Anal. Chem. 1998, 70, 209R-228R
Chemometrics Barry K. Lavine
Department of Chemistry, Clarkson University, Potsdam, New York 13699 Review Contents Statistics Optimization Signal Processing Resolution Multivariate Calibration Parameter Estimation Structure-Activity Relationship Studies Pattern Recognition Library Searching Artificial Intelligence Literature Cited
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Chemometrics is an approach to analytical and measurement science based on the idea of indirect observation. Measurements related to the chemical composition of a substance are taken, and the value of a property of interest is inferred from them through some mathematical relation. Chemometrics works because the properties of substances such as gasoline are uniquely defined by their chemical composition. Indirect observation of a property is the goal, for reasons such as speed and economy. Most of the articles cited, in this review period, involved applications of chemometric methods. Many of these applications were in new fields, such as image analysis. There was also strong interest in artificial neural networks, wavelets, and genetic algorithms, as evidenced by the large number of publications involving these methods. However, most of the citations in the Chemical Abstracts database focused on more traditional applications: pattern recognition and multivariate calibration. For example, pattern recognition in the food industry had more citations than any other search category. The interest in traditional chemometrics is expected to remain high in the near future due to the insinuation of soft modeling methods in other fields. Statistical process control has emerged as a hot application area in this recent review period. Combinatorial chemistry is expected to become the next hot application area. Since the structure and format of data analysis problems encountered in combinatorial chemistry are different than in other areas, new methods and new approaches will be developed. Image analysis may also emerge as another hot application area. Currently, the bulk of the applications in image analysis involve simple principal component analysis. Although three-way analysis offers exciting possibilities for research, practical problems prevent exploitation of this method. The direction of chemometric research is toward more sophisticated methodology, which is a direct result of tackling more difficult data analysis problems. Many of these problems involve the modeling of nonlinear chemical systems. Feature selection, which has been ignored in the past due to the emergence of so-called full-spectrum methods, is generating S0003-2700(98)00008-0 CCC: $15.00 Published on Web 04/30/1998
© 1998 American Chemical Society
renewed interest due to its obvious importance. The use of neural nets and pattern recognition methods as a preprocessing tool in library searching may yet produce a breakthrough. Finally, the interest in validating predictions made by soft modeling methods is evidence of their growing use in industry. During this review period, there was an unexpectedly large number of review articles written on applications of chemometric methods to various areas of chemistry. Brown reviewed the way latent variables can be used to extract information from spectroscopic measurements (A1). Geladi (A2) provided an overview of multivariate calibration methods in spectral analysis. Kowalski (A3) reviewed data preprocessing methods and data transformation methods, as applied to spectroscopic data. Workman (A4) surveyed the use of classic IR tools combined with chemometrics for functional group identification. Geladi (A5) gave a complete overview of chemometrics in near-IR, with emphasis on trends and developments in multivariate calibration. Neural network methods were also reviewed throroughly during the past two years. Mutihac (A6) gave a comprehensive overview on neural network applications in chemistry which covers primarily classification. A similar review on the subject was provided by Pham (A7). Cirovic (A8) reviewed applications of feed-forward neural nets to spectroscopy. Mukesh (A9) reviewed applications of neural nets in process chemistry. Neural nets as classifiers and nonlinear regression modelers were reviewed by Pikington (A10). The problems with using chemometrics for treating classification problems were discussed by Deferenez (A11). Hierlemann (A12) reviewed the different methods of pattern recognition and multicomponent analysis. Gardner (A13) also reviewed pattern recognition, with emphasis on data preprocessing, data display, and artificial neural nets. The three moments of chemometrics, before, during, and after the experiment, were reviewed by Forina (A14). The feasibility of using latent variables to extract information from GC/MS data was reviewed by Brakstad (A15). The past two years saw less activity in the preparation of new books on chemometrics. Nevertheless, there were still some notable additions to the chemometric library. With regard to these books, they are becoming quite sophisticated. It has now become common for authors to bundle software with their texts. This development is significant, because the boundary between books and software, like personal computers and workstations, is becoming less well defined. Geladi and Grahn (A16) authored a text entitled Multivariate Image Analysis, which describes the application of principal component analysis and three-way techniques to problems in magnetic resonance imaging and electron microscopy. Adams (A17) authored a text entitled Chemometrics in Analytical Spectroscopy, which is designed primarily for the novice. Brown (A18), on the other hand, edited a text on advanced cheometric methods Analytical Chemistry, Vol. 70, No. 12, June 15, 1998 209R
in spectroscopy entitled Computer-Assisted Analytical Spectroscopy. This book, which originated from presentations at the fifth Snowbird Conference, contains several chapters on data interpretation methods. Rutledge (A19) edited a text entitled Signal Treatment and Signal Analysis in NMR, which provides broad coverage of data processing methods used in one- and twodimensional NMR. Karjalainen and Karjalainen (A20) authored a text on data analysis techniques for hyphenated instruments. The text is based on a set of working MATLAB programs. The text attempts to fill a central analytical need: obtaining pure spectra from the observed overlapping spectra, with standard deviations for the solutions obtained. Chemometrics in QSAR was the subject of most recent texts. Van de Waterbeemd (A21) edited a book entitled Chemometrics in Molecular Design, which explored the use of multivariate methods, particularly soft modeling and experimental design, in the development of QSARs. He (A22) also edited a book entitled Advanced Computer-Assisted Techniques in Drug Discovery, which explained the integration of QSAR with molecular modeling. Three-dimensional QSAR was treated at great length in the text. Hansch (A23) edited a book entitled Classical and ThreeDimensional QSAR in Agrochemistry, which was developed from a symposium series sponsored by the Division of Agrochemicals at the 208 ACS National Meeting in the fall of 1994. Hansch’s text consists of 24 chapters and covers all facets of QSAR. Doucet and Weber (A24) also wrote a QSAR textbook entitled ComputerAided Molecular Design, which contains several chapters on 3-D QSAR. Devillers (A25) edited a text entitled Neural Networks in QSAR and Drug Design, which explores the extensive approach to QSAR via neural nets. Finally, Livingstone (A26) authored a text entitled Data Analysis for Chemists, Applications to QSAR and Chemical Product Design. Livingstone’s text gives a useful introduction to the field of QSAR. All of the aforementioned published QSAR texts contain at least one chapter on experimental design. In addition, Morgan (A27) has authored a recent text on the subject entitled Chemometrics: Experimental Design, which provides the reader with an introduction to the essential aspects of basic statistics for experimental design. Kyle (A28) has also published a text on experimental design techniques entitled Successful Industrial Experimentation. Texts on chemometrics in environmental chemistry and food science were also published during the recent review period. Einax (A29) edited a text entitled Chemometrics in Environmental Chemistry, Statistical Methods, which covers a broad range of topics including experimental design, soft modeling, and multivariate calibration. Naes and Risvik (A30) edited a text entitled Multivariate Analysis of Data in Sensory Science, which covers soft modeling and experimental design. Clearly, experimental design has been deemed an important topic for good reason. Another important topic is method validation. Riley and Rosanke (A31) edited a text on the subject entitled Development and Validation of Analytical Methods, which focuses on pharmaceutical method development. The importance of improving the quality of methods and products (A32) is elucidated in the text entitled Robustness of Analytical Methods and Pharmaceutical Technological Products. Finally, Quality Assurance in Analytical Chemistry, edited by Funk, Dammann, and Donnevert (A33) focuses on quality assurance in water analysis. 210R
