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  • Analytical Chemistry and Spectroscopy  (25,033)
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  • 1
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 5 (1991), S. 545-545 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 2
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 3
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 3 (1989), S. 601-608 
    ISSN: 0886-9383
    Keywords: Gas phase flow injection analysis ; Membrane-differentiated analysis ; Multicomponent determinations ; Successive linear regression ; Successive regression in fiduciary region ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A membrane-discriminated gas phase analyzer is proposed for multicomponent determinations. Nitrogen gas flows countercurrent through outer and inner channels in a tube-in-tube arrangement. The only communication between the two channels occurs through a 500 μm aperture covered by a porous PTFE membrane. A mixture of organic compounds (up to four components) is injected into the inner channel by a heated backflushed injector and the sample components diffusing into the outer channel are monitored by a flame ionization detector (FID). A calibration set, consisting of pure components, binary, ternary and quaternary mixtures (a total of 64 samples), provides the known data base: temporal profiles of the FID output as a function of sample composition. Although the overall response behavior is not a linearly additive function of individual analyte concentrations, the use of successive inverse multiple linear regression (while continually altering the choice of the calibration samples considered for the forward regression, on the basis of the most recent values of the predicted unknown sample composition) is shown to yield analytical results for unknown samples that are in good agreement with their true values.
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  • 4
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 3 (1989), S. 609-609 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 5
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 4 (1990) 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 6
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 4 (1990), S. i 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 7
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    Journal of Chemometrics 4 (1990), S. 1-13 
    ISSN: 0886-9383
    Keywords: Factor analysis ; Self-modeling curve resolution ; Spectra isolation ; Target transformation factor analysis ; Iterative target transformation factor analysis ; Evolving factor analysis ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: One of the major applications of factor analysis in the chemical literature, self-modeling curve resolution (SMCR), is covered in this review, including a historical account of the methods derived from Lawton and Sylvestre's original method. Papers treating the theory or applications of SMCR are included. Qualitative and quantitative applications are described where appropriate.
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  • 8
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 6 (1992) 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 9
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 6 (1992), S. i 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 10
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    Journal of Chemometrics 6 (1992), S. 63-64 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 11
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    Journal of Chemometrics 6 (1992), S. 65-83 
    ISSN: 0886-9383
    Keywords: Trilinear decomposition ; Receptor modeling ; Source identification ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In many cases, monitoring data for ambient airborne particles can be organized in the form of a three-way data table with one way for chemical species, one for sampling periods and one for sites. A direct trilinear decomposition followed by a matrix reconstruction (DTDMR) is developed to analyze such a data table as a whole. The three-way data set is composed into three two-way matrices by a direct trilinear decomposition (DTD). The column vectors of each of the matrices are called ‘source profiles’, ‘emission patterns’ and ‘site coefficients’ respectively. Particulate sources are identified by examining both their source profiles and emission patterns. After the sources have been identified, emission patterns and site coefficients are used to produce a three-way matrix that gives estimates of mass contributions of sources to the samples collected at every site in every period. By simulation study, not only has the method been verified, but a good indicator has been found that shows the number of factors (i.e. sources) in the system. Unlike other receptor models, DTDMR does not require source profile data and does not involve trial-and-error procedures. Since DTDMR identifies sources based on variations in two dimensions, it has a higher potential to distinguish two sources that have similar chemical compositions. The DTDMR model has provided excellent results with simulated data and has been applied in a real world three-way data set.
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  • 12
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    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 6 (1992), S. 113-116 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 13
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    Journal of Chemometrics 6 (1992), S. 117-118 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 14
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    Journal of Chemometrics 6 (1992), S. 151-161 
    ISSN: 0886-9383
    Keywords: NIR spectroscopy ; Wavelength selection ; Interaction effects ; Multicomponent mixtures ; Partial least squares ; Generalized least squares ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Modern scanning (near-)infrared reflectance/absorption (NIR) spectroscopes measure the absorptions or reflectances at a sequence of around 1000 wavelengths. Training data may consist of 10-100 carefully designed sample mixtures for which the true composition of the mixture is either known by formulation or accurately determined by wet chemistry. In future one wishes to predict the true composition from the spectrum. In this paper we compare a simple wavelength selection approach with methods which retain all the wavelengths. It offers a powerful yet simple technique for choosing those wavelengths that are specific to each pure component as against the other components (including the medium) for the varying compositions. In the presence of a defined range of ingredients in thus chooses wavelengths which are highly selective for each particular component. It has the added advantage of selecting wavelengths which are little effected by interaction effects and consequent non-linearities.The calibration data used consist of 125 observations of three sugars, each varying at five levels in a full 53 design. The validation set consists of 21 further samples specially selected to have compositions outside the range of the training sample. The selection methods perform much better on this prediction set than methods which retain all the wavelengths, 700 in this case. The leave-one-out cross-validation internal to the calibration data would point to the opposite finding and suggests that such crossvalidations may be overly flattering to techniques such as partial least squares and may encourage overfitting. After selection, simple straightforward least squares methods may be used, eschewing the need for ‘shrinkage’ methods such as partial least squares or ridge regression.
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  • 15
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    Journal of Chemometrics 4 (1990), S. 47-50 
    ISSN: 0886-9383
    Keywords: Classification ; Pattern recognition ; Preprocessing ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Over the past 15 years the linear learning machine has been applied to a large number of chemical problems. The learning machine approach is conceptually simple and does not require knowledge about the statistical distribution of the data. However, there are problems associated with this approach. One problem which has not been investigated is the influence of mislabeled samples on the positioning of the hyperplane in feature space. If a few samples in a data set are incorrectly tagged prior to training (i.e. the samples are labeled as members of class 2 even though they are actually members of class 1), it is still possible using the linear learning machine to achieve a classification success rate of 100% for the training set. However, unfavorable results will be obtained for the prediction set. The magnitude of this effect and its potential implications regarding the proper use of the linear learning machine are discussed.
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  • 16
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    Journal of Chemometrics 4 (1990), S. 51-59 
    ISSN: 0886-9383
    Keywords: Deconvolution ; Jansson's method ; Peak restoration ; Iterative deconvolution ; Peak resolution ; Non-linear deconvolution ; Super-resolution ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In previous papers Jansson's method was found to be successful at deconvolving severely overlapped gas chromatographic peaks. In the most recent paper the method was evaluated with respect to quantitative accuracy, peak area and retention time repeatability. The problems associated with deconvolving noisy data and some alternatives which can improve the ability of Jansson's method to deconvolve noisy data are discussed. These alternatives include presmoothing the data with a nine-point, third-order polynomial filter and data reblurring. This paper will test these methods on peaks with various degrees of resolution and signal-to-noise ratios.
