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Chemometrics Barry K. Lavine*,† and Jerome Workman, Jr.†
Department of Chemistry, Clarkson University, Potsdam, New York 13699, and Analytical & Measurement Technology, Kimberly-Clark Corporation, Neenah, Wisconsin 54956 Review Contents Image Analysis Chemometrics Applied to Sensors Chemoinformatics Literature Cited
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This review, the fourteenth of the series, and the twelfth with the title “Chemometrics” covers the most significant developments in the field from November 1999 to January 2002. As in the previous review (A1), breakthroughs and advances in the field are highlighted, trends evaluated, and challenges that must be successfully met to ensure continued progress in the field delineated. The current review is limited to approximately 100 references, which poses a challenge since the number of citations on chemometrics continues to show steady growth as measured by the number of literature citations on the subject. Several definitions for chemometrics have appeared in the chemical literature since the inception of the field in 1972. Workman (A2) has examined these definitions with the emphasis on the message communicated by them to the industrial and scientific community. The message to the community is that chemometrics is a process. Measurements are made; data are collected; information is obtained with the information periodically assessed to acquire actual knowledge. This, in turn, has led to a new approach for solving routine problems: (1) measure a phenomenon or process using chemical instrumentation that generates data inexpensively, (2) analyze the multivariate data, (3) iterate if necessary, (4) create and test the model, and (5) develop fundamental multivariate understanding of the process. The new approach does not involve a thought ritual; rather it is a method involving many inexpensive measurements, possibly a few simulations, and chemometric analysis. It constitutes a true paradigm shift since multiple experimentation and chemometrics are used as a vehicle to examine the world from a multivariate perspective. Mathematics is not used for modeling per se but more for discovery and is thus a data microscope to sort, probe, and look for hidden relationships in data. This new approach attempts to explore the implications of data so that hypotheses, i.e., models of the data, are developed with a greater awareness of reality. This exploratory data mining approach in some respects is more rigorous than simply formulating a hypothesis from a set of observations, since a variety of techniques can be used to validate the model with predictive success being the most powerful. If predictive success can be repeated and found to hold well for the observed experimental † ‡
Clarkson University. Kimberly-Clark Corp.
10.1021/ac020224v CCC: $22.00 Published on Web 05/01/2002
© 2002 American Chemical Society
data, then the successful model can be articulated into cause and effect, not just simple correlation. This new approach, which looks at all the data using multivariate methods, is the basis of combinatorial chemistry, which has revolutionized drug discovery (A3). This new method seeks so-called local solutions to a complex problem. That is, a particular model (i.e., hypothesis) holds for a given set of conditions. If there are changes in these conditions, then it is a different relationship that holds true for the data. The necessity of developing multiple models (i.e., hypotheses) to describe chemical data is inevitable because of the inescapable simplifications and approximations inherent in chemical theory. For example, the relationship between solubility (macroscopic variable) and water-accessible surface area (microscopic variable) is approximate, valid within only certain intervals, and is not the same for both chlorinated hydrocarbons and sugars. Although the pharmaceutical industry has embraced the chemometric approach, few chemists in academia or industry actually take advantage of it. Chemometrics is considered to be too complex; the mathematics can be misinterpreted as esoteric and not relevant; problems arise in the implementation and maintenance of these methods; and there is a lack of official practices and methods associated with chemometrics. This, despite the clear advantages of chemometrics, which include but are not limited to speed in obtaining real-time information from data, extraction of high-quality information from less well resolved data, clear information resolution and discrimination power when applied to second and third-order data, and improved knowledge of existing processes. Practitioners of chemometrics, that is, researchers actively engaged in the development and publication of chemometric methods, are cognizant of the problems limiting the dissemination of chemometrics in industry and academia. There is belated recognition of the challenges posed to ensure the compliance of chemometric solutions in regulatory environments. There are at least three working groups actively involved in drafting specifications