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STATISTICS The focus of this section is on the use of statistics in chemical data analysis. As in previous years, papers that merely incorporate basic statistical methods into chemical analysis or describe research outside the realm of chemistry are excluded. In the analytical chemistry literature, statistics was the subject of research for many reasons such as method validation, sampling, measurement error, interlaboratory comparison, model identification, and process control. A number of reviews and papers on method validation were published during the past two years. Jenke (B1-B3) reviewed the current practices and procedures for chromatographic method validation. Causon (B4) discussed the issues of chromatographic method validation in biomedical analysis. Validation of bioanalytical methods was reviewed by Kayali (B5). The importance of method validation as an essential quality tool in total quality management was discussed by Christensen (B6), who also showed the importance of correcting measurement errors, through use of appropriate reference materials in method validation procedures (B7). Seno (B8) discussed analytical validation in practice at quality control laboratories in the Japanese pharmaceutical industry. Smith (B9) showed that comparing previous data with new data can be an effective tool to augment standard data validation practices. Massart (B10) explained the principle of randomization tests and their possible advantages over the ordinary test statistic in method validation. The feasibility of a collaborative trial for validating a sample protocol was demonstrated by Ramsey (B11). Practical problems and suggested solutions to a variety of statistical and data analysis issues related to analytical method validation were reviewed by Gerlach (B12). Several papers on sampling and measurement error appeared in the chemical literature during this reporting period. Massart (B13) described two statistical tests to assess the representativity of a sample set to its original population. Baiulescu (B14) discussed the relationship between the history of the sample and the development of analytical information. Gy (B15) summarized his theory of sampling, which shows what should be done and what should be avoided to ensure that samples are unbiased and representative. The Analytical Methods Committee (B16) of the Royal Society of Chemistry reviewed the concepts and practices of data quality in analytical chemistry in relation to uncertainty. Brereton (B17) developed a method to visualize the uncertainty in predictions over an experimental domain. Jones (B18) developed a method to estimate confidence limits in nonlinear calibrations using the bootstrap. A few papers regarding the use of statistics for assessing the performance of laboratories in interlaboratory testing appeared in the literature. Szopa (B19) described a new PC program for evaluation of interlaboratory testing results. Duewar (B20) reported the biases responsible for differences in audioradiographic DNA profiling measurements between laboratories. Schantz (B21) noted that testing laboratories generally perform well in interlaboratory comparisons when reference materials are used to validate their analytical procedures. A review of the statistical methods used to analyze data from interlaboratory comparisons was authored by Feinberg (B22). Multivariate statistical methods can be powerful tools to compare the performance of different analytical methods using appropriate reference
materials as evidenced by Braun’s recent report (B23). A substantial increase in the number of literature references describing the use of statistical methods in process control occurred during this review period. The field of process chemometrics was reviewed by Wise (B24) and Kowalski (B25). The use of neural networks, nonlinear biased regression, and genetic algorithms for dynamic model identification in process chemometrics was reviewed by Wise (B26) and Zufiria (B27). Projection methods such as partial least squares, principal component regression, and principal component analysis are becoming widely used in statistical process control. Albert (B28) reported on the use of these methods in batch-process monitoring. A problem that often occurs when these methods are used in batchprocessing monitoring is the estimation of scores for observations with missing measurements. MacGregor (B29) studied several methods for estimating scores from data with missing measurements and derived expressions for the error in the scores calculated by each of these methods. Practical issues regarding the implementation of partial least squares (PLS) in batch process monitoring were also considered by Lakshminarayanan (B30). MacGregor (B31) addressed some of theses issues through the development of a recursive exponentially weighted partial leastsquares algorithm. Walczak (B32) attempted to address these same issues by developing an algorithm that integrates partial least squares with radial basis functions. Cinar (B33, B34) also integrated principal component analysis and discriminant analysis to determine out of control states for a continuous process. The advantages of a pattern recognition approach to fault identification was discussed by Bakshi (B35). Multiway principal component analysis and partial least squares were used for fault detection in batch monitoring by both MacGregor (B36, B37) and Wise (B38). Nonlinear time series modeling of processes using neural networks was a popular topic during this reporting period. This area was reviewed by Bakshi (B39). Cinar (B40) developed a unified nonlinear model for time series modeling using a neural net. However, there remain many issues to be addressed regarding nonlinear system identification with neural networks. The use of experimental design techniques (B41) and cross validation for selection of the appropriate neural network model has been discussed by Schenker (B42). Some examples of neural network modeling for statistical process control include the quality monitoring of an injection molding process (B43) and the identification of dangerous states in a plant (B44). Neural network controllers have also been used in distillation plants (B45) and for identification and control of an anaerobic digester (B46). Fault detection (B47) at a plant and fault diagnosis for amylase production (B48) via neural nets were also reported in the literature. McAvoy (B49) integrated nonlinear finite impulse response functions and partial least squares using a neural net. The self-consistency problem in nonlinear principal component analysis implemented via a neural net was addressed by RicoMartinez (B50). Plummer (B51) reported on the use of Kohonen neural nets in process monitoring. Other miscellaneous statistical topics discussed during this reporting period include robust nonlinear regression (B52) and the application of extreme value theory (B53) to data analysis in chemistry. Jordan (B54) discussed criteria for selecting simple statistical tests for chemical data. De Jong (B55) proposed the
use of a jackknife estimator for outlier detection. Spiegelman (B56) described an alternative to PLS for calibration based on standard and saddle point approximations. Net analyte signal computation was performed for the inverse calibration model by Lorber (B57). Rudnyi (B58) proposed a statistical model for describing systematic error in instrument calibration. Estimating sample variances and local environmental heterogeneity for both known and estimated analytical variances was discussed by Clark (B59) for sparse data sets. OPTIMIZATION Optimization of chemical systems using numerical methods has become widespread in the chemical sciences. For the chemist, the goal of using such methods may include the adjustment of system parameters for optimal method performance, wavelength selection for calibration or pattern recognition, or chemical structure estimation. Many of the studies published during this period employed some sort of mathematical optimization, but most of these involved rather obvious applications of wellestablished techniques. For this reason, only new methods and applications are reviewed in this section. A significant number of studies involving genetic algorithms (GAs) in the optimization of analytical experiments were reported during the last two years. Salomon (C1) showed that GAs with a small mutation rate may suffer a performance loss when the coordinate system of the data is rotated. Buydens (C2) attempted to assess the importance of crossover in genetic-based classifiers and came to the conclusion that recombination does not make a significant contribution to classification. Interestingly enough, Buydens (C3) also believes that a simple lethalization scheme can yield results comparable to penalty functions. GAs as a tool for wavelength selection in multivariate calibration have been investigated by a number of groups (C4-C6). GAs as a method of feature selection for multiple linear regression analysis and PLS can be quite effective when the correct object function is used (C7). GA-based protocols for optimization of nonparametric linear discriminant functions (C8) or for coupling of digital filters with PLS (C9) were developed by Small. GAs can also be used to develop an asymmetric d-optimal design (C10), which can be difficult to construct using standard techniques. Simulated annealing (SA), a numerical optimization technique also based on natural processes, has been investigated by Kalivas (C11-C13) as a tool for wavelength selection in multivariate calibration. Kalivas concluded from his studies that SA’s should be used to reveal important regions in the spectrum as opposed to identifying specific wavelengths. During this reporting period, neural networks were used to optimize a variety of analytical functions. A neural net was employed to perform response surface modeling using a new chromatographic response function developed to modulate both quality and separation time in chiral separations (C14). Response surface modeling in HPLC using feed-forward neural nets was compared to the more traditional nonlinear regression methods (C15). Overfitting was controlled using cross validation, and the number of hidden nodes was optimized using a literal inhibition method. Experimental design techniques, i.e., mixed orthogonal array designs, were also used to optimize separations in HPLC (C16) and select sample subsets for multivariate calibration (C17). Analytical Chemistry, Vol. 70, No. 12, June 15, 1998