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  • 17
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    Journal of Chemometrics 4 (1990), S. 61-77 
    ISSN: 0886-9383
    Keywords: Infrared ; Spectroscopy ; Spectrometry ; Retrieval ; Confirmation ; Chemometrics ; Adequate peaks ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In the series of analytical techniques for identification of chemical substances, infrared spectrometry presents by far the highest information content. However, the information is most complicated too. It concerns a multitude of band positions, band intensities and band shapes, which, moreover, can be disturbed by matrix and other effects. The high redundancy, however, allows conclusions to be made by a qualitative, subjective procedure.IR is often used to prove the equality between a sample and a reference material, e.g. in quality control of a production process. In forensic control, the question to be answered is mostly not to prove equality, but whether or not the presence of a compound in a sample, e.g. a drug, can be proved. Moreover, testing has to be performed according to objective rules.To fulfil these requirements, a new retrieval algorithm, the ‘Adequate Peaks Search’, is presented. It concerns representing the reference spectra by sets of adequate peak positions and the sample spectrum by a set of all peak positions, whereafter the cross-sections of the sample set and the reference sets are determined. The concept ‘adequate peak’ is defined and criteria have been formulated to evaluate the results into a positive (presence of the analyte is proved) or negative (presence is not proved) conclusion.The detection limit when the Adequate Peaks Search (APS) method was applied was four to seven times lower than that attained by a number of experts.
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  • 18
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    Journal of Chemometrics 4 (1990), S. 79-90 
    ISSN: 0886-9383
    Keywords: PLS ; Three-way matrices ; Calibration ; Residual bilinearization ; Background correction ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: When using hyphenated methods in analytical chemistry, the data obtained for each sample are given as a matrix. When a regression equation is set up between an unknown sample (a matrix) and a calibration set (a stack of matrices), the residual is a matrix R.The regression equation is usually solved by minimizing the sum of squares of R. If the sample contains some constituent not calibrated for, this approach is not valid. In this paper an algorithm is presented which partitions R into one matrix of low rank corresponding to the unknown constituents, and one random noise matrix to which the least squares restrictions are applied. Properties and possible applications of the algorithm are also discussed.In Part 2 of this work an example from HPLC with diode array detection is presented and the results are compared with generalized rank annihilation factor analysis (GRAFA).
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  • 19
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    Journal of Chemometrics 4 (1990), S. 91-96 
    ISSN: 0886-9383
    Keywords: Sample size ; Monte Carlo ; Multivariate, normal ; Q-Q plots ; Classification ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Because many pattern recognition techniques are predicated on the assumption of mutivariate normal data, Monte Carlo simulation studies were performed to determine the number of samples that are necessary to describe a multivariate normal population adequately. From these studies we have learned that hundreds of samples are required. These results suggest that parametric procedures should only be used to analyze very large data sets.
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  • 20
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    Journal of Chemometrics 4 (1990), S. 97-100 
    ISSN: 0886-9383
    Keywords: Matrix decomposition ; NIPALS ; Principal component ; SIMCA ; PLS ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The Non-linear Iterative Partial Least Squares (NIPALS) algorithm is used in principal component analysis to decompose a data matrix into score vectors and eigenvectors (loading vectors) plus a residual matrix. NIPALS starts with some guessed starting vector. The principal components obtained by NIPALS depends on the starting vector; the first principal component could not always be computed. Wold has suggested a starting vector for NIPALS, but we have found that even if this starting vector is used, the first principal component cannot be obtained in all cases. The reason why such a situation occurs is explained by the power method. A simple modification of the original NIPALS procedure to avoid getting smaller eigenvalues is presented.
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  • 21
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    Journal of Chemometrics 4 (1990), S. 101-101 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 22
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    Journal of Chemometrics 7 (1993), S. 75-76 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 23
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    Journal of Chemometrics 4 (1990), S. i 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Topics: Chemistry and Pharmacology
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  • 24
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    Journal of Chemometrics 4 (1990), S. 103-121 
    ISSN: 0886-9383
    Keywords: Kalman filter ; Recursive digital filter ; Square-root filter ; Information filter ; Parameter estimation ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The use of recursive filtering techniques for parameter estimation in a variety of areas is reviewed. In particular, the Kalman filter algorithm is described, along with several variations, including square-root, UDUT and information filters. The solution to parameter estimation problems is discussed for both linear and non-linear models. Applications described include calibration, curve resolution in spectroscopy, chromatography, electrochemistry, kinetic analysis and process monitoring.
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  • 25
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 26
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    Journal of Chemometrics 7 (1993), S. 243-253 
    ISSN: 0886-9383
    Keywords: Orthogonal expansion ; Mapping ; Modelling ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In this paper we discuss the orthogonal expansion of data matrices and its application to mapping and modelling. Two new methods, modified optimal discriminant plane (MODP) for mapping and order Gram-Schmidt orthogonalization (OGSO) for modelling, are proposed and examples are given.
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  • 27
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    Journal of Chemometrics 7 (1993) 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 28
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    Journal of Chemometrics 7 (1993), S. 381-392 
    ISSN: 0886-9383
    Keywords: Factor analysis ; Kolmogorov-Smirnov test ; Non-parametric tests in factor analysis ; Non-parametric test for principal components ; Principal component analysis ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Each eigenvector of the dispersion matrix [X]T [X] was shown to be a partial predictor of the original data matrix [X], the sum of the predictions from the individual principal components being equal to the expectance of [X]. By comparing the distributions of the members of two neighbouring predicted matrices, [X̃]1…i and [X̃]1…i+1 (i.e. the sums of the first i and i + 1 individual predictions respectively), it was shown that they should be indistinguishable provided that i is equal to or greater than the effective rank of [X], and significantly different otherwise. This was confirmed by analysing the visible absorption spectra of methyl orange and methyl red solutions as well as the Raman spectra of Na2SO4 and MgSO4 solutions. On the grounds of these findings, a non-parametric goodness-of-fit test for assessing the effective rank of [X] was proposed which proved to be comparatively conservative and more robust than most currently used tests.
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  • 29
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    Journal of Chemometrics 7 (1993), S. 369-379 
    ISSN: 0886-9383
    Keywords: Simulated annealing ; Constrained background bilinearization ; Calibration ; Two-way bilinear data ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Generalized simulated annealing (GSA) is an optimization technique for locating the global optimum. In this paper GSA was used as the optimization procedure in the constrained background bilinearization (CBBL) of two-way bilinear data in order to reduce the possibility of sinking into local optima. The behaviour of the algorithm and its comparison with the modified Powell algorithm were studied by simulations and real fluorescence excitation-emission data for organic dye mixtures.
