missing data.