for the use of chemometric methods in process analytical chemistry and pharmaceutics. One group with chemometric interests addressing specifically the needs and requirements of the pharmaceutical industry is the FDA Subcommittee on Process Analytical Technologies (PAT), formally a Subcommittee of the Advisory Committee for Pharmaceutical Science. Within this subcommittee there is a formal working group on chemometrics. The group is concerned with formulating the role of and issues related to the use of chemometrics in PAT. One important distinction that this group is delineating relative to the role of chemometrics versus that of statistics is that in defining cause and effect relative to chemically based multivariate data, it is Analytical Chemistry, Vol. 74, No. 12, June 15, 2002 2763
essential that the data analyst has a detailed understanding of the underlying chemical principles involved in the generation of the data. The chemist must understand the mathematics rather than the mathematician trying to understand the chemistry. This theme follows with the renewed definition of chemometrics as a discovery tool as much as a modeling tool (A4). There is also a need to develop standard libraries of data processing and preprocessing algorithms. A second group heavily involved in this topic is the American Society for Testing and Materials (ASTM) International Main Committee E13 on Molecular Spectroscopy and Chromatography. This committee has two subcommittees working on related standards, these being subcommittee E13.11 on Chemometrics discussing standard codification of chemometric methods for manufacturing. Additionally, there is a Task Group of E13.01, which is in discussions regarding SpectroML (GAML and XML) data formatting and related issues (A5). Although file and communication standards are crucial to on-line applications of chemometrics, there is currently no accepted format. A third group we will mention is Chemometrics for On-Line Process Analytics (COPA), a new Center for Process Analytical Chemistry (CPAC) initiative which is drawing input from the user community relative to the needs of those scientists and engineers who must apply multivariate calibration techniques for process monitoring and control. The group is looking to make the connection between the off-line analyst development environment and the on-line analyzer runtime environment. The specific tasks at hand have been proposed to consist of the Open Access Development Environment Specification (OADES) and the Universal Runtime Prediction Engine Specification (URPES). Thus, if these protocols were to be standardized, the development chemometrician would have standard output formats for modeling information enabling connection into process analyzers using COPA standard formats and protocols (A6). Implementation of on-line chemometrics also requires more sophisticated data analysis techniques such as a layer of models (instead of a single model) developed using a hierarchy of data preprocessing and data processing routines. Each model would be triggered by a particular control event. To implement this layer, it would probably be necessary to use an object-oriented approach to develop the appropriate software solution (A7). An object is defined as a construct that contains the model, the preprocessing parameters, and the code (e.g., C++ or some higher level language such as MATLAB) to express the model. The end user simply assembles the objects in the desired order for the specific application. However, an engine would be needed to link these objects. The advantage of this approach is that it would significantly reduce operator time and involvement in making decisions related to classification or calibration issues. Calibration of infrared and near-infrared spectrometers is the most popular application of chemometrics in both industry and academia (A8). However, the question remains as to whether it is the best and most desirable use of this technology. Do scientists and managers get excited about calibrating a sensor using a new technique or answering the question as to whether partial least squares or principal component regression is better for a particular application. We think not. Because the success of any field is 2764
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determined by the importance of the problems solved by its practitioners, the question of relevancy has always been an important issue in chemometrics. Much of the criticism directed toward this field by chemists and statisticians has centered on this very issue. The focus of this review is the use chemometrics to revolutionize the very roots of problem solving in analytical chemistry. Articles that are cited in each section of the review possess one or more of the following attributes: (1) chemometrics was used to foster new discoveries and insights, (2) the learning paradigm has been shifted away from classical hypothesis testing to multivariate exploration and analysis as a result of chemometrics, and (3) data have been extracted and presented in a manner that more effectively interfaces with humans. Examples