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  • 30
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    Journal of Chemometrics 7 (1993), S. 439-445 
    ISSN: 0886-9383
    Keywords: Calibration ; Rank annihilation ; Residual bilinearization ; Three-way ; Trilinear ; Net analyte rank ; Second-order ; Generalized rank annihilation method (GRAM) ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Through theoretical analysis and computer simulation, this short communication comments on the residual bilinearization (RBL) method and compares it with non-bilinear rank annihilation (NBRA) for the treatment of second-order calibration with non-bilinear data. It is found that these two methods are mathematically equivalent but have different noise propagation properties. The second-order advantage, namely quantitation in the presence of unknown interferences, can be carried over to non-bilinear data only if there exists a net analyte rank (NAR) for the analyte of interest.
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  • 31
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    Journal of Chemometrics 7 (1993), S. 495-526 
    ISSN: 0886-9383
    Keywords: Standard errors ; Eigenvalues ; PCA ; MLR ; GRAM ; Rank estimation ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: New expressions are derived for the standard errors in the eigenvalues of a cross-product matrix by the method of error propagation. Cross-product matrices frequently arise in multivariate data analysis, especially in principal component analysis (PCA). The derived standard errors account for the variability in the data as a result of measurement noise and are therefore essentially different from the standard errors developed in multivariate statistics. Those standard errors were derived in order to account for the finite number of observations on a fixed number of variables, the so-called sampling error. They can be used for making inferences about the population eigenvalues. Making inferences about the population eigenvalues is often not the purposes of PCA in physical sciences. This is particularly true if the measurements are performed on an analytical instrument that produces two-dimensional arrays for one chemical sample: the rows and columns of such a data matrix cannot be identified with observations on variables at all. However, PCA can still be used as a general data reduction technique, but now the effect of measurement noise on the standard errors in the eigenvalues has to be considered. The consequences for significance testing of the eigenvalues as well as the usefulness for error estimates for scores and loadings of PCA, multiple linear regression (MLR) and the generalized rank annihilation method (GRAM) are discussed. The adequacy of the derived expressions is tested by Monte Carlo simulations.
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  • 32
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    Journal of Chemometrics 7 (1993), S. 559-566 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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  • 33
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    Journal of Chemometrics 8 (1994), S. 21-36 
    ISSN: 0886-9383
    Keywords: GRAM ; Tucker ; Unfold ; NBRA ; Second-order ; Three-way ; PARAFAC ; Trilinear ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: If an analytical instrument or instrumental method gives a response matrix when analyzing a pure analyte, the instrument or instrumental method is called a second-order method. Second-order methods that generate a response matrix for a pure analyte of rank one are called rank-one second-order methods. If the response matrix of a pure analyte is not rank one, essentially two cases exist: medium rank (between two and five) and high rank (greater than five). Subsequently, medium- and high-rank second-order calibration tries to use medium- and high-rank second-order methods to analyze for analytes of interest in a mixture. A particular advantage of second-order methods is the ability to analyze for analytes of interest in a mixture which contains unknown interferences. Keeping this advantage is the challenge on moving away from rank-one second-order calibration methods. In this paper a medium-rank second-order calibration method is proposed based on least-squares restricted Tucker models. With this method the second-order advantage is retained.
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  • 34
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    Journal of Chemometrics 4 (1990), S. 267-268 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Topics: Chemistry and Pharmacology
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  • 35
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    Journal of Chemometrics 4 (1990), S. 269-269 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Topics: Chemistry and Pharmacology
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  • 36
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    Journal of Chemometrics 4 (1990) 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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  • 37
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    Journal of Chemometrics 8 (1994), S. 101-101 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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  • 38
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    Journal of Chemometrics 8 (1994), S. 103-110 
    ISSN: 0886-9383
    Keywords: Taguchi ; Robust design ; Design of experiments ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: This paper is intended to convey the essence of Taguchi's design approach to chemists and others with an interest in chemometrics. Although most Taguchi-style applications worldwide have been in electronics and in elaborately transformed manufactures, examples are increasingly found in chemical processes and in the food industry.Foremost among Taguchi's contributions is the concept of designing processes and products to be robust to the uncontrollable environmental influences which they experience during their operation or lifetime. This concept is explained with a worked example.
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    Journal of Chemometrics 8 (1994), S. 127-145 
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    Keywords: Compression ; Multivariate analysis ; B-splines ; FT-IR spectra ; Second-order ; Two-dimensional ; Hyphenated methods ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In order to improve the storage and CPU time in the numerical analysis of large two-dimensional (hyphenated, second-order) infrared spectra, a data-preprocessing technique (compression) is presented which is based on B-splines. B-splines have been chosen as the compression method since they are wellsuited to model smooth curves. There are two primary goals of compression: a reduction of file size and a reduction of computation when analyzing the compressed representation. The compressed representation of the spectra is used as a substitute for the original representation. For the particular example used here, approximately 0.16 bit per data element was required for the compressed representation in contrast with 16 bits per data element in the uncompressed representation. The compressed representation was further analysed using principal component analysis and compared with a similar analysis on the original data set. The results shows that the principal compotent model of the compressed representation is directly comparable with the principal component model of the original data.
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  • 40
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 8 (1994), S. i 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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  • 42
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    Journal of Chemometrics 8 (1994), S. 181-203 
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    Keywords: RAFA ; GRAM ; Eigenvalues ; Bias ; Variance ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Rank annihilation factor analysis (RAFA) is a method for multicomponent calibration using two data matrices simultaneously, one for the unknown and one for the calibration sample. In its most general form, the generalized rank annihilation method (GRAM), an eigenvalue problem has to be solved. In this second paper expressions are derived for predicting the bias and variance in the eigenvalues of GRAM. These expressions are built on the analogies between a reformulation of the eigenvalue problem and the prediction equations of univariate and multivariate calibration. The error analysis will also be performed for Lorber's formulation of RAFA. It will be demonstrated that, depending on the size of the eigenvalue, large differences in performance must be expected. A bias correction technique is proposed that effectively eliminates the bias if the error in the bias estimate is not too large. The derived expressions are evaluated by Monte Carlo simulations. It is shown that the predictions are satisfactory up to the limit of detection. The results are not sensitive to an incorrect choice of the dimension of the factor space.
