Paper doi abstract bibtex

Multivariate tools based on principal component analysis (PCA) have been developed to supplement the usual serial interpretive approach to TOF-SIMS data. The tools are designed to streamline application of PCA so it can be used routinely in a high throughput industrial surface analysis laboratory. Data pretreatment features such as weighting functions, and post-treatment features such as confidence ellipses on scores cluster plots, have been implemented. PCA allows rapid assessment of differences between spectra and can assist in decision-making for common univariate interpretive tasks such as peak integration. PCA is particularly powerful when applied to so-called ''raw'' data sets, in which a complete mass spectrum is collected for every pixel in the analysis area. A graphical user interface has been developed that uses PCA to simplify and automate many interpretive functions, such as finding features within SIMS images, selecting region-of-interest spectra from image data, and selecting and displaying the most significant ions in a raw data set. Image interpretation can sometimes be improved by using PCA to reduce topographic effects. In some cases spectral comparisons can be improved through extraction of sub-spectra from raw files, followed by PCA of the sub-spectra.

@Article{pachuta04enhancing, author = {Pachuta, Steven J}, title = {Enhancing and automating TOF-SIMS data interpretation using principal component analysis}, journal = {Appl Surf Sci}, year = {2004}, volume = {231}, pages = {217--223}, abstract = {Multivariate tools based on principal component analysis (PCA) have been developed to supplement the usual serial interpretive approach to TOF-SIMS data. The tools are designed to streamline application of PCA so it can be used routinely in a high throughput industrial surface analysis laboratory. Data pretreatment features such as weighting functions, and post-treatment features such as confidence ellipses on scores cluster plots, have been implemented. PCA allows rapid assessment of differences between spectra and can assist in decision-making for common univariate interpretive tasks such as peak integration. PCA is particularly powerful when applied to so-called ''raw'' data sets, in which a complete mass spectrum is collected for every pixel in the analysis area. A graphical user interface has been developed that uses PCA to simplify and automate many interpretive functions, such as finding features within SIMS images, selecting region-of-interest spectra from image data, and selecting and displaying the most significant ions in a raw data set. Image interpretation can sometimes be improved by using PCA to reduce topographic effects. In some cases spectral comparisons can be improved through extraction of sub-spectra from raw files, followed by PCA of the sub-spectra.}, doi = {10.1016/j.apsusc.2004.03.204}, file = {Pachuta_AutomatingSIMSDataInterpretationPCA_AppSuSc_2004.pdf:Pachuta_2004/AutomatingSIMSDataInterpretationPCA_AppSuSc_2004.pdf:PDF}, optmonth = jun, owner = {purva}, publisher = {Elsevier}, timestamp = {2015.11.28}, url = {http://www.sciencedirect.com/science/article/pii/S0169433204002521}, }

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