of articles recently published in the literature where chemometrics has been used to foster discovery include the use of experimental design techniques and parallel factor analysis to understand the causes of drift in ICP-AES (A9), principal component analysis and selforganizing neural network maps to analyze DNA array profile data from microbial communities in glucose fed methanogenic bioreactors in order to decipher community dynamics and identify specific phylotypes (A10), use of multivariate analysis to evaluate data on the biochemical reaction pathways of Archaea and Eukaryotes species to better understand the origin of the biological variation upon which natural selection acts (A11), and the use of near-infrared spectroscopy and principal component analysis to differentiate between genetic and environmental effects involving normal barley lines and the gene regulator lys3a in different genetic backgrounds (A12). Chemometrics is an application-driven field. Any review of this field cannot and should not be formulated without focusing on so-called novel or exciting applications. Therefore, this review has been divided into three sections with each section corresponding to an application area that has been judged to be exciting or hot. The criteria used to select these three application areas are based in part on the number of literature citations uncovered during the search and in part on the perceived impact that developments in these particular areas will have on chemometrics and analytical chemistry. The three application areas that are highlighted in this review are image analysis, chemometrics applied to sensors, and chemoinformatics. Image analysis attempts to exploit the power gained by interfacing human perception with cameras and imaging systems that utilize the entire electromagnetic spectrum. Insight into chemical and physical phenomena can be garnered where the superior pattern recognition of humans over computers provides us with a strong argument to develop chemometric tools for imaging. These include tools for interpretation, creation, or extraction of virtual images from real data, data compression and display, image enhancement, and three-dimensional views into structures and mixtures. Chemometrics has an even greater potential to improve sensor performance than miniaturization of hardware. Fast computations combined with multivariate sensor data can provide the user with continuous feedback control information for both the sensor and process diagnostics. The sensor can literally become a selfdiagnosing entity, flagging unusual data that arise from a variety of sources including a sensor malfunction, a process disruption,
an unusual event, or a sampling issue. Single sensors or mixed arrays of different types of sensors can be networked into an intelligent system, which can send an alarm to a human operator when questions or unusual circumstances arise. Chemoinformatics encompasses the analysis, visualization, and use of chemical information as a surrogate variable for other data or information. Contemporary applications of chemoinformatics include among others diversity analysis, library design, and virtual screening. Chemical structure representation through descriptors that capture the structural characteristics and properties of molecules is an unsolved problem in chemoinformatics. Relating chemical structure to biological activity or physical and chemical properties is not a new endeavor; the ability to perform this task on a large data set, however, presents challenges that will require an assortment of new computational methods including new methods for analysis and visualization of data. Other important topics of interest to chemometricians include the most recent developments in mathematical data mining and modeling approaches such as machine learning and genetic algorithms for pattern recognition analysis and multivariate calibration, and multiway analysis. It is important to note that chemometrics research groups today often collaborate successfully with psychometricians, bioinformaticians, and electrical and computer engineering groups, such as IEEE. The next review article might be used to address the details of new algorithmic and mathematical approaches in the field. For this review, applications combining dramatic significance and at least a modicum of published research papers were selected for inclusion. There is a renewed interest in chemometrics for process control in pharmaceutics. Chemometrics are useful for new approaches to process control, monitoring, and process modeling. Key applications such as imaging (dealt with as a separate issue in this review), mixing and uniformity studies, and end point determination for reactions and processes are most prolific. When combined with sound experimental design, a process can be continuously monitored, studied, evaluated, and redesigned in real time using chemometric approaches. Important direct contributions of chemometrics to pharmaceutical technology include the development of mathematical tools for experimental design, data preprocessing, sensor calibration, and