Publications

Abstract not provided.

While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.

A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.

Molecular analysis of cancer, at the genomic level, could lead to individualized patient diagnostics and treatments. The developments to follow will signal a significant paradigm shift in the clinical management of human cancer. Despite our initial hopes, however, it seems that simple analysis of microarray data cannot elucidate clinically significant gene functions and mechanisms. Extracting biological information from microarray data requires a complicated path involving multidisciplinary teams of biomedical researchers, computer scientists, mathematicians, statisticians, and computational linguists. The integration of the diverse outputs of each team is the limiting factor in the progress to discover candidate genes and pathways associated with the molecular biology of cancer. Specifically, one must deal with sets of significant genes identified by each method and extract whatever useful information may be found by comparing these different gene lists. Here we present our experience with such comparisons, and share methods developed in the analysis of an infant leukemia cohort studied on Affymetrix HG-U95A arrays. In particular, spatial gene clustering, hyper-dimensional projections, and computational linguistics were used to compare different gene lists. In spatial gene clustering, different gene lists are grouped together and visualized on a three-dimensional expression map, where genes with similar expressions are co-located. In another approach, projections from gene expression space onto a sphere clarify how groups of genes can jointly have more predictive power than groups of individually selected genes. Finally, online literature is automatically rearranged to present information about genes common to multiple groups, or to contrast the differences between the lists. The combination of these methods has improved our understanding of infant leukemia. While the complicated reality of the biology dashed our initial, optimistic hopes for simple answers from microarrays, we have made progress by combining very different analytic approaches.

Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for the quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorophores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluorescent labels over the entire image. Since the concentration maps of the fluorescent labels are relatively unaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.

High throughput instruments and analysis techniques are required in order to make good use of the genomic sequences that have recently become available for many species, including humans. These instruments and methods must work with tens of thousands of genes simultaneously, and must be able to identify the small subsets of those genes that are implicated in the observed phenotypes, or, for instance, in responses to therapies. Microarrays represent one such high throughput method, which continue to find increasingly broad application. This project has improved microarray technology in several important areas. First, we developed the hyperspectral scanner, which has discovered and diagnosed numerous flaws in techniques broadly employed by microarray researchers. Second, we used a series of statistically designed experiments to identify and correct errors in our microarray data to dramatically improve the accuracy, precision, and repeatability of the microarray gene expression data. Third, our research developed new informatics techniques to identify genes with significantly different expression levels. Finally, natural language processing techniques were applied to improve our ability to make use of online literature annotating the important genes. In combination, this research has improved the reliability and precision of laboratory methods and instruments, while also enabling substantially faster analysis and discovery.

A manuscript describing this work summarized below has been submitted to Applied Spectroscopy. Comparisons of prediction models from the new ACLS and PLS multivariate spectral analysis methods were conducted using simulated data with deviations from the idealized model. Simulated uncorrelated concentration errors, and uncorrelated and correlated spectral noise were included to evaluate the methods on situations representative of experimental data. The simulations were based on pure spectral components derived from real near-infrared spectra of multicomponent dilute aqueous solutions containing glucose, urea, ethanol, and NaCl in the concentration range from 0-500 mg/dL. The statistical significance of differences was evaluated using the Wilcoxon signed rank test. The prediction abilities with nonlinearities present were similar for both calibration methods although concentration noise, number of samples, and spectral noise distribution sometimes affected one method more than the other. In the case of ideal errors and in the presence of nonlinear spectral responses, the differences between the standard error of predictions of the two methods were sometimes statistically significant, but the differences were always small in magnitude. Importantly, SRACLS was found to be competitive with PLS when component concentrations were only known for a single component. Thus, SRACLS has a distinct advantage over standard CLS methods that require that all spectral components be included in the model. In contrast to simulations with ideal error, SRACLS often generated models with superior prediction performance relative to PLS when the simulations were more realistic and included either non-uniform errors and/or correlated errors. Since the generalized ACLS algorithm is compatible with the PACLS method that allows rapid updating of models during prediction, the powerful combination of PACLS with ACLS is very promising for rapidly maintaining and transferring models for system drift, spectrometer differences, and unmodeled components without the need for recalibration. The comparisons under different noise assumptions in the simulations obtained during this investigation emphasize the need to use realistic simulations when making comparisons between various multivariate calibration methods. Clearly, the conclusions of the relative performance of various methods were found to be dependent on how realistic the spectral errors were in the simulated data. Results demonstrating the simplicity and power of ACLS relative to PLS are presented in the following section.