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    Journal of Chemometrics 4 (1990) 
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 8 (1994), S. 243-243 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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  • 45
    ISSN: 0886-9383
    Keywords: Fitting ; Non-linear ; Least squares ; Refinement ; Constraints ; MSE ; Confidence ; C ls ; XPS ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A non-linear least squares iterative refinement has been implemented which shows high performance on a multiple-peak spectrum including baseline or background. Constraints as well as links within a range are introduced to drive the mathematical optimization: each peak parameter (i.e. height, position, Gaussian/Lorentzian mixing ratio and HWHM on both left and right sides) has assigned to it an allowed range of variation and can be strained to be correlated with other parameters belonging either to the same peak (symmetrical peaks) or to other peaks (doublets, triplets, etc.). Peak shapes typical of XP spectra are used and applications in the field of XPS are discussed. Through emulated curves with Poisson distributed noise, the accuracy and precision of back-calculated (refined) parameters have been estimated. Moreover, a confidence level calculated from X2 and degrees of freedom has been suggested to check the overall fitting of experimental curves where the signal-to-noise ratio is a priori unknown. An application to real C ls XP spectra is described as an example and a list of suggestions is given to match operator requirements. Finally, features of NLLSRC are discussed with respect to other approaches.
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    Journal of Chemometrics 8 (1994), S. 263-272 
    ISSN: 0886-9383
    Keywords: Molecular descriptors ; Principal component analysis ; Chemometrics ; Pattern recognition ; Total surface area ; PCDD PCDF ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: New theoretical molecular indices are defined. They contain information about the whole molecular structure in terms of size, shape, symmetry and atom distribution. These indices are calcualted from the (x, y, z) co-ordinates of a molecule within different weighting schemes in a straightforward manner and represent a very general approach to describe molecules, molecular fragments, macromolecules and molecular conformations in a unitary conceptual framework. Their interpretability is quite evident and is defined by the same mathematical properties as the algorithm used for their calculation. Examples on the total surface area, toxicity of PCDD and PCDF and reaction rate of catalysed reactions show a high modelling power of these indices.
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    Journal of Chemometrics 8 (1994), S. 301-302 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Topics: Chemistry and Pharmacology
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  • 48
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    Journal of Chemometrics 8 (1994), S. 333-347 
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    Keywords: PLS ; ATR ; Paper ; Resolution ; Infrared ; FTIR ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Attenuated total reflectance Fourier transform infrared spectrometry (ATR-FTIR) has been used to determine the amount of styrene-butadiene latex on the surface of coated papers and to predict the composition of the polymer. Spectrum recording was performed on the sample in its usual form without any modification.For quantitative analysis, partial least squares (PLS) regression, principal component regression (PCR) and multi-linear regression (MLR) were used to calculate models for prediction. The best result is obtained with PLS.We analysed two series of paper samples. The first analysis concerns the measurement of the quantity of latex of a constant quality on the coating surface. For 15 samples the concentration varied between 5 and 25 parts (grams per 100g of mineral pigments). We compared the predictive results at various resolutions. We obtained a relative error of 0.15 parts in latex at 4 cm-1 resolution. The second analysis concerns the measurement of the styrene/butadiene ratio in various types of latex. We obtained a relative error of 0.156 parts for styrene determination and 0.161 parts for butadiene determination.
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    Journal of Chemometrics 8 (1994), S. 375-376 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 8 (1994), S. 391-407 
    ISSN: 0886-9383
    Keywords: Neural networks ; Non-linear multivariate regression ; Pattern classification ; Kalman filter ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Finding methods for the optimization of weights in feedforward neural networks has become an ongoing developmental process in connectionist research. The current focus on finding new methods for the optimization of weights is mostly the result of the slow and unreliable convergence properties of the gradient descent optimization used in the original back-propagation algorithm. More accurate and computationally expensive second-order gradient methods have displaced earlier first-order gradient optimization of the network connection weights. The global, extended Kalman filter is among the most accurate and computationally expensive of these second-order weight optimization methods. The iterative, second-order nature of the filter results in a large number of calculations for each sweep of the training set. This can increase the training time dramatically when training is conducted with data sets that contain large numbers of training patterns. In this paper an adaptive variant of the global, extended Kalman filter that exhibits substantially improved convergence properties is presented and discussed. The adaptive mechanism permits more rapid convergence of network training by identifying data that contain redundant information and avoiding calculations based on this redundant information.
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    Journal of Chemometrics 8 (1994), S. 439-443 
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    Keywords: Pattern recognition ; Principal component analysis ; Inverse mapping ; Optimization ; Material design ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: An inverse mapping method called PCB (principal component backing), in which the point representing an unknown sample from a low-dimensional principal component subspace is back-projected to the high-dimensional original feature space, is proposed. Two sorts of boundary conditions, non-linear inverse mapping and linear inverse mapping, are used to obtain an accurate solution in the PCB method. The method is applied to the material design of high-Tc superconductors, predicting the composition and process conditions for the synthesis of F-doped Bi-based materials. Samples in the ‘optimal’ region with the highest Tc of the Bi-based ceramics have been predicted.
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    Journal of Chemometrics 9 (1995), S. 1-2 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 9 (1995), S. 3-20 
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    Keywords: Deconvolution algorithms ; Instrumental analysis ; Spectrometry ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Deconvolution algorithms for measurand reconstruction are considered. Their metrological and numerical properties are briefly characterized. Six algorithms most frequently used for instrumental applications are selected for closer analysis. Their comparative study is based on the use of spectrometric-type synthetic data, calorimetric-type synthetic data and spectrometric real-world data. Conclusions concerning computational complexity and accuracy of the compared algorithms as well as their metrological applicability are drawn.
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    Journal of Chemometrics 9 (1995), S. 67-68 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 9 (1995) 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 9 (1995) 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 9 (1995), S. 125-135 
    ISSN: 0886-9383
    Keywords: Procrustean analysis ; FT-IR spectroscopy ; NIR spectroscopy ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Procrustean methods allow the fitting of a given matrix to another given matrix observed on the same objects. In the traditional approach orthogonal constraints are imposed upon the transformation matrix, whereas in the alternative approach Procrustean analysis may be performed without such constraints. The two methods (with and without constraints) were compared on data dealing with mid- and near-infrared spectra of oil. The aim was to reconstruct the mid-infrared spectral information using data from the near-infrared spectra. Unconstrained Procrustean analysis proved to be the more efficient for both the calibration and verification sets. Furthermore, the analysis of the transformation matrix between the two infrared ranges made it possible to indicate wavelengths and wave numbers corresponding to the same chemical groups.