real-time diagnostics. Creating tools to properly predict with and validate the sensor for specific quality information is also essential. Sensors are also dealt with as a separate topic in this paper. Another potential focal topic is combinatorial chemistry and microreactor monitoring, modeling, and control. Microreactors allow complex chemistries to be conducted at a miniature scale at relatively low temperatures. The favorable kinetics and increased yield and efficiency potential of microreactor systems promises a bold change in the traditional chemical and pharmaceutical manufacturing processes. A major requirement of microreactors is continuous monitoring of reaction chemistry and quality derived in near real time. Such information can contribute to a network of microreaction systems replacing traditional single large batch reactions where both kinetics and efficiency are unfavorable for high-purity and high-yield conditions. The use of multiple miniaturized sensors combined with chemometric data processing can be a valuable enhancement to the future of drug and fine chemical manufacturing technology. However, this work
is in near infancy and there is very scant literature on the subject to date. IMAGE ANALYSIS Chemical imaging is a combination of molecular spectroscopy and digital imaging. Data sets generated by chemical imaging are large, are multivariate, and require significant processing. Schweitzer (B1) reviewed the use of chemometric techniques to process and visualize imaging data. Segmentation and classification tasks can be impeded by the high dimensionality of data in image analysis. Al-Nuaimy (B2) proposed a procedure for feature selection that can automatically select the set of texture features best suited for a particular application. Wold (B3) investigated two feature extraction methods to classify images with respect to protein type. Geladi (B4) compared image PLS to image PCA by using comparison data sets. He discovered that image PLS decomposes the data differently from image PCA in accordance with previous experience. Many applications of image analysis continue to focus on brain imaging. Buydens (B5) has reviewed this field with emphasis on combining MRI and MRSI images using chemometrics to obtain additional information on test subjects. Nicholson (B6) and Windig (B7, B8) have also examined the use of pattern recognition and three-way techniques in biomedical magnetic resonance imaging. Other applications of image analysis include the use of autofluorescence spectroscopy and electron microscopy to image human tissue, food, and powders. Harvey (B9) provides a review and discussion of a fiberoptic microspectrophotometer for fluorescence imaging of frozen tissue sections. The potential of autofluorescence for skin cancer detection was assessed using linear discriminant analysis and principal component plots to analyze the spectral features derived from the images. Wold (B10) mapped lipid oxidation in chicken meat by autofluorescence imaging using principal component analysis to display the data. Wold (B11) also quantified the intramuscular fat content in beef by combining data from autofluorescence spectra and autofluorescence images and then analyzing the data using partial least squares to develop robust prediction models. Esbensen (B12, B13) evaluated signal complexity as a function of geometric scale from local to global in spectroscopic images using the so-called angle measure technique (AMT) to transform the image data. Principal component analysis of AMT spectra derived from image data was able to discriminate powders by type. Light microscopy coupled with digital imaging was used to develop a potential method to measure a range of microstructual features in thin sections of raw and cooked potatoes (B14). Image features could be correlated with sensory and textual properties using partial least squares regression. CHEMOMETRICS APPLIED TO SENSORS Multivariate calibration has been successfully applied to the monitoring of chemical processes. However, difficulties arise because sensors are inherently prone to drift and processes are susceptible to unmodeled disturbances. Kowalski (C1) addressed this problem by investigating various weighting schemes to update the calibration model using additional calibration samples that contain the new chemical interference or the instrumental variation. Artursson (C2), on the other hand, used principal component analysis and partial least squares to affect simple drift counteraction. Thomas (C3) addressed this problem using statistical design Analytical Chemistry, Vol. 74, No. 12, June 15, 2002