We describe the design, construction, and operation of a hyperspectral microarray scanner for functional genomic research. The hyperspectral instrument operates with spatial resolutions ranging from 3 to 30 {micro}m and records the emission spectrum between 490 and 900 nm with a spectral resolution of 3 nm for each pixel of the microarray. This spectral information, when coupled with multivariate data analysis techniques, allows for identification and elimination of unwanted artifacts and greatly improves the accuracy of microarray experiments. Microarray results presented in this study clearly demonstrate the separation of fluorescent label emission from the spectrally overlapping emission due to the underlying glass substrate. We also demonstrate separation of the emission due to green fluorescent protein expressed by yeast cells from the spectrally overlapping autofluorescence of the yeast cells and the growth media.

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to ''achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life.'' While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, ''Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling.'' This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.

Abstract not provided.

Hyperspectral Fourier transform infrared images have been obtained from a neoprene sample aged in air at elevated temperatures. The massive amount of spectra available from this heterogeneous sample provides the opportunity to perform quantitative analysis of the spectral data without the need for calibration standards. Multivariate curve resolution (MCR) methods with non-negativity constraints applied to the iterative alternating least squares analysis of the spectral data has been shown to achieve the goal of quantitative image analysis without the use of standards. However, the pure-component spectra and the relative concentration maps were heavily contaminated by the presence of system artifacts in the spectral data. We have demonstrated that the detrimental effects of these artifacts can be minimized by adding an estimate of the error covariance structure of the spectral image data to the MCR algorithm. The estimate is added by augmenting the concentration and pure-component spectra matrices with scores and eigenvectors obtained from the mean-centered repeat image differences of the sample. The implementation of augmentation is accomplished by employing efficient equality constraints on the MCR analysis. Augmentation with the scores from the repeat images is found to primarily improve the pure-component spectral estimates while augmentation with the corresponding eigenvectors primarily improves the concentration maps. Augmentation with both scores and eigenvectors yielded the best result by generating less noisy pure-component spectral estimates and relative concentration maps that were largely free from a striping artifact that is present due to system errors in the FT-IR images. The MCR methods presented are general and can also be applied productively to non-image spectral data.

Monitoring of dielectric thin-film production in the microelectronics industry is generally accomplished by depositing a representative film on a monitor wafer and determining the film properties off line. One of the most important dielectric thin films in the manufacture of integrated circuits is borophosphosilicate glass (BPSG). The critical properties of BPSG thin films are the boron content, phosphorus content and film thickness. We have completed an experimental study that demonstrates that infrared emission spectroscopy coupled with multivariate analysis can be used to simultaneous y determine these properties directly from the spectra of product wafers, thus eliminating the need of producing monitor wafers. In addition, infrared emission data can be used to simultaneously determine the film temperature, which is an important film production parameter. The infrared data required to make these determinations can be collected on a time scale that is much faster than the film deposition time, hence infrared emission is an ideal candidate for an in-situ process monitor for dielectric thin-film production.

Abstract not provided.

A significant improvement to the classical least-squares (CLS) multivariate analysis method has been developed. The new method, called prediction-augmented classical least-squares (PACLS), removes the restriction for CLS that all interfering spectral species must be known and their concentrations included during the calibration. We demonstrate that PACLS can correct inadequate CLS models if spectral components left out of the calibration can be identified and if their 'spectral shapes' can be derived and added during a PACLS prediction step. The new PACLS method is demonstrated for a system of dilute aqueous solutions containing urea, creatinine, and NaCl analytes with and without temperature variations. We demonstrate that if CLS calibrations are performed with only a single analyte's concentrations, then there is little, if any, prediction ability. However, if pure-component spectra of analytes left out of the calibration are independently obtained and added during PACLS prediction, then the CLS prediction ability is corrected and predictions become comparable to that of a CLS calibration that contains all analyte concentrations. It is also demonstrated that constant-temperature CLS models can be used to predict variable-temperature data by employing the PACLS method augmented by the spectral shape of a temperature change of the water solvent. In this case, PACLS can also be used to predict sample temperature with a standard error of prediction of 0.07°C even though the calibration data did not contain temperature variations. The PACLS method is also shown to be capable of modeling system drift to maintain a calibration in the presence of spectrometer drift.

