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A novel method is described that combines high-resolution scanning microdiffraction techniques, Rietveld quantitative phase analysis and a statistical method known as canonical correlation analysis (CCA). The method has been applied to a sample taken from a bone-tissue-engineered bioceramic porous scaffold implanted in a mouse for six months. The CCA technique allows the detection of those pixels throughout the investigated sample that best correlate with signal models. Besides the standard usage of this approach, which requires theoretical profiles as signal models, a novel application is presented here, which consists of picking the model spectra out of the experimental data set. Patterns representative of a reasonable range of phase compositions were selected among the huge number of two-dimensional patterns (folded in one-dimensional profiles) to extract quantitative phase fractions. At this stage, the CCA approach was also used to overcome the low Poisson statistic of signal models, so making Rietveld quantitative analysis more reliable. These patterns have been used as profile models for CCA. The final classification map, obtained by assigning the considered pixel to the model spectrum with the highest canonical coefficient, provides the spatial variation of phase concentration.
