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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 | Year: 2015

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.

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 | Year: 2013

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.

Gujral P.,Ecole Polytechnique Federale de Lausanne | Amrhein M.,Ecole Polytechnique Federale de Lausanne | Ergon R.,Telemark University College | Wise B.M.,Eigenvector Research Inc. | Bonvin D.,Ecole Polytechnique Federale de Lausanne
Journal of Chemometrics | Year: 2011

In principal component regression (PCR) and partial least-squares regression (PLSR), the use of unlabeled data, in addition to labeled data, helps stabilize the latent subspaces in the calibration step, typically leading to a lower prediction error. For using unlabeled data in PLSR, a non-sequential approach based on optimal filtering (OF) has been proposed in the literature. In this work, a sequential version of the OF-based PLSR and a PCA-based PLSR (PLSR applied to PCA-preprocessed data) are proposed. It is shown analytically that the sequential version of the OF-based PLSR is equivalent to that of PCA-based PLSR, which leads to a new interpretation of OF. Simulated and experimental data sets are used to point out the usefulness and pitfalls of using unlabeled data. Unlabeled data can replace labeled data to some extent, thereby leading to an economic benefit. However, in the presence of drift, the use of unlabeled data can result in an increase in prediction error compared to that obtained with a model based on labeled data alone. © 2011 John Wiley & Sons, Ltd.

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 | Year: 2010

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.

Zorzetti B.M.,University of Alberta | Shaver J.M.,Eigenvector Research Inc. | Harynuk J.J.,University of Alberta
Analytica Chimica Acta | Year: 2011

The ability to predict the amount of time that a light petroleum mixture has been weathered could have many applications, such as aiding forensic investigators in determining the cause and intent of a fire. In our study, an evaporation chamber that permits control of airflow and temperature was constructed and used to weather a model nine-component hydrocarbon mixture. The composition of the mixture was monitored over time by gas chromatography and a variety of chemometric models were explored, including partial least squares (PLS), nonlinear PLS (PolyPLS) and locally weighted regression (LWR or loess). A hierarchical application of multivariate techniques was able to predict the time for which a sample had been exposed to evaporative weathering. A classification model based on partial least squares discriminant analysis (PLS-DA) could predict whether a sample was relatively fresh (< 12. h exposure time) or highly weathered (>20. h exposure time). Subsequent regression models for these individual classes were evaluated for accuracy using the root mean square error of prediction (RMSEP). Prior to regression model calculation, y-gradient generalized least squares weighting (GLSW) was used to preprocess the data by removing variance from the X-block, which was orthogonal to the Y-block. LWR was found to be the most successful regression method, whereby fresh samples could be predicted to within 40. min of exposure and highly weathered samples predicted to within 5.6. h. These results suggest that our hierarchical chemometric approach may also allow us to estimate the age of more complicated light petroleum mixtures, such as gasoline. © 2011 Elsevier B.V.

Windig W.,Eigenvector Research Inc. | Keenan M.R.,8346 Roney Rd.
Chemometrics and Intelligent Laboratory Systems | Year: 2015

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.

Windig W.,Eigenvector Research Inc. | Shaver J.M.,Eigenvector Research Inc. | Keenan M.R.,8346 Roney Rd. | Wise B.M.,Eigenvector Research Inc.
Chemometrics and Intelligent Laboratory Systems | Year: 2012

A problem with self-modeling mixture analysis solutions is the range of solutions that is generally possible. The range of solutions goes from those with high contrast spectra and low contrast contributions/images to solutions with high contrast contributions/images and low contrast spectra. The term contrast is a measure for dissimilarity, which results in simpler spectra or images. Because of their simplicity, the extremes of this solution range often have clear advantages for the interpretation of the data. Previously, an angle modification in alternating least squares self-modeling mixture analysis has been shown as a method of identifying these extremes. The utility of this method of this utility will be demonstrated in this paper with : autofluorescence spectroscopy microscopy images of lung cells; secondary ion mass spectrometry (SIMS) analysis of a sample obtained by laser activation modification of semiconductor surfaces (LAMSS) and energy dispersive X-ray fluorescence (EDXRF) of a granite sample. © 2012 Elsevier B.V.

Windig W.,Eigenvector Research Inc. | Keenan M.R.,8346 Roney Rd
Applied Spectroscopy | Year: 2011

When resolving mixture data sets using self-modeling mixture analysis techniques, there are generally a range of possible solutions. There are cases, however, in which a unique solution is possible. For example, variables may be present (e.g., m/z values in mass spectrometry) that are characteristic for each of the components (pure variables), in which case the pure variables are proportional to the actual concentrations of the components. Similarly, the presence of pure spectra in a data set leads to a unique solution. This paper will show that these solutions can be obtained by applying angle constraints in combination with non-negativity to the solution vectors (resolved spectra and resolved concentrations). As will be shown, the technique goes beyond resolving data sets with pure variables and pure spectra by enabling the analyst to selectively enhance contrast in either the spectral or concentration domain. Examples will be given of Fourier transform infrared (FT-IR) microscopy of a polymer laminate, secondary ion mass spectrometry (SIMS) images of a two-component mixture, and energy dispersive spectrometry (EDS) of alloys. © 2011 Society for Applied Spectroscopy.

Marini F.,University of Rome La Sapienza | Gallagher N.B.,Eigenvector Research Inc.
Chemometrics and Intelligent Laboratory Systems | Year: 2015

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.

Wise B.M.,Eigenvector Research Inc. | Roginski R.T.,Eigenvector Research Inc.
IFAC-PapersOnLine | Year: 2015

Multivariate calibration, classification and fault detection models are ubiquitous in QbD (Quality by Design) and PAC and PAT (Process Analytical Chemistry and Technology, respectively) applications. They occur in the both the development of processes and their permissible operating limits, (i.e. models for relating the process design space to product quality), and in manufacturing (i.e. models used in monitoring and control). Model maintenance is the ongoing servicing of these multivariate models in order to preserve their predictive abilities. It is required because of changes to either the sample matrices or the instrument or response. The goal of model maintenance is to sustain or improve models over time and changing conditions with the least amount of cost and effort. This paper presents a roadmap for determining when model maintenance is required, the probable source of the response variations, and the appropriate approaches for achieving it. Methods for evaluating model robustness in order to identify models with lower ongoing maintenance costs are also discussed. . © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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