The advent of inductively coupled plasma-atomic emission spectrometers (ICP-AES) equipped with charge-coupled-device (CCD) detector arrays allows the application of multivariate calibration methods to the quantitative analysis of spectral data. We have applied classical least squares (CLS) methods to the analysis of a variety of samples containing up to 12 elements plus an internal standard. The elements included in the calibration models were Ag, Al, As, Au, Cd, Cr, Cu, Fe, Ni, Pb, Pd, and Se. By performing the CLS analysis separately in each of 46 spectral windows and by pooling the CLS concentration results for each element in all windows in a statistically efficient manner, we have been able to significantly improve the accuracy and precision of the ICP-AES analyses relative to the univariate and single-window multivariate methods supplied with the spectrometer. This new multi-window CLS (MWCLS) approach simplifies the analyses by providing a single concentration determination for each element from all spectral windows. Thus, the analyst does not have to perform the tedious task of reviewing the results from each window in an attempt to decide the correct value among discrepant analyses in one or more windows for each element. Furthermore, it is not necessary to construct a spectral correction model for each window prior to calibration and analysis: When one or more interfering elements was present, the new MWCLS method was able to reduce prediction errors for a selected analyte by more than 2 orders of magnitude compared to the worst case single-window multivariate and univariate predictions. The MWCLS detection limits in the presence of multiple interferences are 15 rig/g (i.e., 15 ppb) or better for each element. In addition, errors with the new method are only slightly inflated when only a single target element is included in the calibration (i.e., knowledge of all other elements is excluded during calibration). The MWCLS method is found to be vastly superior to partial least squares (PLS) in this case of limited numbers of calibration samples.

The analysis precision of any multivariate calibration method will be severely degraded if unmodeled sources of spectral variation are present in the unknown sample spectra. This paper describes a synthetic method for correcting for the errors generated by the presence of unmodeled components or other sources of unmodeled spectral variation. If the spectral shape of the unmodeled component can be obtained and mathematically added to the original calibration spectra, then a new synthetic multivariate calibration model can be generated to accommodate the presence of the unmodeled source of spectral variation. This new method is demonstrated for the presence of unmodeled temperature variations in the unknown sample spectra of dilute aqueous solutions of urea, creatinine, and NaCl. When constant-temperature PLS models are applied to spectra of samples of variable temperature, the standard errors of prediction (SEP) are approximately an order of magnitude higher than that of the original cross-validated SEPs of the constant-temperature partial least squares models. Synthetic models using the classical least squares estimates of temperature from pure water or variable-temperature mixture sample spectra reduce the errors significantly for the variable temperature samples. Spectrometer drift adds additional error to the analyte determinations, but a method is demonstrated that can minimize the effect of drift on prediction errors through the measurement of the spectra of a small subset of samples during both calibration and prediction. In addition, sample temperature can be predicted with high precision with this new synthetic model without the need to recalibrate using actual variable-temperature sample data. The synthetic methods eliminate the need for expensive generation of new calibration samples and collection of their spectra. The methods are quite general and can be applied using any known source of spectral variation and can be used with any multivariate calibration method.

We have completed an experimental study to investigate the use of infrared emission spectroscopy (IRES) for the quantitative analysis of borophosphosilicate glass (BPSG) thin films on silicon monitor wafers. Experimental parameters investigated included temperatures within the range used in the microelectronics industry to produce these films; hence the potential for using the IRES technique for real-time monitoring of the film deposition process has been evaluated. The film properties that were investigated included boron content, phosphorus content, film thickness, and film temperature. The studies were conducted over two temperature ranges, 125 to 225 *C and 300 to 400 *C. The later temperature range includes realistic processing temperatures for the chemical vapor deposition (CVD) of the BPSG films. Partial least squares (PLS) multivariate calibration methods were applied to spectral and film property calibration data. The cross-validated standard errors of prediction (CVSEP) fi-om the PLS analysis of the IRES spectraof21 calibration samples each measured at 6 temperatures in the 300 to 400 "C range were found to be 0.09 wt. `?40 for B, 0.08 wt. `%0 for P, 3.6 ~m for film thickness, and 1.9 *C for temperature. By lowering the spectral resolution fi-om 4 to 32 cm-l and decreasing the number of spectral scans fi-om 128 to 1, we were able to determine that all the film properties could be measured in less than one second to the precision required for the manufacture and quality control of integrated circuits. Thus, real-time in-situ monitoring of BPSG thin films formed by CVD deposition on Si monitor wafers is possible with the methods reported here.