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SIGNAL PROCESSING This section focuses on research in signal processing, that is, with the development or use of methods for enhancing analytical measurements to obtain chemically or physically relevant information. In the course of the two years covered by this review, work in this area has mainly involved techniques such as wavelet packets or maximum entropy techniques for peak deconvolution and digital filtering methods for signal enhancement and background correction. Several reviews of signal processing methods were published during this reporting period. Cross (D1) provides an overview of digital filtering techniques in NMR. Gilbert (D2) reviews data preprocessing techniques for baseline correction, spectral data smoothing, and signal-to-noise retio enhancement in spectroscopy. Hercules (D3) provide a review of deconvolution via nonlinear least squares in surface science. Remond (D4) gives an overview of deconvolution in X-ray emission spectroscopy. Goubran (D5) compares the performance of several conventional digital signal processing techniques for peak detection in ion mobility spectrometry. Riris (D6) compares the performance of Wiener, Kalman, and matched filtering for enhancing the sensitivity and accuracy of near- and mid-IR data. Madden (D7) compares six deconvolution techniques using simulated data. Himmelsbach (D8) extols the virtues of maximum likelihood methods for correlation spectroscopy with multiple regions. Rao (D9) discusses recent progress in fiber-optic low-coherence interferometry. Stone (D10) champions the median filter for noisy data, and Packham (D11) recommends the use of the fast Hartley transform for deconvolution of analytical peaks. A substantial increase in the number of references in the Chemical Abstracts database on wavelets occurred during this reporting period. Bakshi (D12) reviewed data compression and feature extraction with wavelets. Kimble (D13) compared wavelets to the fast fourier transform for dynamic data analysis. Wavelet denoising of Gaussian peaks was studied by Mittermayr (D14). Wolkenstein (D15) compared wavelet filtering with established techniques for denoising of EPMA images. Kosanovich (D16) examined a new family of wavelets derived from the Poisson distribution for analyzing time-dependent signals. Chen (D17) considered a new family of wavelets for construction of orthogonal interval wavelets. Massart (D18, D19) used wavelets to extract the relevant components from near-IR data for multivariate calibration and pattern recognition. Liu (D20) compressed IR spectra using wavelet neural networks, and Neue (D21) compressed NMR spectra using the wavelet transform. Both the fast wavelet transform (D22) and the B-spline wavelet transform (D23) were applied to the compression of UV/visible spectra. Wavelets were also applied to analysis of mass spectrometry signals for transient event detection (D24). The wavelet transform was applied to accoustic emission spectra (D25, D26), voltammetry (D27), and analysis of DNA sequences (D28, D29). The Fourier transform continued to find favor with a number of workers as a tool for signal processing and peak deconvolution. Mathies (D30) used a low-pass Fourier filter to detect DNA separated by capillary gel electrophoresis equipped with a single molecular photon burst counter. Fourier filtering (D31) was also used to filter signals in potentiometric stripping analysis experiments. Fourier self-deconvolution techniques were used to resolve overlapping IR bands (D32, D33). Smeller (D34) described a 212R
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method to minimize artifacts which can arise in Fourier deconvolution. Although Fourier filtering and deconvolution can yield truly impressive results, they cannot remedy the problem of a varying background due to differences in optical path length or light source intensity. Parnell (D35) used multiplicative signal correction to account for a varying optical path length prior to filtering. Hoffmann (D36) used applied Fourier analysis to perform background correction in atomic emission spectroscopy. Principal components (D37) were used to model scene clutter in thermal IR spectroscopy prior to digital filtering. Kalman filtering, with nonnegativity constraints, was used to model signal and background (D38). There were a number of papers published on maximum entropy. Cooke (D39) and Dowsett (D40) applied maximum entropy deconvolution to SIMS data from depth profiling experiments. Zhang (D41) used maximum entropy deconvolution to enhance resolution in mass spectrometry of biomolecules. STM images were deconvoluted using entropy as a regularization function (D42). Maximum entropy also found use in ion beam applications (D43). Schmieder (D44) reported that advantages of maximum entropy in spectral reconstructions stem from its nonlinearity. Kalceff (D45) compared maximum entropy to direct, indirect, constrained, and unconstrained deconvolution, using X-ray profiling data. Maximum entropy was also used to analyze scattering data (D46) and deconvolute XPS spectra (D47). Two application areas highlighted in the recent review period were chromatography and image analysis. Hatrik (D48) reported on the use of general exponential functions to deconvolute fused chromatographic peaks. The fast Fourier and Hartley transforms were used to resolve exponentially modified Gaussian peaks in HPLC (D49). Artificial neural networks were also employed to deconvolute overlapping peaks and work very well with high accuracy and less computing time compared to other methods (D50). Torres-Lapasio (D51) developed a model for the description of skewed chromatographic peaks which he used to deconvolute overlapping bands in binary and ternary mixtures. Barbee (D52) deconvoluted peaks of human plasma lipoproteins in gel permeation chromatograms based on a matrix of spreading functions. For multivariate image analysis, a variety of filtering techniques have been used. PCA was utilized as part of an extended strategy to explore multivariate images and reducing their noise (D53). Fourier filtering of high-resolution TEM images can reduce noise significantly (D54). Hadjiiski (D55) developed a neural network to correct for nonlinearities in scanning probe microscope images. Deconvolution of one-dimensional microprobe scans using the nonlinear Jansson method was reported by Coote (D56). Reconstructions of FT-IR spectra in fire gas analysis using PCA can result in significant reductions in background according to Tetteh (D57). IMAGEO (D58), a software system for noise filtering and deconvolution, was developed for processing noisy images RESOLUTION This section is concerned with the mathematical resolution of mixtures. A mathematical resolution of mixtures is usually performed in far less time than a physical or chemical separation. Thus, mathematical analysis of data obtained on mixtures is faster than an alternative separation and is also more accurate and more
precise. The mathematical resolution of mixtures is usually achieved with principal component analysis-based methods. During this reporting period, some new methods to resolve multicomponent systems appeared in the literature. All of these methods required the user to determine by PCA the number of abstract factors in the data. The problem of selecting the number of abstract factors in the data is a daunting task. Faber (E1) evaluated two F-tests, Malinowski and Sindreu’s, for selecting the number of significant factors in abstract factor analysis. Pavkovic (E2) proposed a new statistic based on eigenvalues to assess the significance of individual components in PCA. Shen (E3) noted that spectral background and chromatographic drift, which prevents an accurate determination of the number of significant principal components in curve resolution, can be eliminated using orthogonal wavelets. Wang (E4) proposes using morphological analysis to estimate the local chemical rank of two-way data confounded by heteroscedastic noise. Amrhein (E5) addressed the causes and possible cures for rank deficiency in factor analysis. Some new methods published on resolving overlapping peaks in HPLC diode array data sets include the needle algorithm (E6), which involves using a uniqueness test in target factor analysis to explore diode array data sets. Alternating least squares (E7E9) with its initial estimates obtained from SIMPLISMA or evolving factor analysis was also used to resolve overlapping chromatographic bands. Malinowski (E10) reported a more efficient method for determining concentration profiles from the spectroscopically active components in HPLC diode array data. The method, which is called automatic window factor analysis, can reveal components not detected by precursor techniques. Ritter (E11) approached the same problem by correcting the heteroscedasticity in window evolving factor analysis. Brereton (E12) approached the problem of noise in evolving window factor analysis through judicious wavelength selection using a double window and previously described resolvability indexes. Window factor analysis was also used by Shao (E13) to resolve overlapping chromatographic bands of rare earth elements. Wavelets were employed as a preprocessing step to compress the data and filter noise out of it. Methods to resolve and recover pure component spectra from overlapped spectra of mixtures were applied to a variety of problems during this reporting period. Massart (E14) used evolutionary factor analysis to determine the reaction products, which were being monitored by an HPLC/FT-IR instrument. PCA and iterative target testing factor analysis (E15) were used to inspect the chromatograms from a capillary electrophoresis/diode array instrument for peak purity. PCA was also used to explore and resolve coeluting substances in GC/MS (E16). Factor analysis was used to determine paracetamol, aspirin, and caffeine in an analgesic tablet from UV spectra without prior separation of these components (E17). Evolving factor analysis of fluorescence spectra of fulvic acids during the course of a pH titration revealed information about their acid/baseproperties (E18). Factor analysis has also been used to reveal information about the multiple components in NMR spectra (E19, E20) and, in voltammetric curves, about the nature of the complexes formed between metal ions and biological/inorganic ligands (E21-E24). Complexation of metal ions has also been studied by evolving factor analysis of synchronous fluorescence spectra (E25, E26)
