Optimal scaling of TOF-SIMS spectrum-images prior to multivariate statistical analysis. Keenan, M. R & Kotula, P. G Appl Surf Sci, 231:240–244, Elsevier, 2004. ![Optimal scaling of TOF-SIMS spectrum-images prior to multivariate statistical analysis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@Article{keenan04optimal,
author = {Keenan, Michael R and Kotula, Paul G},
title = {Optimal scaling of TOF-SIMS spectrum-images prior to multivariate statistical analysis},
journal = {Appl Surf Sci},
year = {2004},
volume = {231},
pages = {240--244},
abstract = {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.},
doi = {Optimal scaling of TOF-SIMS spectrum-images prior to multivariate statistical analysis},
file = {KeenanEtAl_ScalingSIMSImagesMVA_AppSuSc_2004.pdf:2004/KeenanEtAl_ScalingSIMSImagesMVA_AppSuSc_2004.pdf:PDF},
optmonth = jun,
owner = {purva},
publisher = {Elsevier},
timestamp = {2015.11.28},
url = {http://www.sciencedirect.com/science/article/pii/S0169433204002612},
}
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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. 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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. 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