Multivariate calibration methods have been applied extensively to the quantitative analysis of Fourier transform infrared (FT-IR) spectral data. Partial least squares (PLS) methods have become the most widely used multivariate method for quantitative spectroscopic analyses. Most often these methods are limited by model error or the accuracy or precision of the reference methods. However, in some cases, the precision of the quantitative analysis is limited by the noise in the spectroscopic signal. In these situations, the precision of the PLS calibrations and predictions can be improved by the incorporation of weighting in the PLS algorithm. If the spectral noise of the system is known (e.g., in the case of detector-noise-limited cases), then appropriate weighting can be incorporated into the multivariate spectral calibrations and predictions. A weighted PLS (WPLS) algorithm was developed to improve the precision of the analysis in the case of spectral-noise-limited data. This new PLS algorithm was then tested with real and simulated data, and the results compared with the unweighted PLS algorithm. Using near-infrared (NIR) calibration precision when the WPLS algorithm was applied. The best WPLS method improved prediction precision for the analysis of one of the minor components by a factor of nearly 9 relative to the unweighted PLS algorithm.

An experimental system for the characterization of metal/dielectric interfaces has been developed. Attenuated Total Reflection (ATR) spectroscopy of a dielectric on a thin metal film, deposited on a multiple reflection ATR element, yields information about the bonding, or lack thereof, at the metal/dielectric interface. At a certain metal thickness, the absorbance due to molecules at the interface, relative to the signal from the bulk dielectric, is at a maximum. A model which uses the Fresnel equations in matrix form, has been used to predict the best metal thickness for each dielectric/metal/ATR element system. The ATR element may be placed in an environmental chamber in which the temperature, humidity etc. can be varied, in order to test the integrity of the interface to hostile environments. Chemometric analysis of the IR spectral data maximizes our ability to measure small changes in the interface properties. Preliminary results from polyimide/metal samples are presented.

Infrared absorption spectra of borophosphosilicate glass (BPSG) thin films were collected to develop a rapid classification method for determining if the films are within the desired specifications. Classification of samples into good and bad categories was performed using principal component analysis applied to the spectra. Mahalanobis distances were used as the classification metric. The highest overall percentage of correct classification of samples based upon their spectra was 91.6%.

Infrared emission (IRE) spectra were obtained from two borophosphosilicate glass (BPSG) thin-film sample sets. The first set consisted of 21 films deposited on undoped silicon wafers, and the second set consisted of 9 films deposited on patterned and doped (product) wafers. The IRE data were empirically modeled using partial least-squares calibration to simultaneously quantify four BPSG thin-film properties. The standard errors of the determinations when modeling the 21 monitor wafers were

Infrared emission spectra of 21 borophosphosilicate glass (BPSG) thin films on silicon wafers were collected with the samples held at constant temperature between 125--400{degree}C using a heating stage designed for precise temperature control ({plus_minus}{degree}C). Partial test squares calibrations applied to the BPSG infrared emittance spectra allowed four BPSG thin-film properties to be simultaneously quantified with precisions of 0.1 wt. % for boron and phosphorus, 35 {Angstrom} for film thickness, and 1.2{degree}C for temperature.

Experiments were performed to examine sensitivity of thin-film property determinations to several experimental variables when applying multivariate calibration methods to infrared reflection spectroscopic data. Results indicate that low angles of incidence are best for robust quantitative determination of boron, phosphorus, and film thickness in borophosphosilicate glass (BPSG) dielectric films. However, the polarization state of the incidence beam does not affect the quantitative prediction ability.

Infrared (IR) reflection spectroscopy has been shown to be useful for making rapid and nondestructive quantitative determinations of B and P contents and film thickness for borophosphosilicate glass (BPSG) thin films on silicon monitor wafers. Preliminary data also show that similarly precise determinations can be made for BPSG films on device wafers.

Partial least squares multivariate calibration methods were applied to the infrared spectra of a new set of borophosphosilicate glass (BPSG) thin films on silicon wafers. The calibration samples were prepared by a low pressure chemical vapor deposition (LPCVD) process. The statistically designed calibration set included data from nearly 400 coated Si wafers. Calibrations were attempted for properties such as dopant concentrations, thickness, etch rate, film stress, and electrical parameters. It was found that annealed films were predicted more precisely than unannealed films. B, P, and thickness measurements yielded the most precise results by these techniques. Multivariate calibration methods applied to etch rate for annealed films and unannealed film stress provided some limited predictive ability. The detection and removal of outliers greatly improved the analysis precisions. Finally, within wafer and between wafer dopant uniformity may be responsible for degrading the precision of these analytical methods.