or window factor analysis of visible spectra (E27, E28). Rank mapping of three-way multicomponent profiles was reported by Kvalheim (E29) and Liwo (E30) during this reporting period. MULTIVARIATE CALIBRATION Multivariate calibration refers to the process of relating the analyte concentration or the measured value of a physical or chemical property to a measured response, e.g., near-IR spectra of multicomponent mixtures or gas chromatographic profiles of complex biological samples. It remains, by far, the fastest growing area of chemometrics, as evidenced by the tremendous number of papers that have appeared in the last two years on partial least squares. PLS has come to dominate the practice of multivariate calibration, because of the quality of the calibration models produced and the ease of their implementation due to the availability of PLS software. The latent variables in PLS are developed simultaneously along with the calibration model, so that each latent variable is a linear combination of the original measurement variables rotated to ensure maximum correlation with the information provided by the property variable. Hence, confounding of the desired signal by interferants is usually less of a problem in PLS, since PLS utilizes both the response and measurement variables to iteratively determine the latent variables in the data. The problem of validating predictions in PLS is an active area of research in chemometrics. Faber (F1) derived an expression for estimating the uncertainty in the predicted values that also accounts for the error in the independent variables using propagation of error. Kleinknecht (F2) developed an approach to error calculations. The prediction error in PLS as computed by UNSCRAMBLER was investigated for accuracy by De Vries (F3) and Kowalski (F4). Faber (F5) reported on the contribution of the measurement error in the reference value of each standard to the overall prediction error and proposed a simple correlation procedure that yields a more realistic estimate of the true prediction error. The prediction error in PLS can be minimized through judicious wavelength selection. Schechter (F6) developed a theory to model the analytical uncertainty in multicomponent analysis and show the conditions where wavelength selection is absolutely essential. An error indicator function was developed to predict the analytical performance in a certain spectral range. Cooper (F7) reported that overfitting of the data is avoided and the reliability of the subsequent predictions improved by using selected spectral regions. Massart (F8) described a new method to eliminate uninformative variables from multivariate calibration data sets. The method involves adding artificial noise to the closed data and identifying uninformative variables by inspecting the b vector. Massart also investigated genetic algorithms for wavelength selection in PLS (F9). Lindgren (F10) used permutation tests to investigate four different variable-selection methods: GOLPE, MUSEUM, VIP, and IVS-PLS. Wold (F11) reviewed the issue of variable selection in PLS. He proposed dividing the variables into conceptually meaningful blocks and applying hierarchical multiblock PLS or PC models. The effect of noise on interactive variable-selection procedures was investigated by Lindgren (F12). Research into the structure of PLS for the purpose of improving its performance continues to be pursued by a number of investigaAnalytical Chemistry, Vol. 70, No. 12, June 15, 1998
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tors. Davies (F13) described the relationship between multiple linear regression, principal component regression, and PLS. Vigneau (F14) compared ridge regression to principal component regression and partial least squares. Massart (F15) developed an alternative form of model validation based on the simulation of instrumental perturbations on a subset of calibration standards. PLS and principal component regression were compared using diode array data sets (F16). Mean centering and selection of the chromatographic region for calibration were found to be especially advantageous. Littlejohn (F17) described some improvements made to partial least squares for overcoming the problem of spectral interference and reducing the computational time needed to determine the optimum number of latent factors. Cummins (F18) reported the development of a robust PLS algorithm that is relatively insensitive to outliers. Nonlinear relationships can be difficult to model using PLS. Workman (F19) reported that the lack of fit due to nonlinearities contributes more to the overall modeling error than experimental error for the calibration sample set. Massart (F20) suggested that transforming the original variables prior to PLS is a reasonable approach to correct nonlinearities in spectroscopic multivariate calibration data. Wold (F21) suggested that addition of squared x variables to the x block is a simple way to develop suitable nonlinear PLS models. Malthouse (F22) proposed a new nonlinear projection algorithm based on the PLS and projection pursuit paradigm, which is implemented using feed-forward neural nets. Frisvad (F23) described a new technique called correspondence analysis-partial least squares for nonlinear calibration data. Walczak (F24) proposed a new approach founded on radial basis functions and PLS to model nonlinear chemical systems. Booksh (F25) proposed to mitigate the nonlinearity problem by matching the model employed in the calibration method with the instrumental response function. This should parse the decision- making process involved when sorting through the large array of calibration methods available for first- and second-order data. Lorber (F26) noted the inherent advantages of local centering, when addressing the problem of nonlinearity with PLS. Locally linear PLS models were also investigated by Janik (F27). Another approach to modeling nonlinear relationships is neural nets. During this reporting period, a large number of papers appeared in the literature on applications of neural nets to problems in multivariate calibration. Only a few dealt with novel approaches to calibration. Broten (F28) and Shao (F29) developed an approach to estimate confidence limits for property values modeled by neural nets. Tetko (F30) and Smith (F31) investigated the issue of overfitting and overtraining using literature data. They concluded that overfitting does not have any affect on prediction when overtraining is avoided by cross validation. Zhang (F32) proposed a hidden node-pruning algorithm as a method of configuration optimization and training in feed-forward network. Faber (F33) commented on the results from recent sensitivity analysis studies on radial basis and feed-forward neural nets. He concluded that feed-forward neural nets are not superior to radial basis neural nets for modeling. Wang (F34) developed a robust back-propagation algorithm that is insensitive to outliers, by introducing some kind of transform on the residual term. Walczak (F35) also developed a robust back-propagation algorithm. Bro 214R
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(F36) used a neural net to implement PLS, with the attendant advantages of interpretability and suitability for nonlinear problems. Goodacre (F37) demonstrated that drift in mass spectral data can be corrected by using an appropriately trained neural net. Applications of neural nets to calibration problems are too numerous to be cited in their entirety. Nevertheless, some of the more novel applications are listed. The use of neural nets in solving interference problems caused by the formation of intermetallic compounds in anodic stripping (F38-F40) was investigated by several research groups. Neural nets have also been used to calibrate a nonselective chalcogenide glass sensor array for multicomponent analysis of heavy metal cations and inorganic anions (F41). Schweizer-Berberich (F42) used neural nets to calibrate a polymer-based sensor array. An investigation was carried out on the ability of artificial neural networks to identify and quantify individual concentrations of mixtures of gases in variable humidity environments using tin oxide thin-film sensors (F43). Tobias (F44) described a neural network suitable for the evaluation of signals from chemical sensors. Gotshal (F45) reported that evanescent wave spectroscopy with a silver halide fiber as the sensing element could quantitate three blood components using neural nets to develop the calibration curve. An artificial neural net was used to extend the response range of an optical fiber pH sensor (F46). Hayes (F47) used a feed-forward neural net to predict NOx emissions on-line from a gas-fired utility boiler. The combination of pyrolysis mass spectrometry and artificial neural nets was used to quantify levels of penicillins in fermentation broth (F48). During the past two years, other calibration methods were reported in the literature as well. Schechter (F49) proposed a factor analysis method for calibration, based on composing a subspace excluding the contributions of the component of interest and calculating its net analyte signal through an orthogonal projector to an orthogonal space. Wentzell (F50) developed an approach to calibration that allows the user to incorporate information about measurement uncertainties in the modeling process. Vigneau (F51) explored the possibility of using latent root regression to calibrate near-infrared spectra. Liang (F52) described a robust calibration method based on least median of squares and sequential number theory optimization. A genetic algorithm and a simplex optimization routine were used to develop a nonlinear calibration for a sensor array (F53). Genetic regression, a technique that uses a genetic algorithm to select the optimum wavelengths for multiple linear regression, was studied by Paradkar (F54). Huber-type, Hampel-type, and Andrews-type M estimators were utilized to develop robust regression methods (F55). A B-spline zero compression method was used by Olsson (F56) to compress spectra for PLS modeling. Least-terminated squares was used to calibrate piezoelectric immunosensor array data by Chu (F57). Johnston (F58) reported that locally weighted parametric regression can yield calibration models that are robust to random error. Achievement of a satisfactory calibration model is usually not the final step in practical applications of PLS or neural nets. Once a calibration model is developed, it must be transferred to other instruments, so the calibration can be used at the point of application. One way to achieve calibration transfer is a method
