Principal component analysis (PCA) and multilinear regression analysis (MLR) of PDF data have recently been demonstrated to be effective approaches to analyse components of MOF-composite series as well as describing atom-atom correlations at their interface. PCA is a multivariate analysis able to reduce complex data into its components. Extracted principal components (PCs) may correspond to atomic correlations, atomic distortions, mathematical compensations or noise within the data and, therefore, they are not always chemically intuitive given their purely mathematical derivation. It does however provide a mechanism for determining the minimum number of components required to describe the series. PCA can be employed to study highly disordered MOF materials, including glasses and amorphous MOFs as well as composites. Here, three PCs (Figure .a) were extracted from PCA of all the D(r)s of the materials, i.e. x = 0 wt% (pristine glass), 15, 25, 50 and 75 wt% composites [(Ti-MOF)x(IG)1-x], and 100 wt% (pristine Ti-MOF).