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    Journal of Chemometrics 9 (1995), S. 91-123 
    ISSN: 0886-9383
    Keywords: H-principle ; PCA ; PLS regression ; latent variable models ; quadratic models ; sensitivity analysis ; outlier tests ; prediction variances ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: We present here an algorithmic approach to modelling data that includes principal component analysis (PCA) and partial least squares (PLS). In fact, the numerical algorithm presented can carry out PCA or PLS. The algorithm for linear analysis and extensions to non-linear analysis applies to both PCA and PLS. The algorithm allows for combination of PCA and PLS types of models and therefore extends modelling to new types of models that involve combination of regression models and ‘selection of variation’ models, which is the idea of PCA-type models. The fact that the algorithm carries out both PCA and PLS shows that PCA and PLS are based on the same theory. This theory is based on the H-principle of mathematical modelling. The algorithm allows tests for outliers, sensitivity analysis and tests of submodels. These aspects of the algorithm are treated in detail. We compute various measures of sizes, e.g. of components, of the covariance matrix, of its inverse, etc. that show how much the algorithm has selected at each step. The analysis is illustrated by data from practice.
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    Journal of Chemometrics 9 (1995), S. 169-178 
    ISSN: 0886-9383
    Keywords: minimum volume ellipsoid (MVE) estimators ; robust distance method ; pattern recognition ; Hotelling's T2 statistics ; near-infrared spectra ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A method for pattern recognition analysis of near-infrared spectra has been developed using robust distances determined by minimum volume ellipsoid (MVE) estimators of multivariate location and scatter. Classical methods such as the Mahalanobis distance method often fail in the presence of a moderate number of outliers in a training data set, while robust distance methods can tolerate a considerably larger proportion of outliers in a training data set Outliers can be detected by their relatively large robust distances and can be excluded from a training set without a priori knowledge of the nature of the data set. In this paper the properties of a robust distance method are examined using near-infrared spectra of sulfamethoxazole and mixtures with its major degradation products, sulfanilic acid and sulfanilamide. The robust distance method successfully detected unacceptable samples (71.4%-89.3% (α = 0.05) or 78.6%-92.9% (α = 0.10)) even when they were inadvertently included in the training data set.
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    Journal of Chemometrics 9 (1995), S. 226-228 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 9 (1995), S. 197-209 
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    Keywords: proficiency test ; true value ; homogeneity robust statistic ; standard ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Profficiency tests raise two types of problems: (i) the homogeneity of a sample must be carefully checked before using it as a reference material; (ii) it is necessary to define a conventional reference value (RV) in order to rank participants. Since these topics are poorly addressed in the literature, a reference material was specially prepared in order to propose a procedure for verifying homogeneity and to establish whether the algorithms classically used to compute the conventional true value have an influence on the conclusion of the test.The homogeneity of the sample was not perfect and univariate and multivariate techniques were used to demonstrate some analytes can be suspected as heterogeneous. However, this was considered adequate for introducing the sample in a large proficiency test organized between four laboratory associations which regularly perform such tests. Altogether more than 3000 measurements were collected and eight algorithms were applied to compute the RV on 14 analytes. Thus is was possible to demonstrate that some of these algorithms are better adapted than others. It is also obvious that some analytes are better suited to proficiency testing.It can be concluded from this work that some effort towards standardization would be profitable to check homogeneity or to compute the RV, especially since the economic weight of proficiency testing is becoming even more important with the development of certification accreditation.
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    Journal of Chemometrics 5 (1991), S. 129-145 
    ISSN: 0886-9383
    Keywords: Multivariate calibration ; Biased regression ; Partial least squares (PLS) ; Principal component regression (PCR) ; Model validation ; Non-linear calibration ; Chemistry ; Analytical Chemistry and Spectroscopy
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    Topics: Chemistry and Pharmacology
    Notes: With the goal of understanding global chemical processes, environmental chemists have some of the most complex sample analysis problems. Multivariate calibration is a tool that can be applied successfully in many situations where traditional univariate analyses cannot. The purpose of this paper is to review multivariate calibration, with an emphasis being placed on the developments in recent years. The inverse and classical models are discussed briefly, with the main emphasis on the biased calibration methods. Principal component regression (PCR) and partial least squares (PLS) are discussed, along with methods for quantitative and qualitative validation of the calibration models. Non-linear PCR, non-linear PLS and locally weighted regression are presented as calibration methods for non-linear data. Finally, calibration techniques using a matrix of data per sample (second-order calibration) are discussed briefly.
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    Journal of Chemometrics 5 (1991), S. 147-161 
    ISSN: 0886-9383
    Keywords: Digital filtering ; Real-time analysis ; Kalman filtering ; Infrared spectroscopy ; Principal components regression ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Real-time monitoring of pollutant levels from a mobile measuring platform requires fast, flexible data analysis methods. This paper reports a method for rapid analysis of passive remotely sensed infrared data with the aid of a Kalman filter. The background spectra produced by emission from the atmosphere are modelled at the start of the data collection sequence with a simple principal components model obtained by eigenanalysis of the initial ‘blank’ data taken with the spectrometer. The species of interest are included in the state space model by a separate measurement of their infrared spectra. It is demonstrated that for best filter performance in detecting the simulated pollutant species SF6 in the atmosphere, a filter model with two principal components describing the emission background works best. The filter ‘maps’ of SF6 closely follow the integrated spectral intensities measured after removal of suitable backgrounds.
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    Journal of Chemometrics 5 (1991), S. 163-179 
    ISSN: 0886-9383
    Keywords: Principal component analysis ; Factor analysis ; Chemometrics ; Exploratory data analysis ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Principal component analysis is used to examine large multivariate databases. The graphical approach to exploratory data analysis is described and illustrated with a single example of chemical composition data obtained on environmental dust particles. While the graphical approach to exploratory data analysis has certain advantages over the numerical procedures, the empirical approach described here should be viewed as complementary to the more robust treatments that statistical methodologies afford.
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    Journal of Chemometrics 9 (1995), S. 331-342 
    ISSN: 0886-9383
    Keywords: partial least squares (PLS) ; variable selection ; IVS-PLS ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: With the aim of developing PLS models with improved predictive properties, an interactive variable selection (IVS) approach for PLS regression was introduced in Part I of this series. IVS-PLS is based on a dimension-wise selective removal of single elements in the PLS weight vector w. IVS uses cross-validation (CV) as a guiding tool. The present paper illustrates the use of IVS-PLS on both simulated data and real examples from chemistry. In the first example, spectrophotometric data were simulated according to an experimental design. The objective was to see how IVS-PLS was influenced by different levels of noise in X and Y and by the number of predictor variables (K). The results of the modelling are shown as response surfaces. In addition, four real examples were modelled by the IVS-PLS technique. The real data sets were chosen to reflect different types of data from chemistry. For each example a comparison of ‘prediction error sum of squares’ (PRESS) between IVS-PLS and classical PLS is madeFor most of the examples containing many predictor variables IVS-PLS shows an improvement in predictive properties over classical PLS. Also, improvements for IVS-PLS2 (modelling of more than one y-variable) models were found. For data sets with a moderate number of variables the influence of the IVS method becomes less pronounced.