called piecewise direct standardization. This method was applied to a number of calibration problems during this reporting period with vary degrees of success (F59-F66). Recently, Brown (F67) has developed a method for transferring near-IR multivariate calibration models to other instruments without the use of standards. Brown’s method is an important development in the field of multivariate calibration. Goodacre (F68) reported that an artificial neural network-based drift correction procedure has been developed which makes it possible to transfer a calibration model from one instrument to another. Wentzell (F69) investigated the possibility of using maximum likelihood principal component analysis to develop calibration models suitable for transfer from one instrument to the other. Gemperline (F70) developed a calibration transfer function for instruments utilizing CCD detectors and multichannel fiber-optic probes. Calibration to properties continues to be an active area of investigation, too. Erskine (F71) used PLS to determine enantiomeric purity from chirally sensitive spectral measurements. Principal component regression was used to correlate cotton fiber strength to its visible/near-IR reflectance spectrum (F72). Antti (F73) used PLS to correlate the properties of the pulp with the quality of the finished paper product. The coating surface properties of paper were predicted from IR spectra using PLS (F74). Octane number and Reid vapor pressure in commercial gasoline were determined using fiber-optic Raman spectroscopy and PLS (F75, F76). De Bakker (F77) developed a PLS model to determine the octane number of unformatted gasoline, using fiber-optic Fourier transform Raman spectral data. Firmstone (F78) compared PLS to neural nets to predict the engine test ratings observed in gasoline engines from specific base oil compositional features. Lindblom (F79) determined the concentration of a stabalizer in a single-based propellant by PLS analysis of FT-IR spectra. Headspace gas chromatography and artificial neural networks were used to predict milk shelf life (F80). Use of PLS regression in the pharmaceutical industry has become commonplace. Bautista (F81, F82) and Bouhsain (F83) determined acetylsalicyclic acid, acetaminophen, and caffeine in pharmaceutical preparations from spectrophotometric data using PLS. DRIFTS and PLS were used to develop a new method to determine the active compound and major excipients in a pharmaceutical formulation (F84). Blanco (F85) used PLS to develop a spectrophotometric method for the simultaneous determination of the active principle and a flavoring agent in cough syrups. PLS and UV/visible absorbance spectrophotometry were also used by Blanco (F86) to determine ketoprofen and paraben in a gel preparation. Near-infrared and PLS were used to develop an indestructible method to compute tablet hardness (F87). Moisture in hard gelatin capsules was also determined by nearinfrared spectroscopy and PLS (F88). Drennen (F89) used nearIR and PLS to study transdermal drug delivery by controlling the amount of reflected light reaching the detector using a combination of diaphragms with different diameter apertures. Multivariate calibration is fast becoming an integral part of biological analyses. Blood glucose by PLS and near-infrared was a popular topic during the past two years (F90-F100). However, other analytes were also measured in physiological fluids using PLS. Oxygen saturation of myoglobin was studied by DRIFTS (F101), calcium and magnesium ions in plasma by UV/visible
absorbance spectrophotometry (F102), and aspirin (F103) and codeine (F104) in urine by fluorescence. There were also a large number of citations on multivariate calibration in environmental analysis. Pesticide levels were measured in wastewater using polarography and PLS (F105). Triazines were measured in surface waters by membrane separation coupled on-line to a flow injection analysis system and PLS (F106). Phenol, o-cresol, m-cresol, and p-cresol in natural waters and soil samples were determined by fluorescence excitation emission spectra and PLS (F107). Andrew (F108) determined the concentration of BTEX compounds in model effluent systems using flow injection diode array spectrophotometry and multivariate calibration. A significant number of references focused on multivariate calibration in the food industry. Delwiche (F109) described the development of a near-IR reflectance method using PLS to assess the cooking quality of rice. A genetic algorithm was applied to optimizing sets of aroma components in black teas for calculating multiple linear regression models to predict sensory scores (F110-F112). Li (F113) used near-IR and PLS to determine the concentration of glucose, fructose, sucrose, and citric and malic acids in orange juice. Similar analyses for sugars in orange juice and other fruit juices were performed by Rambla (F114), Norgaard (F115), and Li (F116). Near-infrared spectroscopy and PLS were used to determine the concentration of ethanol, glycerol, fructose, glucose, and other sugars in botrytized grape wines (F117). A Fourier transform infrared method for the rapid determination of casein in raw milk was developed using PLS to process the FT-IR data (F118). Henriksen (F119) showed that partial least-squares analysis of amino acid and sensory data from dried sausages suggests that bouillon taste is related to a mixture of different amino acids and peptides. Near-IR and PLS were also used to develop a method to determine the water, fat, and crude protein content of sausage (F120). Espinosa-Mansilla (F121) used PLS analysis and UV/visible absorbance spectroscopy to determine the concentration of synthetic food antioxidants in multicomponent mixtures. Zhang (F122) described the development of a nearinfrared spectrophotometric method to determine the concentration of free fatty acids in fish. PLS was used to develop a calibration model using the first-derivative spectra of the fish oil samples. Mehrubeoglu (F123) used near-IR to predict the total reducing sugar content of sliced potato samples. PLS again was used to develop the prediction model. PARAMETER ESTIMATION Most of the papers referenced in this section are concerned with the mathematical modeling of chemical or physical properties. The number of papers published during the past two years on modeling has increased from the previous review period, indicating the widespread use of sophisticated data analysis methods in all fields of chemical research. There were very few novel approaches for parameter estimation published during this reporting period. The bulk of the papers reported only applications. Five application areas are highlighted in this section: materials, surface science, physicochemical parameters, kinetics, and pharmaceutics. Zhang (G1) used stack neural networks with a bootstrap technique to estimate polymer quality. Liu (G2) demonstrated the advantage of using PLS scores as inputs for a back-propagation Analytical Chemistry, Vol. 70, No. 12, June 15, 1998
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neural net to design hydrogen storage battery materials. An artificial neural net was used to predict the glass-forming region of the Sm-Si-Al-O-N system (G3). A neural network was used to predict the property of a sealing alloy (G4). Both PCA and cluster analysis were used in Auger studies of fiber/matrix interactions in alumina (G5). Both PCA and neural nets were used to study depth-profiling data from Auger experiments on thin films (G6). Target factor analysis was applied to Auger depth profile data to determine the interface region of a thin-layer Si/ Mo sample (G7). PCA was used to study the data from an argon ion sputtering experiment for the purpose of identifying relevant chemical species from the bombardment of RexSi1-x thin films (G8). Factor analysis was also used to study chemical bonding in tungsten carbide and chromium films by application of PCA to AES depth profile data (G9). Neural nets were applied to a wide variety of problems in surface science, e.g., depth profiling models of ion and plasma etching on a variety of substrates (G10-G15), reconstruction of spectra from thermal and Auger depth profiling experiments (G16, G17), design of automobile exhaust catalysts (G18), and performance estimates of SnO2 catalysts (G19). In chemical analysis, neural nets were used to determine the selectivity coefficients of berberine (G20) and medicine electrodes (G21). A genetic algorithm was developed to determine stability constants from calorimetric and polarographic data obtained from literature sources (G22). Multiple linear regression analysis was used to develop a structure-property relationship for molar absorptivity of colored reagents (G23). During this review period, neural nets were used to estimate physical parameters, e.g., vapor pressure of stabalized gasoline from an historic database (G24) and the superconducting transition temperature of composite materials (G25). The phase behavior of microemulsion systems was predicted, using a backpropagation feed-forward neural network model of previously published phase diagrams (G26). Compositional maps of phaseseparated protein/polysaccharide mixed gels were formulated by PLS (G27). Modeling of VOC adsorption on graphite by neural nets was reported by Basheer (G28). Optimization of HPLC mobile-phase parameters using back-propagation was discussed by Gobburu (G29). Brown (G30) demonstrated that neural nets provide an efficient, general interpolation method for nonlinear functions of several variables. He used a feed-forward neural net to model global properties of potential energy surfaces from information available at a limited number of configurations. Neural nets have also been applied to problems in the modeling of kinetic data, i.e., estimation of rate constants from compositional data (G31-G38). Neural nets in pharmaceutics involved a diverse set of applications, e.g., estimation of cellulose binding constants (G39), kinetic parameters (G40, G41), and drug release rates from tablets (G42, G43). STRUCTURE-ACTIVITY RELATIONSHIP STUDIES In this section, the use of multivariate methods to build linear or nonlinear models that relate chemical structure to a physical, chemical, or biologically measurable property is reviewed. Most studies in this area focus on representing the structure of a compound by a set of molecular descriptors and applying soft modeling methods to discover the relationship between structure 216R
