To increase efficiency of bulk heterojunctions for photovoltaic devices, the functional morphology of active layers has to be understood, requiring visualization and discrimination of materials with very similar characteristics. Here we combine high-resolution spectroscopic imaging using an analytical transmission electron microscope with nonlinear multivariate statistical analysis for classification of multispectral image data. We obtain a visual representation showing homogeneous phases of donor and acceptor, connected by a third composite phase, depending in its extent on the way the heterojunction is fabricated. For the first time we can correlate variations in nanoscale morphology determined by material contrast with measured solar cell efficiency. In particular we visualize a homogeneously blended phase, previously discussed to diminish charge separation in solar cell devices.

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Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps, visualizing spectral and spatial structure. The most commonly used techniques are principal component analysis (PCA) and independent component analysis (ICA). Both methods operate in an unsupervised manner. However, their decomposition estimates usually feature negative counts and are not amenable to direct physical interpretation. We propose probabilistic latent semantic analysis (pLSA) for non-negative decomposition and the elucidation of interpretable component spectra and abundance maps. We compare this algorithm to PCA, ICA, and non-negative PARAFAC (parallel factors analysis) and show on simulated and real-world data that pLSA and non-negative PARAFAC are superior to PCA or ICA in terms of complementarity of the resulting components and reconstruction accuracy. We further combine pLSA decomposition with a statistical complexity estimation scheme based on the Akaike information criterion (AIC) to automatically estimate the number of components present in a tissue sample data set and show that this results in sensible complexity estimates.