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    Journal of Chemometrics 9 (1995), S. 389-409 
    ISSN: 0886-9383
    Keywords: multivariate image analysis ; principal component analysis ; exploratory data analysis ; projection in multivariate space ; graphical visualization ; noise ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Multivariate image analysis (MIA) is a powerful tool for many image segmentation and classification problems, but the interpretation and understanding of the original and resulting multidimensional (multivariate) data are not always easy. A strategy for MIA has been proposed which describes its usage on multivariate images for segmentation tasks. MIA starts with principal component analysis (PCA) and then continues with interactive analysis of the output from PCA. In this paper a number of extensions to MIA are proposed. The extensions are the suggestion to incorporate preprocessing of the multivariate image in MIA, the suggestion to use synthetic multivariate image models which create a clear-cut situation, and new visualization tools which improve the interactivity and understanding of the results. Extended MIA is applied on synthetic multivariate image data simulating a possible application with large noise, positron emission tomography (PET). As a result of the interactive analysis, suggestions for preprocessing emerge. The developed methodology for handling the noise is then applied on real PET image data with good results.
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    Journal of Chemometrics 5 (1991), S. 227-239 
    ISSN: 0886-9383
    Keywords: Unit-sum constraint ; Mixing proportions ; Ratio data ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The statistical analysis of compositional data is of fundamental importance to practitioners in general and to chemists in particular. The existing methodology is principally due to Aitchison, who effectively uses two transformations, a ratio followed by the logarithmic, to create a useful, coherent theory that in principle allows the plethora of normal-based multivariate techniques to be used on the transformed data. This paper suggests that the well-known class of Box-Cox transformations can be employed in place of the logarithmic to significantly improve the existing methodology. This is supported in part by showing that one of the most basic problems that Aitchison managed to overcome, namely the specification of an interpretable covariance structure for compositional data, can be resolved, or nearly resolved, once the ratio transformation has been applied. Hence the resolution is not directly dependent on the logarithmic transformation. It is then verified that access to the general Box-Cox family will allow a more accurate use of the normal-based multivariate techniques, simply because better fits to normality can be achieved. Finally, maximum likelihood estimation and some associated asymptotics are employed to construct confidence intervals for ratios of the true, unknown compositional constituents. Heretofore this had not been done even in the context of the logarithmic transformation. Applications to real data are presented.
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    Journal of Chemometrics 5 (1991), S. 241-248 
    ISSN: 0886-9383
    Keywords: Multivariate kurtosis ; Generalized distance ; Multivariate outliers ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Multivariate outliers in environmental data sets are often caused by atypical measurement error in a single variable. From a quality assurance perspective it is important to identify these variables efficiently so that corrective actions may be performed. We demonstrate a procedure for using two multivariate tests to identify which variable ‘caused’ each outlier. The procedure is tested with simulated data sets have have the same correlation structure as selected water chemistry variables from a survey of lakes in the Western United States. The success rates are evaluated for three of the variables for sample sizes of 50 and 100, significance levels of 0.01 and 0.05 and various amounts of mean shift. The procedure works best for highly correlated variables.
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    Journal of Chemometrics 5 (1991), S. 249-261 
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    Keywords: Asymptotic power ; Clean-up standard ; Gamma distribution ; Likelihood ratio test ; Uniformly most powerful unbiased test ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The object of this paper is to develop a suitable statistical procedure to evaluate clean-up standards at hazardous waste sites. Under the assumptions that contaminant masses at a site follow a gamma distribution and that the data from the pre-remediation baseline sample as well as from the interim or final sample taken after a certain period of operation are both distributed as gamma with the same shape parameter but different scale parameters, we derive a uniformly most powerful unbiased test of the hypothesis that a specified percentage of contaminant mass has been reduced. A large-sample approximation of the exact test procedure and a comparison with the likelihood ratio test are provided.
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  • 70
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    Journal of Chemometrics 5 (1991), S. 263-271 
    ISSN: 0886-9383
    Keywords: Mean ; Variance ; Lognormal ; Optimal estimators ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Theoretical and simulation results are employed to evaluate mean and variance estimates for normal data when a lognormal distribution is assumed and for lognormal data when a normal distribution is assumed. Misspecifying the distribution leads to the use of suboptimal estimation methods. However, the results show that the suboptimal methods still produce estimators of good quality (low bias and variance) relative to the minimum variance unbiased estimators for each distribution, at least when practical efficiency is considered.
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  • 71
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    Journal of Chemometrics 5 (1991), S. 273-290 
    ISSN: 0886-9383
    Keywords: Correspondence analysis ; Eastern Lake Survey - Phase I data ; Acidic deposition ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Correspondence analysis (CA) was applied to lakewater data in order to study the effects of acidic deposition on the geochemical composition of lakes in the Adirondacks. The lake chemistry data analyzed were taken from the Eastern Lake Survey - Phase I (ELS-I) conducted by the U.S. Environmental Protection Agency. CA was used to identify ‘outlying’ lake samples as well as ‘superflous’ and ‘unresolved’ analytes. Correlational relationship among analytes were also examined.
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  • 72
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    Journal of Chemometrics 5 (1991), S. 291-298 
    ISSN: 0886-9383
    Keywords: Absorbance ratio ; Statistical confidence ; Quality control ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Ratio measurements are commonly used to address a variety of analytical problems in environmental, forensic and pharmaceutical laboratories. In absorbance ratioing techniques, analytical chemists rely on the spectral features of the analyte(s) of interest. The absorbances at two wavelengths are monitored and the ratio of these two absorbances is computed. This ratio is then used to confirm the identity of the analyte(s) of interest, the purity of a product of the overlap of chromatographic peaks. These decisions often have far-reaching consequences (e.g. the identification of the source, biogenic or petrogenic, of hydrocarbons in biological tissues or water). Given the cost and the liabilities associated with such decisions, it is unfortunate that these ratios are seldom reported with any statistical confidence. The purpose of this study is to delineate the parameters that affect absorbance ratio measurements. The models that can be used to estimate the statistical confidence in these measurements are derived and evaluated experimentally. The results show that these models can estimate the relative standard deviations in absorbance ratios accurately. They can also estimate the effect of signal-to-noise ratio and the choice of wavelengths on the precision of absorbance ratios.