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techniques, which considers the environmental and instrumental factors during the experimental design phase to develop robust calibrations. For sensor arrays, the problem of drift is even more complex. Hines (C4) reviewed the literature on this subject, which includes the use of pattern recognition techniques, neural networks, and fuzzy-based paradigms. Akybaryan (C5) developed a pattern recognition-based method to identify malfunctioning sensors. Input patterns belonging to a class of events are first scaled to unit variance and zero mean; digital band-pass filtering is then used to eliminate the noisy segments. A wavelet-based method is employed for each new sensor to extract pattern features. Principal component analysis is then applied to characterize the information content of the pattern features. Doymaz (C6) approached this same problem using Hotelling T2 statistic and principal component analysis. During this reporting period, there has been renewed interest in the deployment of field-portable and inexpensive chemical sensors in process monitoring environments. Booksh (C7, C8) demonstrated that inexpensive optical waveguides, which generate SPR features broader than prism-based instruments, can achieve an acceptable level of accuracy when multivariate methods of calibration such as PLS or weighted regression are applied. The combined use of a unique fiber-optic system, FT-IR, and PLS to characterize glucose single-beam spectra has resulted in the development of an on-line noninvasive method to monitor glucose concentration in cell culture medium (C9). Gemperline (C10) showed that window factor analysis can be used to deconvolute concentration time profiles of reactants, products, and intermediates in a batch process obtained from spectra collected by an inexpensive fiber-optic UV/visible spectrometer. Esbensen (C11, C12) investigated the feasibility of using acoustic emission and PLS-2 to monitor EMF-induced pipeline wall friction reduction by intercalibration of laser velocimetry data. The simultaneous determination of ammonia and relative humidity in air continues to be a challenging problem. Narayanaswamy (C13) tackled this problem using an inexpensive optical fiber chemical sensor based on a Nafion-crystal violet composite and a multivariate calibration based on an artificial neural network. Most of the citations on applications of chemometrics to sensors have focused on sensor arrays. Lindstrom (C14) reviewed the use of electronic tongues, sensor arrays, and pattern recognition techniques for capturing signal patterns. Garcia-Villar (C15) describes the development of a potentiometric sensor array for the determination of lysine in feed samples. The sensor array consists of a lysine biosensor and several ion-selective electrodes. Although the selectivity of the lysine biosensor itself is not sufficient, chemometric analysis of the information contained in the multisensor array can circumvent this problem. Sales (C16) showed that multivariate standardization of sensors in arrays will improve sensor performance. They used piecewise standardization and a Kernel Stone algorithm to select standardization samples from the original responses. Ivarsson (C17) compared a voltametric electronic tongue to a lipid membrane taste sensor. Although the two sensor systems performed equally well, additional information can be garnered by combining these two sensor systems. Wallace (C18) applied principal component analysis to select conducting electroactive polymers (CEP) and applied voltage waveforms to discriminate potassium and methy2766
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lamine in flow injection analysis using CEP sensor arrays as the detector for a wide range of analytes. Lewis (C19) used pattern recognition techniques to analyze the multivariate data from vapor sensing arrays of swellable polymer composites containing chiral films to detect chiral organic vapor analytes. Porter (C20) compared two artificial intelligence-based approaches (decision trees versus hill climbing algorithms) for the optimum selection of the components of a sensor array used for the identification of volatile organic components. The array consists of quartz crystal microbalances, each coated with a different polymeric material. Menzel (C21) developed a dynamic gas classification model for an electronic nose to achieve reliable discrimination. The model, which combines classification of steady states with transient time series analysis, performed better than SIMCA or discriminant analysis. Venger (C22) describes a novel strategy aimed at improving the quality and quantity of chemical information obtained from sensing instruments based on incorporation of the kinetic aspects of the array response. Surface acoustic wave (SAW) devices continue to be an active area of research with a large and growing literature (C23). Grate (C24-C26) describes a novel method for the characterization and classification of unknown vapors based on the response of an array of polymer-coated acoustic wave sensors. The method does not require the system to be trained on all samples; rather, solvation parameters of the unknown vapor are estimated given the sensor responses and the linear solvation energy relation coefficients of the sorbent polymer coating. The vapor is then identified from a database of candidate vapor parameters. Frye (C27) describes a portable SAW array instrument with integrated chemometric software for identification of organic compounds such as paraffin, aromatics, and chlorinated hydrocarbons. The