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  • 1
    Electronic Resource
    Electronic Resource
    Use of replication and signal-to-noise ratios in the identification and estimation of the composition of lubricant basestock mixtures using 13C nuclear magnetic resonance spectroscopy and projection into principal component/canonical variates space (1989)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 3 (1989), S. 359-374 
    Details
    ISSN: 0886-9383
    Keywords: Abstract factor analysis ; Target transformation factor analysis ; Signal-to-noise ratios ; Canonical variates analysis ; 13C NMR spectroscopy ; Mixture constituent identification and estimation ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The theory of experimental error in analysis of mixture experiments by abstract factor analysis or target transformation factor analysis is considered. The theoretical implications of using signal-to-noise ratios (as weights) or canonical variates analysis to reduce the level of imbedded error in the factor model are examined. The approach is illustrated by application to 13C NMR spectra of lubricant basestock mixtures.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180030205
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  • 2
    Electronic Resource
    Electronic Resource
    Testing for redundancy in product quality control test criteria: An application to aviation turbine fuel (1989)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 3 (1989), S. 477-491 
    Details
    ISSN: 0886-9383
    Keywords: Principal components analysis ; Cross-validation ; Procrustes rotation ; Variable selection ; Quality control ; Aviation turbine fuel ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Given a set of test criteria that determine a quality specification, the question often arises whether any of the tests are redundant because of intercorrelations Simple selection of tests on the basis of partial correlations with the other tests is rejected on the basis that the random error in the data may be causing spurious correlations.One method is to use cross-validation to define the systematic principal components and examine the correlation structure in this reduced space. It is shown that the presence of principal components dominated by individual tests (‘variable specific’ PCs), which are indicated by cross-validation as being non-systematic, must be taken into account. Having defined the dimensionality, a variable reduction method based on Procrustes rotation selects subsets of tests that preserve the structure of the samples in multivariate space. This is an attractive proposition in the context of maintaining a quality control specification. It is also shown that the variable reduction technique can be used to aid the identification of the true dimensionality of the data space. This approach is applied to a number of routine tests carried out on aviation turbine fuel.
    Additional Material: 10 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180030305
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  • 3
    Electronic Resource
    Electronic Resource
    Discriminant principal components analysis (1989)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 3 (1989), S. 589-600 
    Details
    ISSN: 0886-9383
    Keywords: Discriminant analysis ; Principal components ; Canonical variates ; Multivariate analysis of variance ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: When the number of variables exceeds the number of samples, one method of multivariate discrimination is to use principal components analysis to reduce the dimensionality and then to perform canonical variates analysis (PC-CVA). This paper proposes an alternative approach in which discriminant analysis is carried out by a weighted principal component analysis of the group means (DPCA). This method does not require prior data reduction and produces discriminant factors that are orthogonal in the original data space.The theory and performance of the two methods are compared. Although the individual factors of DPCA are found to be less discriminating than PC-CVA, the overall discrimination, calculated by multivariate analysis of variance, and the predictive value, estimated by the leaving-one-out error rate, are broadly comparable.
    Additional Material: 3 Tab.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180030407
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