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Windig W.,Eigenvector Research Inc. | Keenan M.R.,8346 Roney Rd.
Chemometrics and Intelligent Laboratory Systems

Independent component analysis (ICA) is an increasingly popular method to resolve complex data sets, such as chemical image data, into images and their associated spectra. Unfortunately, the pre-requisite of statistical independence severely limits the application of ICA. In this paper we will show that, for a certain class of data, increasing the sparsity of a data set increases the independence of components, which enables the successful application of ICA. The sparsity can be increased by simply adding zeros to the data set or by applying a Haar-wavelet transform. ICA will be explained using simple numerical examples and actual data sets obtained by energy dispersive X-ray spectrometry (EDS) of a Cu-Ni diffusion couple and a braze interface. © 2015 Elsevier B.V. Source

Marini F.,University of Rome La Sapienza | Gallagher N.B.,Eigenvector Research Inc.
Chemometrics and Intelligent Laboratory Systems

The Sixth International Chemometrics Research Meeting (ICRM 2014) took place on 14-18 September 2014 at the Golden Tulip Val Monte in Berg en Dal near the City of Nijmegen, The Netherlands. Talks included longer keynote talks with a discussant and several shorter interesting talks at the forefront of chemometrics. ICRM will return in 2017. © 2015 Elsevier B.V. Source

Gujral P.,Ecole Polytechnique Federale de Lausanne | Amrhein M.,Ecole Polytechnique Federale de Lausanne | Wise B.M.,Eigenvector Research Inc. | Bonvin D.,Ecole Polytechnique Federale de Lausanne
Journal of Chemometrics

Latent-variable calibrations using principal component regression and partial least-squares regression are often compromised by drift such as systematic disturbances and offsets. This paper presents a two-step framework that facilitates the evaluation and comparison of explicit drift-correction methods. In the first step, the drift subspace is estimated using different types of correction data in a master/slave setting. The correction data are measured for the slave with drift and computed for the master with no drift. In the second step, the original calibration data are corrected for the estimated drift subspace using shrinkage or orthogonal projection. The two cases of no correction and drift correction by orthogonal projection can be seen as special cases of shrinkage. The two-step framework is illustrated with four different experimental data sets. The first three examples study drift correction on one instrument (temperature effects, spectral differences between samples obtained from different plants, instrumental drift), while the fourth example studies calibration transfer between two instruments. Copyright © 2010 John Wiley & Sons, Ltd. Source

Arakaki L.S.L.,University of Washington | Schenkman K.A.,University of Washington | Ciesielski W.A.,University of Washington | Shaver J.M.,Eigenvector Research Inc.
Analytica Chimica Acta

We have developed a method to make real-time, continuous, noninvasive measurements of muscle oxygenation (Mox) from the surface of the skin. A key development was measurement in both the visible and near infrared (NIR) regions. Measurement of both oxygenated and deoxygenated myoglobin and hemoglobin resulted in a more accurate measurement of Mox than could be achieved with measurement of only the deoxygenated components, as in traditional near-infrared spectroscopy (NIRS). Using the second derivative with respect to wavelength reduced the effects of scattering on the spectra and also made oxygenated and deoxygenated forms more distinguishable from each other. Selecting spectral bands where oxygenated and deoxygenated forms absorb filtered out noise and spectral features unrelated to Mox. NIR and visible bands were scaled relative to each other in order to correct for errors introduced by normalization. Multivariate Curve Resolution (MCR) was used to estimate Mox from spectra within each data set collected from healthy subjects. A Locally Weighted Regression (LWR) model was built from calibration set spectra and associated Mox values from 20 subjects using 2562 spectra. LWR and Partial Least Squares (PLS) allow accurate measurement of Mox despite variations in skin pigment or fat layer thickness in different subjects. The method estimated Mox in five healthy subjects with an RMSE of 5.4%. © 2013. Source

Keenan M.R.,Independent Scientist | Windig W.,Eigenvector Research Inc. | Arlinghaus H.,ION TOF GmbH
Journal of Vacuum Science and Technology A: Vacuum, Surfaces and Films

Multivariate statistical analysis, in general, and multivariate curve resolution (MCR), in particular, have found an important role in extracting chemical information from the very large datasets typical of time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging. MCR seeks to uncover and describe the underlying chemistry that gives rise to the spectral image. It is often implemented with alternating least squares procedures that include physically inspired constraints, like non-negativity of concentrations and mass spectra, to guide the solution process toward those that are physically plausible. Besides appropriate constraints, the ToF-SIMS community has long recognized the importance of proper preprocessing of the mass spectra to achieving good results. This has led to an analysis paradigm of preprocess-analyze-postprocess. In this article, a number of limitations of this approach will be identified, and the authors propose a framework for MCR calculations that integrates the three steps into a unified algorithm that is implemented with alternating weighted least squares and is numerically efficient. Several advantages of the proposed framework are illustrated with simple examples, some of which are not easily accommodated by the existing approach. As a byproduct, a couple of new analyses are suggested. These include a new variant of the angle constraint that expresses a preference for relatively orthogonal image components, an alternative maximum autocorrelation factors-like procedure for empirically estimating the error covariance matrix, and an approach that may be suitable for simultaneously analyzing several spectral images that share a common chemistry. © 2015 American Vacuum Society. Source

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