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    Journal of Chemometrics 5 (1991), S. 299-308 
    ISSN: 0886-9383
    Keywords: Errors in variables ; Orthogonal regression ; Latent variables ; Acid rain ; Acidic deposition ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Techniques for testing for and estimating relative bias between two laboratories are developed and applied to a survey of the chemistry of streams in the United States. The design of the quality assurance program allows estimation of linear corrections for bias as well as testing of the hypothesis of linearity. Designs of this type are useful, but improvements are suggested.
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    Journal of Chemometrics 5 (1991), S. 309-319 
    ISSN: 0886-9383
    Keywords: Confidence intervals ; Products of normal random variables ; Risk/exposure modeling ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In many environmental applications, such as exposure assessment and risk modelling, the desired estimate is a random variable computed as the product of three independently distributed random variables. These variables may not necessarily have the same mean and variance. The method for finding the 100(1 - α)% confidence interval for the mean of the product random variable has been proposed by some practitioners as the product of the 100(1 - α)% confidence interval of the three means. In this paper we show that the distribution of the product of three independent normal variables is not normal. We find the mean and variance of the product distribution. Further, we show that although the mean of the product is equal to the product of the means, the product of the three confidence intervals is not a good approximation of the confidence intervals for the mean of the product variable. The confidence interval of the mean of the product variable may be estimated by computer simulation. An algorithm for estimating the confidence interval for the mean of the product random variable is given. The program implementing this algorithm is given as an appendix.
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    Journal of Chemometrics 5 (1991), S. 321-331 
    ISSN: 0886-9383
    Keywords: Screening ; Ground-water quality ; Monitoring ; Volatile organic compounds (VOCs) ; Optimization ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: It is shown that the presence of 31-35 commonly measured volatile organic compounds (VOCs) in ground water can be detected with small error rates by using screening methods which analyze for a subset of such VOCs. A study of selected data sets indicates that analytical determinations of only from two to eight VOCs will suffice to detect 95% of all VOC hits. It is also shown that a serially optimal algorithm for selecting the VOCs for screening is very nearly as accurate as a globally optimal algorithm and much easier to implement. These conclusions are supported by empirical evidence from two drinking-water data sets and one hazardous waste site data set. Additional research areas are also outlined.
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    Journal of Chemometrics 5 (1991) 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. i 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. 333-343 
    ISSN: 0886-9383
    Keywords: Rank estimation ; Bootstrap resampling ; Canonical correlation ; Excitation-emission matrix ; Singular value decomposition ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Rank estimation by canonical correlation analysis in multivariate statistics has been proposed as an alternative approach for estimating the number of components in a multicomponent mixture. A methodological turning point of this new approach is that it focuses on the difference in structure rather than in magnitude in characterizing the difference between the signal and the noise. This structural difference is quantified through the analysis of canonical correlation, which is a well-established data reduction technique in multivariate statistics. Unfortunately, there is a price to be paid for having this structural difference: at least two replicate data matrices are needed to carry out the analysis.In this paper we continue to explore the potential and to extend the scope of the canonical correlation technique. In particular, we propose a bootstrap resampling method which makes it possible to perform the canonical correlation analysis on a single data matrix. Since a robust estimator is introduced to make inference about the rank, the procedure may be applied to a wide range of data without any restriction on the noise distribution. Results from real as well as simulated mixture samples indicate that when used in conjunction with this resampling method, canonical correlation analysis of a single data matrix is equally efficient as of replicate data matrices.
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    Journal of Chemometrics 5 (1991), S. 345-360 
    ISSN: 0886-9383
    Keywords: Three-way PCA ; Three-way PLS ; PARAFAC ; Trilinear ; Unfolding ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: For the calibration of chromatographic systems, different methods can be used. One class of methods utilizes three-way approaches. The calibration problem is stated in such a way that the decomposition of a three-way array can serve for the prediction of retention on new stationary phases.Two three-way approaches are presented: the Unfold-PCA and PARAFAC models. The theory of both methods is presented and the differences are highlighted, the main difference being that PARAFAC is a trilinear decomposition whereas Unfold-PCA is not. Both three-way methods are evaluated on a small data set consisting of retention measurements of eight solutes at six mobile phase compositions on six stationary phases. The differences in performance of the two models are minor.For calibration purposes, two variants of the methods are discussed: three-way PLS and an extension of PARAFAC. Again the theory and differences between the two methods are explained. The predictive performance of the two methods is compared using the same data set as earlier. The differences in predictive performance, however, are minor. Both methods are capable of predicting 98% of the variation in the test sets. Yet, there are other considerations when comparing methods than predictive performance, e.g. the quality of the predictions.
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    Journal of Chemometrics 5 (1991), S. 361-374 
    ISSN: 0886-9383
    Keywords: Closure ; Normalization ; Multivariate trimming ; Minimum distance ; Bootstrap ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Compositional data arise naturally in several branches of science, including chemistry, geology, biology, medicine, ecology and manufacturing design. In chemistry, these constrained data seem to occur typically when raw data are normalized or when output is obtained from a constrained estimation procedure, such as might be used in a source apportionment problem. It is important not only for chemists to be aware that the usual multivariate statistical techniques are not applicable to constrained data, but also to have access to appropriate techniques as they become available. The currently available methodology is due principally to Aitchison and is based on log-normal models. This paper suggests new parametric and non-parametric approaches to significantly improve the existing methodology. In the parametric setting, some recent work of Rayens and Srinivasan is extended and a practical regression model is proposed. In the development of the non-parametric approach, minimum distance methods coupled with multivariate bootstrap techniques are used to obtain point and region estimators.
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    Journal of Chemometrics 5 (1991), S. 375-387 
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    Keywords: Determinant criterion ; Multiresponse non-linear fitting ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: This work evaluates objective functions for multiresponse non-linear modeling using computer simulations. Tests are performed under a variety of signal-to-noise ratios and noise variance-covariance structures. The standard error of prediction for the model parameters, computed from 50 trials, is used for performance comparisons. The full rank and rank-deficient problems are considered. For the full rank problem one model was investigated, a first-order two-step consecutive reaction model, and two objective functions were considered, the total sum of squares and the determinant criterion. No distinction could be made between the two objective functions for this model.For the rank-deficient case two models were investigated, a first-order two-step consecutive reaction as in the full rank case, and a pH titration model described by the Henderson-Hasselbalch equation. Three objective functions were investigated for the rank-deficient case, the total sum of squares, a weighted total sum of squares and the determinant criterion. The total sum of squares was found to perform poorly under all conditions tested compared to the weighted total sum of squares and the determinant criterion. The determinant criterion was found to perform much better than the other two criteria when the data have a combination of a low signal-to-noise ratio and high variance-covariance noise structure.
