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A new aberration-corrected scanning transmission electron microscope equipped with an array of Si-drift energy-dispersive X-ray spectrometers has been utilized to acquire spectral image data at atomic resolution. The resulting noisy data were subjected to multivariate statistical analysis to noise filter, remove an unwanted and partially overlapping non-sample-specific X-ray signal, and extract the relevant correlated X-ray signals (e.g., channels with L and K lines). As an example, the Y2Ti2O7 pyrochlore-structured oxide (assumed here to be ideal) was interrogated at the [011] projection. In addition to pure columns of Y and Ti, at this projection, there are also mixed 50-50 at. % Y-Ti columns. An attempt at atomic-resolution quantification is presented. The method proposed here is to subtract the non-column-specific signal from the elemental components and then quantify the data based upon an internally derived k-factor. However, a theoretical basis to predict this non-column-specific signal is needed to make this generally applicable. [ABSTRACT FROM AUTHOR]

Abstract: This paper assesses the potential of multivariate statistical analysis (MSA) applied to electron backscattered diffraction (EBSD) data. Instead of directly indexing EBSD patterns on an individual basis, this multivariate approach reduces a large (thousands) set of individual EBSD patterns into a core set of statistically derived component EBSD patterns which can be subsequently indexed. The following hypotheses are considered in this paper: (1) experimental EBSD patterns from a microstructure can be analytically treated as linear combinations of spatially simple components, (2) MSA has an angular resolution on par with standard EBSD, (3) MSA can discriminate between similar and dissimilar phases, and (4) the MSA approach can improve the effective spatial resolution of automated EBSD. [Copyright &y& Elsevier]

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is capable of generating huge volumes of data. TOF-SIMS spectrum-images, comprising complete mass spectra at each point in a spatial array, are easily acquired with modern instrumentation. With the addition of depth profiling, spectra can be collected from up to three spatial dimensions leading to data sets that are seemingly unlimited in size. Multivariate statistical techniques such as principal component analysis, multivariate curve resolution and other factor analysis methods are being used to meet the challenge of turning that mountain of data into analytically useful knowledge. These methods work by extracting the essential chemical information embedded in the high dimensional data into a limited number of factors that describe the spectrally active pure components present in the sample. A review of the recent literature shows that the mass spectral data are often scaled prior to multivariate analysis. Common preprocessing steps include normalization of the pixel intensities, and auto- or variance-scaling of the mass spectra. In this paper, we will demonstrate that these pretreatments can lead to less than satisfactory results and, in fact, can be counterproductive. By taking the Poisson nature of the data into consideration, however, a scaling method can be devised that is optimal in a maximum likelihood sense. Using a simple and intuitive example, we will demonstrate the superiority of the optimal scaling approach for estimating the number of pure components, for segregating the chemical information into as few components as possible, and for discriminating small features from noise. [Copyright &y& Elsevier]

Tomographic spectral imaging is a powerful technique for the three-dimensional (3-D) analysis of materials. Using a focused ion-beam/scanning electron microscope equipped with an x-ray spectrometer, 3-D microanalysis can be performed on individual regions of a sample, such as defects, with microanalytical spatial resolution of better than 300 nm typically. The focused ion-beam can serially section at comparable thicknesses to sequentially reveal new analytical surfaces within the specimen. After each slice a full 2-spatial dimension spectral image, consisting of a complete spectrum at each point in the 2-D array, is acquired with the scanning electron microscope/energy-dispersive x-ray spectrometer on the same platform. The process is repeated multiple times to result in a 3-D or tomographic spectral image. The challenge is to effectively and efficiently analyze the tomographic spectral image to extract chemical phase distributions. Therefore, automated multivariate statistical analysis methods were developed and applied to these images. Sandia's Automated eXpert Spectral Image Analysis multivariate statistical analysis software requires no a priori information to find even very weak signals hidden in the data sets. The result of the analysis is a small number of chemical components which describe the 3-D phase distribution in the volume of material sampled. These 3-D phases can then be effectively visualized with off-the-shelf 3-D rendering software. [ABSTRACT FROM AUTHOR]

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010. [ABSTRACT FROM PUBLISHER]

Abstract: Hyperspectral imaging combined with multivariate statistics is an approach to microanalysis that makes the maximum use of the large amount of data potentially collected in forensics analysis. This study examines the efficacy of using hyperspectral imaging-enabled microscopies to identify chemical signatures in simulated bioagent materials. This approach allowed for the ready discrimination between all samples in the test. In particular, the hyperspectral imaging approach allowed for the identification of particles with trace elements that would have been missed with a more traditional approach to forensic microanalysis. The importance of combining signals from multiple length scales and analytical sensitivities is discussed. [Copyright &y& Elsevier]