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and activity. Hence, it should come as no surprise that research in descriptor development was an active area during this reporting period. Electrotopological state indexes in QSAR analysis were studied by Buolamwini (H1). The issue of diversity in chemical databases was addressed by Cummins (H2), who developed a molecular descriptor space to describe structural diversity. Goodford (H3) developed a force field method to characterize molecules. The approach uses specific chemical probes, each of which simulates one explicit chemical group, e.g., proton or a methyl group. The results are a set of three-dimensional matrices, each defining the interactions of the chemical group with one target molecule. These results can be used as the X matrix for the computation of QSARs. Zupan (H4) describes a new approach to represent chemical structure, based on the projections of atoms on a sphere of arbitrary radius. During this reporting period, the most active research area in structure-activity modeling concerned the relation of chemical structure with biological activity. Several reviews on the role of chemometric methods in molecular design have been published (H5-H8), including tutorials on experimental design (H9) and genetic algorithms (H10-H13). Karplus (H14, H15) demonstrated the unique advantages of genetic algorithms for developing a QSAR. The use of cluster analysis to select substituents for the original compound of a chemical series in order to achieve a higher biological activity was proposed by Moreno (H16). Wienke (H17) proposed the use of adaptive resonance theory-based neural nets for nonlinear modeling in drug design. Many studies in QSAR published during this review period utilized statistical design technique and PLS or discriminant analysis to fasten the required multivariate relationships (H18-H20). Although drug design was the goal for the bulk of these published studies, biodegradability and toxicology were also investigated using QSAR techniques. Klopman (H21) used MULTICASE, a program that can identify molecular substructures believed to cause or inhibit biodegradability to assess the danger that industrial organic waste poses to our ecosystem. Loonen (H22) investigated the issue of biodegradability via QSAR using a more traditional approach. QSAR in toxicology was reported by a number of workers. Polar narcosis was studied by Urrestarazu-Ramos (H23). Toxicity in the fathead minnow was investigated by Kaiser using backpropagation neural nets (H24). Prediction of acute mammalian toxicity for a diverse set of substituted anilines was undertaken by Jurs (H25); the potential of organic compounds to cause eye irritation was assessed by Barratt (H26) and Cronin (H27); and genetic toxicology was reviewed by Benigni (H28). During this reporting period, three-dimensional QSAR procedures emerged as the method of choice in computer-assisted molecular design. Comparative molecular field analysis (COMFA) was employed in a number of studies with truly impressive results. Carrigan (H29) used this approach to better understand the relation between structure and biological activity for camptothecin analogues. COMFA was also used to study ligand receptor binding (H30, H31), the affinity of anathraquinones for cellulose fibers (H32), enthalpies of sublimation for PAHs (H33), the activity of dihydrofolate reductase inhibitors (H34), and the inhibition activity of protein-tyrosine kinase inhibitors (H35). The use of nonlinear PLS for COMFA was validated by Kim (H36). The modeling of molecular surfaces for use as a template in the
analysis of shape of various other molecules via self-organizing neural nets was proposed by Gasteiger (H37). Structure-property modeling was also popular during the most recent reporting period. Experimental design techniques and PLS were used to study the fastness properties of a series of thiadiazolyl azo dyes (H38). Descriptors based on the overall three-dimensional geometry of the dyes were used to develop the property model. The detergency effect of a series of technical nonionic surfactants was examined using response surface modeling via PLS (H39, H40). Neural nets were used to model the relationship between the Cartesian coordinates of the modeled structures of disazo dyes and the affinity of the dyes for cellulose (H41). Binding properties of crown ethers to metal ions were predicted using neural nets (H42). Estimation of the aqueous solubility of drug molecules was performed using neural network models based on topological and electrostatic descriptors (H43). Interestingly enough, a similar approach was used to predict other physical properties of organic compounds: (1) aqueous solubility (H44), (2) solubility in supercritical carbon dioxide (H45), (3) solubility of alkanes in hydrofluorocarbons (H46), (4) vapor pressure of hydrocarbons and halogenated hydrocarbons (H47), (5) boiling point and critical temperature (H48), and (6) reduced ion mobility constants (H49). There were only a few published reports on structure retention relationships, which should come as a surprise since it was reported to be the most active area in structure-activity modeling in the 1996 review. During the most recent reporting period, multivariate characterization continues to play an important role in characterizing mobile and stationary phases in HPLC. PCA was used to characterize similarities and dissimilarities among reversed-phase materials, using the capacity factors of test solutes to characterize the stationary-phase materials tested (H50-H52), including chiral stationary phases (H53). Forgacs (H54, H55) employed the standard deviations of the capacity factors to develop the principal component maps of the HPLC stationary phases. The retention behavior of small peptides in several RPLC-HPLC columns was correlated to peptide structure using simple molecular descriptors (H56). A large number of researchers have attempted to develop models that can predict some type of spectroscopic response from chemical structure and vice versa. The literature in this area was recently reviewed by Luinge (H57) and Gasteiger (H58); the focus of much of this work continues to be in the field of 13C NMR spectral simulation. Small (H59) and Jurs (H60-H63) continue to develop, refine, and apply an automated spectrum simulation method for 13C NMR. They tested this approach using data sets of monosaccharides, ribonuleosides, trisaccharides, and dibenzofurans. Svozil (H64) used neural nets to predict chemical shifts of alkanes; Ivanciuc (H65, H66) predicted the shifts of sp3 carbon atoms in the R position relative to the double bond in acyclic alkenes and sp2 carbons in acyclic alkenes using so-called environmental descriptors and neural nets. Isu (H67) has developed a neural network simulator to predict 13C shifts of PAHs. Principal component regression has been used to predict 13C chemical shifts in naphthyl derivatives (H68). The aforementioned 13C chemical shift simulators used primarily topological descriptors to predict chemical shifts. Finding the 3-D structure from an infrared spectrum was also a problem of interest during
the recent reporting period. Gasteiger (H69) briefly reviewed this field and also reported (H70) on a method to identify chemical compounds from their IR spectra based on a novel 3-D structural representation and a counterpropagation neural net. Functional group predictions were pursued by a number of groups (H71H74) using a variety of methods, e.g., back-propagation neural nets, Hopfield nets, hierarchical clustering, Kohonen self-organizing maps, etc. PATTERN RECOGNITION The overall goal of pattern recognition is classification. Developing a classifier from spectral or chromatographic data may be desirable for any number of reasons including source identification, detection of odorants, presence or absence of disease in an animal or person from which the sample was taken, and food quality testing. During the past two years, some new classification methods were reported in the literature. Many of these methods utilized neural nets. Both Jiang (I1) and McAvoy (I2) reported the development of a neural network algorithm for nonlinear principal component analysis. McAvoy’s approach integrated principal curves and neural networks to yield an algorithm that produced scores and loadings. Chen (I3) developed a procedure called correlative component analysis to identify the classification characteristics of the data used as inputs for a pattern classifier neural net. Sagrario (I4) utilized a genetic algorithm to train a neural network to classify data based on direct optimization of frequencies of both misclassifications and the number of correct classifications. Cleij (I5) developed a linear projection method based on a feed-forward neural net to obtain a map of the data that maximizes the separation between the predefined classes in the data set. Salenieks (I6) employed polytopic vector analysis to develop a nonorthogonal basis set which can be used as feature vectors for pattern recognition analysis of so-called dynamic data. Jiang (I7) extended feature extraction to nonlinear data sets using a neural network. The Kalman filter (I8) was employed as a feature extractor and classifier for near-IR data. Most of the literature on pattern recognition during this reporting period focused on novel and not so novel applications. Hence, the bulk of the references in this section are organized according to type of application, namely, applications to chromatography, spectroscopy, sensors, food, archaeological and forensic chemistry, environmental chemistry, textiles, polymers, and plastics, to name a few. The majority of these studies involve the use of one or several techniques that are now fairly well established, e.g., PCA, K-NN, SIMCA, and statistical discriminant analysis. Classification of data remains an important subject, as evidenced by the fact that pattern recognition had the largest number of citations in the Chemical Abstracts database during this reporting period. One of the most interesting applications of pattern recognition methods was image analysis. Bright (I9) utilized PCA to analyze compositional electron microprobe X-ray maps. PCA was also used to investigate the effects of sampling procedures on 2-D Raman images (I10) and to classify SIM micrographs (I11). Neural nets (I12) were used to classify clusters in a scatter diagram formed from images in a multispectral data set and to detect structural variability within an image data set of a member of the chaperone family of proteins (I13). Multichannel imaging Analytical Chemistry, Vol. 70, No. 12, June 15, 1998
