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  • Analytical Chemistry and Spectroscopy  (25,033)
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  • 11
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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.
    Additional Material: 7 Ill.
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  • 12
    Electronic Resource
    Electronic Resource
    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
    Additional Material: 1 Ill.
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  • 13
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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
    Type of Medium: Electronic Resource
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  • 14
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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.
    Additional Material: 4 Ill.
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  • 15
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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.
    Additional Material: 1 Ill.
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  • 16
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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.
    Additional Material: 3 Ill.
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  • 17
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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.
    Additional Material: 8 Ill.
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  • 18
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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).
    Additional Material: 2 Ill.
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  • 19
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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.
    Additional Material: 3 Ill.
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  • 20
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    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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