Another approach to differential PDF analysis instead subtracts the normalised scattering data of the physical mixture from that of the corresponding composite (Fig. ). This difference S(Q) is then Fourier transformed to obtain the dPDF. These dPDFs showed similar features for all the composites at long range, in addition to two new correlations at short range for [(ZIF-8)0.3(IG)0.7] (Fig. ). The presence of two peaks located at 2.50 and 3.38 Å would be consistent with Na•••N, and Zn•••P correlations in zinc phosphate structures (Fig. ). However, these peaks only appear in one of the samples, which suggests this approach is unreliable for these systems. Multivariate analysis is a tool which can reduce complex data into its components It has previously been employed to observe the correlations at the interface of composite materials. It has however not been widely extended to the characterisation of complex hybrid materials. Recently, one of the principal methods of multivariate analysis, known as principal component analysis (PCA), has been employed to study disorder in an amorphous MOF, Fe-BTC. Components extracted from PDFs using PCA in this manner are not necessarily chemically intuitive, given the purely mathematical approach used. However, they may correspond to atom-atom correlations, distortions, noise within the data and compensation resulted from the orthogonalisation. To interpret PCA of PDF, the first principal component (PC), i.e. that with the largest eigenvalue, accounts for the largest proportion of the total variance in the data and should approximate to the PDF from the largest contributor to the composite PDF. In this case, this is the IG PDF. The second PC accounts for the next largest proportion of the variance (mainly from the second largest contributor, the ZIF-8 PDF), and so on. As outlined above, PCA does not constrain the form of the components by component weightings, and the raw PC output can include components and weightings that are linear combinations of the PDFs and abundances of the distinct phases within the sample. The big advantage is that it treats the dataset as a whole. In this case, PCA was applied to the set of four D(r) functions of all the materials labelled by weight % of ZIF-8 in the mixture: 0% (pure IG), 10%, 20% and 30% composites and 100% (pristine ZIF-8). From these, four principal components (PCs) were extracted and of these only three were deemed statistically significant with 77.25%, 22.88% and 0.44% of the variance, respectively (Fig. ). However, PC4 (0.12%) did not contain any correlations above the background noise level, and was therefore without any potential physical significance.