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and discriminant analysis were employed to characterize whole sections of wheat grain (I14). Wienke (I15-I18) applied a Fuzzy ARTMAP to correctly classify image data from postconsumer packages on an industrial conveyor belt. Mansfield (I19) describes a novel method for analyzing image data, based on fuzzy c-means clustering. Pattern recognition methods have become an integral part of artificial odor systems. This topic has been reviewed by Craven (I20), Schweizer-Berberich (I21), and Morizumi (I22). RymanTubb (I23) reported improvements in recognition ability of odor sensor arrays due to neural computing. Using discriminant analysis, Vernat-Rossi (I24, I25) showed that semiconductor gas sensors are able to identify different types of cured meat products. An electronic nose with a neural net could classify grain samples on the basis of their odor (I26). PCA and neural nets were used to classify soot analyzed by an array of gas sensors (I27). Perfumes (I28) and blended fragrances (I29) could be identified as to type using piezoelectric crystal sensor arrays and PCA. Royet (I30) attempted to relate odor profiles and perceptual memory performances using discriminant analysis and neural nets. Wang (I31) coupled radial basis neural nets with the fuzzy c-means algorithm to improve odor pattern classification of electronic noses. The importance of data preprocessing on the performance of perceptrons and feed-forward neural networks cannot be overstated (I32). An artificial neural network combined with fuzzy logic was shown to perform better then conventional pattern recognition methods for an electronic nose (I33). A hybrid network for odor classification that exploits the benefits of selforganizing feature maps and feed-forward networks was developed by Di Natale (I34). The implementation of a fuzzy neural network with an array of tin oxide-based gas sensors for carbon monoxide, methanol, and ethanol detection at different humidity levels was demonstrated by Avaritsiotis (I35). Detection of drugs and explosives by metal oxide sensors using a neural network to interpret the heterogeneous sensor matrix was reported by Grimaldi (I36). A significant number of references involving pattern recognition applications focused on applications in food chemistry. These applications were targeted in six areas: (1) coffee, tea, and milk, (2) cheese, (3) fruit juice, (4) vegetables, (5) essential food oils, and (6) wine and spirits. Routine analytical methods, such as GC, HPLC, and trace metal analysis, were used to obtain the data with PCA and discriminant analysis constituting the bulk of the multivariate data analysis techniques employed. Coffee, tea, and milk applications focused on adulteration and geographic origin. Principal component and discriminant analysis of DRIFTS spectra of coffee beans could differentiate coffee type (I37) or detect adulteration (I38). PCA applied to headspace and HPLC data of coffees permits grouping of samples according to origin or processing type (I39). PCA and LDA were applied to mid-IR spectra for the identification of a variety of green coffee (I40). Green and black teas were correctly classified using amino acids, caffeine, theobromine, and theophylline as features (I41). Goodacre (I42) used pyrolysis mass spectrometry and artificial neural nets to detect adulteration of milk. Cheese applications focused on varieties. PCA and LDA of the mineral elements (I43) in cheese or of their SDS-PAGE profiles (I44) could identify the type of cheese. Fruit juice applications focused on adulteration. 218R
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Proton NMR combined with PCA, K-NN, and LDA was used to differentiate between authentic and nonauthentic orange juice (I45). Authentic and adulterated orange juice samples were correctly identified using a neural network developed from the concentration of the flavanones and trace element components of the juice samples (I46). Fruit drink could be differentiated from fruit juice (I47) by PCA of its volatiles. Vegetables focused on variety and origin. Potatoes were classified by type using isoelectrophoretic focusing patterns and a feed-forward neural net (I48). HPLC profiles of soy sauce could be assigned to their brand by SIMCA (I49). Soy sauce classification by geographic region was accomplished by linear discriminant analysis of NIR data (I50). Discriminant analysis and NIR was also used to determine the country of origin of gingseng (I51). Almond cultivars were identified by type using principal component analysis of their fatty acids (I52) or trace metal (I53). Essential oil applications focused on sensory evaluation and type. Sensory data were correlated with gas chromatograms of the volatile components of olive oils using an artificial neural network (I54) and PCA (I55). Gas chromatography and artificial neural nets were also used to identify the constituent oils present in edible oil blends (I56). Essential oils of cami (I57), thymus (I58, I59), and eucalyptus (I60) could be identified as to type by principal component and linear discriminant analysis of their gas chromatograms. Wine and spirit applications focused on geographic origin, brand, and aging. Wines could be classified by geographic region based on their trace metal content using an artificial neural network (I61). LDA, K-NN, and SIMCA analysis of the furanic and phenolic constituents of cider brandies could correctly classify these spirits by age (I62). Wines could be classified by brand from their amino acid profiles (I63, I64) using linear discriminant analysis. Changes in the composition of the volatile components of Airen wines over time were studied using stepwise discriminant analysis (I65) and hierarchical cluster analysis (I66). An increasing number of papers focused on the use of pattern recognition methods in the medical sciences. These applications were targeted in two distinct areas: (1) cancer detection and (2) identification of microorganisms. Applications of pattern recognition methods to cancer focused on the interpretation of NMR data. Using 1H magnetic resonance and LDA, it was possible to differentiate between malignant and benign human prostate tissue (I67). A noninvasive diagnosis of human brain tumors was developed using proton resonance spectroscopy and LDA (I68). A review on pattern recognition methods suitable for processing proton nuclear magnetic resonance spectroscopic data for cancer markers was written by de Certaines (I69). Pattern recognition analysis of in vivo magnetic resonance spectroscopy of lipid metabolites could classify tumors according to their stage and type (I70). Benninghoff (I71) reported that cluster analysis of the trace element distributions in malignant and normal tissues suggests the possibility of classification for cancer diagnosis. PCA of IR spectra of DNA suggests that progression of normal prostrate tissue to BPH and adenocarcinoma involves structural alterations that are distinctly different (I72). Discriminant analysis of six trace element concentrations measured by neutron activation analysis shows the technique to be a potentially valuable clinical tool for making the malignant-normal classification (I73). Application of pattern recognition methods to the identification of harmful
microorganisms focused on pyrolysis mass spectrometry. Voorhees (I74) showed that Gram-positive and Gram-negative bacteria could be differentiated by PCA of PYMS or PYGC data obtained from methylated fatty acids formed via in situ reactions. Goodacre (I75) reported that cluster analysis of Curie point pyrolysis mass spectrometra yields relationships indicative of the taxonomic position of microorganisms. He also showed that subspecies discrimination is possible when a Kohonen neural net is used to analyze the PYMS data (I76). Curie point pyrolysis mass spectrometry and artificial neural nets could also correctly type cheese-associated fungae (I77). Pattern recognition methods continue to be an integral component of environmental studies. The so-called chemical mass balance problem in receptor modeling has been reviewed by Gleser (I78) and Hopke (I79). Powers (I80) employed factor analysis to elucidate the type and distribution of NAPLs at sites with groundwater contamination. PCA was used to investigate water quality patterns via multivariate analysis of features chosen on the basis of the specific goals of the investigation (I81-I84). PCA was also used to relate air and precipitation chemistry to climate (I85) and phospholipid fatty acid profiles of biomass to the type of landfill leachate in a polluted aquifer (I86). There were a few papers focusing on the application of pattern recognition methods to pharmaceutics and archaeology. Pharmaceutical fingerprinting using HPLC trace impurity patterns was investigated by Collantes (I87) and Welch (I88). They showed that preprocessing of chromatographic data by Wavelet packets improved the performance of K-NN, SIMCA, and ANN. Hierarchical cluster analysis of X-ray flourescence spectra showed that French faience could be divided into two compositionally distinct groups, which they attributed to Nevers and La Rochelle (I89). Artificial neural networks were compared to PCA and SIMCA for obtaining information about geographic and chronological origin (I90). Cluster analysis and PCA of electron probe microbeam spectra of glass beads found at two Sarmatian burials suggest that two distinct glass recipes were used to manufacture the beads (I91). Pattern recognition techniques continue to be exploited in a wide variety of industrial settings. Factor analysis applied to data of 10 elements obtained by spark source mass spectrometry was used to assess the chemical homogeneity of solid-state materials (I92). Dolmatova (I93) used IR spectroscopy to characterize paper coating. Kohonen-type self-organizing maps of the data produced a visual representation of the papers, which could be used to predict the properties of paper coatings not part of the original data set. PCA of cotton fabric Raman (I94) and near-IR (I95) spectra could differentiate among types of dyes. Factor analysis of DRIFTS spectra of mercerized cotton fabrics revealed information about the preparation of the poplin cotton (I96). LIBRARY SEARCHING Library searching has become an integral part of chemistry. During this review period, there were only a few citations on library searching in the Chemical Abstracts database. As in previous years, the literature in this area of chemometrics has focused on applications. For the purpose of this review, the ensuing discussion will emphasize the enhancement of existing methods, unusual applications, and novel procedures.