system was trained using known chemicals. Sivavec (C28) evaluated SAW arrays for recognition and quantitation of TCE, PCE, VC, and DCE species using locally weighted regression and partial least squares. Sensor arrays using coated piezoelectric quartz crystals continue to garner interest among researchers. Zenkel (C29) showed that partial least squares and artificial neural networks can leverage quantitative information from mass-sensitive sensor arrays that utilize supramolecular host-guest chemistry. Xylene isomers could be detected with an accuracy of approximately 0.1% while nearly eliminating the residual water cross sensitivity of the sensor coating. Electronic noses utilizing MOSFET technology can detect gaseous emissions from leather when combined with pattern recognition analysis routines. Using principal component analysis, Lundstrom (C30) demonstrated that MOSFET arrays yield information comparable to GC/MS. The proposed system could be useful in on-line quality monitoring during leather manufacturing to detect anomalies in car interior trim materials. Electronic noses have also been used to discriminate Helicobacter pylori and other gastroesophageal isolates in vitro (C31). Artificial neural networks were used to develop a classification rule that could cluster various bacterial classes. CHEMOINFORMATICS Many contemporary applications in computer-aided drug discovery and chemoinformatics depend on the representation of molecules by descriptors. During the past twenty-five years, hundreds of molecular descriptors have been reported in the
literature ranging from topological indices to complex molecular fingerprints, which sometimes consists of thousands of bit positions. Bajorath (D1) reviewed the progress made in this area. Estrada (D2) also reviewed the same literature focusing on the role of topological indices in lead discovery whereas Trophsha’s review (D3) focused on the general concept of the descriptor pharmacophore. New types of molecular descriptors continue to be developed during this reporting period including BCUT descriptors (D4, D5) for characterizing the structure of organic molecules, and so-called GRID (D6-D8)-based descriptors for characterizing the structure of proteins. BCUT descriptors contain information about molecular structure that complements the better known two-dimensional fingerprints and three-dimensional pharmacophore-based descriptors. The GRID descriptors are also readily interpretable and easy to compute. First, the molecular interaction fields are simplified, followed by a second step in which the results are subjected to an autocorrelation function producing alignment independent variables. The molecular descriptors so obtained can be used to obtain graphical diagrams called correlograms that are amenable to chemometric techniques such as principal component analysis and partial least squares. Muskal (D9) described a new method of pharmacophore fingerprinting where a basis set of ten 549 three-point pharmacophores were constructed by enumerating several distances and pharmacophoric features. Software was developed to assign pharmacophoric types to atoms in each chemical structure. Chemical structure representation is a problem that continues to plague the field of chemoinformatics. Another problem plaguing the field is the mining of data and visualization of relationships in large chemical databases. Weinstein and co-workers (D10) investigated various statistical and artificial intelligence methods for data mining including principal component analysis, hierarchical clustering, multidimensional scaling, and neural network modeling in the context of the National Cancer Institute’s (NCI’s) 70 000-compound database. A vector of 60 anticancer activity values characterizes each compound in the database. King (D11) using Warmr, a data mining method from the field of inductive logic programming, also analyzed the same database. The Warmr method identified common substructures in the database and used them to develop probabilistic prediction rules relating chemical structure to carcinogenicity. Lavine (D12) developed a genetic algorithm for data mining that identified a set of features (molecular descriptors) that optimized the separation of the classes in a plot of the two or three largest principal components of the data. The principal component analysis routine embedded in the fitness function of the GA acted as an information filter, significantly reducing the size of the search space, since it restricts the search to features whose principal component plots showed clustering on the basis of class. In addition, the algorithm focuses on those classes or samples that are difficult to classify as it trains using a form of boosting to modify the class and sample weights. Boosting minimizes the problem of convergence to a local optimum because the fitness function of the GA changes as the population evolves toward a solution. With the emergence of combinatorial chemistry, it is now possible to generate a large number of diverse or focused compound libraries. By applying nonparametric statistical methods, e.g., recursive partitioning, to large data sets containing