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    Journal of Chemometrics 5 (1991), S. 405-409 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
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    Journal of Chemometrics 5 (1991), S. 389-403 
    ISSN: 0886-9383
    Keywords: Acoustic emission ; Pattern recognition ; Feature selection ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Features used to characterize acoustic emission signals from chemical systems are evaluated with regard to their potential for pattern recognition. Eight chemical systems involving phase transitions, hydration, dissolution and effervescence are employed and treated as separate signal classes. These are compared pairwise and the discriminatory capabilities of about 50 features are investigated by computing Fisher weights. Time domain and frequency domain descriptors are examined. Correlations among the features evaluated are also reported. Recommended descriptors are the mean and median frequencies, frequency bandwidth, number of level crossings (0% and 25%), crest factor (time and frequency domains), half-life, kurtosis and normalized percentiles of the signal and its power spectrum. The effectiveness of the recommended descriptors is demonstrated through the separation of signal classes in two different systems (melting ice and an enzyme-catalyzed gas formation reaction) by principal components analysis.
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. 416-416 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. i 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. 417-434 
    ISSN: 0886-9383
    Keywords: Factor analysis ; Power density distribution ; Chromatography ; Absorption spectroscopy ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Evaluation of the results of factor analysis of sets of spectroscopically detected chromatograms is carried out by examining the shapes of the abstract factors. This is done either by visual inspection or by analysis of the power density spectra produced from them. Owing to constraints imposed by the column function and the spectroscopic instrument function, the information content of the chromatograms necessarily occurs at low spatial frequencies. As a consequence, it appears as relatively broad features in the abstract chromatograms and as a peak in the low-frequency region of the corresponding power density plot. On the basis of examination of the power density distribution, a well-defined distinction is made between primary and secondary abstract factors. The major uncertainty encountered in determining the number of chemical components appears to arise from effects of contaminants in reagents.
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    Journal of Chemometrics 5 (1991), S. 435-453 
    ISSN: 0886-9383
    Keywords: Class-modelling methods ; Potential functions ; Pattern recognition ; Discriminant analysis ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A probabilistic and distribution-free class-modelling technique is developed from potential function discriminant analysis. In the multidimensional space of variables the class boundary is built either by the sample percentile of the probability density estimated by means of potential functions, or by the estimate of the ‘equivalent’ determinant of the variance-covariance matrix. The equivalent determinant is that of a hypothetical multivariate normal distribution whose mean probability density was obtained by potential functions. The bases of this modelling rule are evaluated by means of Monte Carlo experiments. The results on four datasets are used to measure the performances of this method, which equal and sometimes exceed the performances of parametric class-modelling methods based on linear and quadratic discriminant analysis which were used for comparison.
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    Journal of Chemometrics 5 (1991), S. 455-465 
    ISSN: 0886-9383
    Keywords: Correspondence analysis ; Cluster analysis ; Optimization ; Eigenanalysis ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Carey et al. utilized principal components analysis (PCA) to analyze frequency shift data obtained from piezoelectric sensors formed by coating quartz crystals with 27 different GC stationary phases and tested using 14 analytes. The objective of the analysis was to determine an optimal reduced set of coatings for detection of the analytes. The results were correlated with those obtained from cluster analysis. In this paper the data are re-analyzed using correspondence analysis (CA). The advantage of using CA include a symmetric treatment of sensor coatings and analytes and better identification of the representation of the analytes in terms of the detection components. The results obtained by the conjunctive use of PCA, a varimax rotation and cluster analysis were obtained by CA.
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    Journal of Chemometrics 5 (1991), S. 467-486 
    ISSN: 0886-9383
    Keywords: Expert system ; Neural network ; Fuzzy entropy ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A fuzzy multivariate rule-building expert system (FuRES) has been devised which also functions as a minimal neural network. This system builds rules from training sets of data that use feature transformation in their antecedents. The rules are constructed using the ID3 algorithm with a fuzzy expression of classification entropy. The rules are optimal with respect to fuzziness and can accommodate overlapped and underlapped clusters of data. The FuRES algorithm combines the benefits obtained from simulated annealing and gradient optimization, which provide robustness and efficiency respectively. FuRES classification trees support OR logic in their inference. The system automatically generates meaningful and consistent certainty factors during rule construction. Unlike other neural networks, FuRES uses local processing which furnishes qualitative information in the rule structure of its classification trees and variable loadings of the weight vectors.
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. i 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 5 (1991), S. 487-501 
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    Keywords: Calibration ; Locally linear models ; Discrimination ; Optimality ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A calibration situation is considered where the calibration data are split into subsets with good linear relationship between y and x within each group. Different strategies for good prediction in this case are proposed. Modifications for collinear data are considered and a simple simulated data set is used for illustration.
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    Journal of Chemometrics 5 (1991), S. 503-521 
    ISSN: 0886-9383
    Keywords: Antagonism ; Bounded ordinal scale ; Herbicide interaction ; Inter block comparisons ; Non-parametric ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Studies of interactions among bioactive compounds are often difficult to interpret unambiguously. A priori assumptions about the nature of such interactions can seriously distort analysis of the data. By applying a rank order analysis appropriate to the naturally ordinal scale of response to xenobiotic insult, several co-herbicides were successfully identified from among numerous candidates in an experiment involving multiple blocks, rates and species. Moreover, underlying herbicide interactions were substantiated and identified which were not apparent by more traditional parametric analysis.
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    Journal of Chemometrics 6 (1992), S. 247-255 
    ISSN: 0886-9383
    Keywords: Analysis of variance ; Assumptions ; Graphics ; Models ; Validation of model ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The computer has made it possible to scrutinize data rapidly by means of graphics. This should be done prior to the application of any model to the data, since the model must be validated before using it as a means of analyzing the data. The procedure is illustrated in terms of two examples of real experimental data.
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    Journal of Chemometrics 6 (1992), S. 284-285 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Journal of Chemometrics 6 (1992), S. 307-334 
    ISSN: 0886-9383
    Keywords: Non-linear PLS ; Quadratic regression ; Non-linear models ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: We treat here an extension of linear PLS regression to include regression on quadratic PLS components. The quadratic regression can be viewed as a natural extention of linear PLS regression to quadratic PLS according to the H-principle of mathematical modelling. The numerical implementation is treated in detail. It is shown that this approach can be used for models with large numbers of variables. Some modelling strategies are discussed depending on the purpose of the modelling. Applications of this approach are treated.
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    Journal of Chemometrics 6 (1992), S. 357-357 
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    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
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    Topics: Chemistry and Pharmacology
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