The combination of neural networks and library searching for identifying individual components in unresolved mixtures has recently been the subject of numerous reports (J1-J3). Neural nets can clean a database by identifying doubtful structures or spectra and can validate library search results. However, a better way to improve the performance of a library search involves standardization of the data. Wright (J4) has developed a method to correct for instrumental changes that can influence the responses of a spectrometer over time. This method, which involves the development of a transfer function to equalize instrument bias between different spectrometers, has been applied to mass spectral databases with truly impressive results. Most of the citations on library searching during this reporting period focused on structural databases. Combinatorial libraries are just one example of structural databases. The current status of these libraries has been recently reviewed (J5, J6). The structural diversity of these libraries is important, which has been the primary force motivating the development of genetic algorithms for searching large chemical databases, to assess their structural diversity (J7-J9). Cluster analysis has also been applied to similarity searching in a two- and three-dimensional structural database (J10). The advantages of encoding distance geometry in a two-dimensional molecular representation are significant, because these two-dimensional diagrams allow the use of conventional pattern recognition techniques for assessing molecular similarity and data searching (J11). A number of structural handling and pattern recognition techniques have been developed to assess the information content of two-dimensional structural databases, including the so-called MDL keys (J12), and selforganizing Kohonen maps (J13, J14). On this score, topological autocorrelation vectors appear promising in obtaining information about biologically active compounds in a structurally diverse library (J15). Simulated annealing (J16) has been employed to search databases of constitutional isomers possessing the proscribed properties. Prediction of protein structure in insertion and deletion regions is feasible using a library of protein fragments, which can be searched for similarity via pattern recognition methods (J17). Finally, some novel or interesting ways to use a database have also appeared. An electrospray mass spectral database of crude fungal extracts was developed for taxonomic purposes (J18). A database of methods for HPLC conditions has been designed by Yoshida (J19). A library of 2-D gel electrochromatograms of c-DNA has been developed by Lefkovitz (J20) for the purpose of facilitating gene retrieval. A database for evaluating the toxicological risk of pesticides was developed by Mazzullo (J21), using the Overtox-DB program. The use of library searching techniques to confirm the presence of trace levels of analytes from infrared spectra was recently investigated by Luinge (J22), who demonstrated the superiority of library search techniques over neural nets for this particular type of application. A library containing standardized FT-IR spectra of polymers was developed by Hummel (J23), who showed the importance of sample preparation on library performance. Blijenberg (J24) described the development and validation of a natural urinary stone data-based infrared library for analysis of urinary stones. Analytical Chemistry, Vol. 70, No. 12, June 15, 1998
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ARTIFICIAL INTELLIGENCE Artificial intelligence (AI) continues to be of interest to chemists as evidenced by the large number of citations in the Chemical Abstract database on expert systems. Expert systems have been used to solve problems in structural elucidation, spectrochemical analysis, and chromatography, by generating rules and models, and, sometimes, even refinements in our fuzzy reasoning of problems. Peris (K1) and Stillman (K2) have published a review on expert system applications in analytical chemistry. Elling (K3) reviewed expert networks that are a hybrid of an expert system and an artificial neural network. Elling showed that expert networks are able to automate the process of validating routine GC data and diagnosing instrument malfunctions. Expert systems have been compared to pattern recognition methods for classifying pyrolysis mass spectral data (K4) and have been compared to database systems for handling large quantities of data (K5). Database and expert system teaching paradigms have also been compared (K6). However, the development of a statistics-based expert calibration system is probably the most significant application of AI in analytical chemistry during this reporting period. Progress in this area was discussed by Kowalski (K7) and by Workman (K8). Expert systems have also been applied to problems in other areas of spectrochemical analysis during this reporting period including method validation in graphite furnace atomic absorption spectrometry (K9), detection of spectral interference (K10) and line selection (K11) in ICAP, and qualitative interpretation of X-ray fluorescence (K12, K13) and IR spectra (K14). A SIMS expert system (K15) for selecting the appropriate conditions, and Sherpa (K16), a MacIntosh-based expert system for interpretation of electrospray ionization LC/ MS data from protein digests, were reported in the literature. An expert system that utilizes a genetic-based classifier to learn classification rules from an ion chromatography database was developed by Buydens (K17). Mulholland (K18, K19) also reported an evaluation on an expert system for ion chromatography developed from a database instead of an expert. An expert system was compared to a self-organizing Kohoonen neural network for selection of detectors in ion chromatography (K20). Stillman (K21) discussed a package of macros called MATRIX for development of database functions for an expert system used to diagnose problems in gas chromatography. An expert system to control the use of a gas chromatograph for hazardous wastes was developed by Matek (K22). The problem of enantioselective separations was tackled by Bryant (K23), who is currently developing an expert system to tackle problems in chiral separations using Pirkle-type columns. A rule-based spectra interpretation system called SpecInt (K24) was developed to allow a user to deduce the chemical structure of a molecule from simultaneous spectroscopic measurements. The performance of these rule-based systems depends strongly on the type of structure generator applied (K25). EXPIRS (K26), an improved version of an expert system for generation of alternative sets of substructures derived from infrared specta interpretation, was proposed. X-PERT, a molecular structure interpretation system using IR and 1H and 13C NMR spectra was described in detail by Elyashberg (K27). ESSESA, an expert system for determining resonance assignments from 13C NMR 220R
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spectra using a knowledge base of 1277 substructures, was described by Huixiao (K28). Expert systems have also been applied to QSAR problems in toxicology. A critical review of expert systems for QSAR’s of lethal end points was authored by Cronin (K29). Combes (K30) authored a review on the use of AI for the prediction of toxic hazard via the establishment of structure-activity correlations. Klopman (K31-K33) has presented some interesting perspectives on the use of structure-activity expert systems in toxicology. Judson also reviews the use of AI for predicting toxicity (K34). The structural basis of sensory irritation was discussed by Rosenkranz (K35). He used MultiCASE, a structure-activity relationship expert system, to identify the molecular fragments responsible for the observed activity. Marchant (K36) compared the performance of the following expert systems via a set of 44 chemicals: DEREK, TOPKAT, CASE, COMPACT, and HAZARDEXPERT. Knowledge-based systems, a type of expert system, have also influenced the approach that chemists take in analyzing their data. Knowledge-based systems utilize libraries to develop their rules. However, dynamic link libraries, critical for the successful implementation of knowledge-based systems in analytical chemistry, have been difficult to develop. Difficulties with the implementation of dynamic link libraries have been reviewed recently by Massart (K37, K38). Knowledge-based systems have been successfully developed for differential pulse polarography (K39), acid/base titrations in nonaqueous solvents (K40), and infrared spectroscopy (K41). The construction of knowledge-based systems have also been reported during this review period in other fields, e.g., quantum chemistry (K42), physical chemistry (K43), organic synthesis (K44, K45), phase equilibria (K46), and composite material selection (K47). Barry K. Lavine is an Associate Professor of Chemistry at Clarkson University in Potsdam, NY. His research interests encompass many aspects of the application of computers to chemical analysis, including the development of novel methods for quantitative and qualitative analysis using genetic algorithms and soft modeling methods. Professor Lavine is on the editorial board of several journals and is the Assistant Editor of Chemometrics for Analytical Letters. He is an instructor for the ACS short course on chemometrics and has been a Visiting Professor of Chemistry at the Universiti Teknologi Malaysia.
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