thousands of compounds and their associated biological data, it is possible to improve the hit rate of primary screens while screening efficiency is increased (D13). Experimental design is another approach that can be taken to improve the hit rates of primary screens. Wold (D14) applied statistical designs to the building block sets and to the full set of virtually constructed products. No difference in efficiency was found between selections made in the building block space and in the product space. Diversity of large chemical libraries is crucial in the design of combinatorial chemistry and high-throughput screening experiments. Agrafiotis (D15) developed an algorithm for estimating diversity in a large chemical library. The algorithm does not require exhaustive enumeration of all pairwise distances in the data set. 1H NMR has proven to be a powerful and efficient means of monitoring the interactions of pharmacological agents with cell and tissues. Using 1H NMR and pattern recognition techniques, screens can be developed to detect drug-induced toxicity. Shockcor (D16) reviewed the literature on this subject. The therapeutic efficiency of an orally administered drug is dictated not only by its pharmacological properties but also ny its pharmacokinetic properties such as access to the site of activity. Goodwin (D17) examined the relationships of various computationally derived molecular geometric descriptors for a set of peptides and peptidomimetics. No significant correlation of cellular permeability with computed descriptors was found. This was attributed to the inability to identify surrogates for hydrogenbonding desolvation potential for the solutes among the descriptors. Frokjaer (D18) was able to develop a structure-property model for membrane partitioning of oligopeptides via PLS using two chromatographic measurements and three calculated parameters as molecular descriptors from which latent structures were developed. The model supported the claim that large hydrogenbonding potential and the presence of negative charge impair membrane partitioning whereas hydrophobic parameters promote partitioning. Gottfries (D19) describes the development of a minimalistic model for oral drug absorption using PLS and simple two-dimensional descriptors. A PLS model of blood-brain barrier permeation (D20) and the brain blood distribution (D21) was developed using topological, constitutional, and three-dimensional descriptors. Osterberg (D22, D23) also used PLS to model and predict drug transport processes using simple theoretical computed molecular descriptors such as molecular connectivity indices. In conclusion, the field of chemometrics is in a suitable position to enter into a variety of important multivariate problem-solving issues facing science and industry in the 21st century. The ever expanding endeavors of imaging, sensor development, chemoinformatics, machine learning, combinatorial chemistry, microreactors, and multivariate data exploration will all prove to be challenging opportunities for new scientific insights and improved processes. Barry K. Lavine is an Associate Professor of Chemistry at Clarkson University in Potsdam, NY. He has published more than 80 papers in chemometrics and is on the editorial board of several journals. He is also Assistant Editor of Chemometrics for Analytical Letters. Lavine’s research interests encompass many aspects of the application of computers to chemical analysis including multivariate curve resolution, pattern recognition, and multivariate calibration using genetic algorithms and other evolutionary techniques.
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Jerome (Jerry) J. Workman, Jr. is a Senior Research Fellow at Kimberly-Clark Corporation, Analytical & Measurement Technology group. He has published nearly 400 scientific papers, proprietary corporate research technical reports, multiple inventions and patents, and six volumes on spectroscopy. He is a Fellow and Chartered Chemist of the Royal Society of Chemistry (UK), ASTM International, and the American Institute of Chemists. He is current Chair of ASTM E13 Main Committee on Molecular Spectroscopy and Chromatography and is on the Governing Board of the Coblentz Society. He serves as Executive Editor of Spectroscopy Letters, Associate Editor of Applied Spectroscopy Reviews, and as an Editorial Advisory Board member for Spectroscopy magazine. He is a member of Sigma Xi, the American Chemical Society (26 years), ASTM International, and the Coblentz Society. He has served as a graduate industrial advisor for several institutions and has received a number of awards for his work. He received the B.A. degree (1976) cum laude in natural sciences and M.A. degree (1980) Delta Epsilon Sigma from Saint Mary’s University of Minnesota; and the Ph.D. degree (1984) in biological chemistry from Columbia Pacific University, CA. He has completed Executive and Technical Management certificates at Columbia University, NY (1990-1991, 1999-2000), and MIT (2001).
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