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  • Multivariate calibration  (2)
  • class modeling  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Analytica Chimica Acta 295 (1994), S. 109-118 
    ISSN: 0003-2670
    Keywords: Infrared spectrometry ; Moisture ; Multivariate calibration ; Oil ; Partial least-squares regression ; Proteins ; Soya flour ; Validation procedures
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Analytica Chimica Acta 284 (1993), S. 79-89 
    ISSN: 0003-2670
    Keywords: Air ; Chemometrics ; Gas chromatography ; Multivariate calibration ; Pattern recognition
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 69-89 
    ISSN: 0886-9383
    Keywords: chemometrics ; pattern recognition ; class modeling ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Different estimators of the Mahalanobis distance (such as that based on the Defrise - Gussenhoven correction) are studied and compared with respect to the bias on the distance and the characteristics (sensitivity and specificity) of the class model.Results obtained using estimators with critical values from χ2-statistics are compared with those obtained using estimators with critical values from β-statistics (training set) and Hotelling statistics (evaluation set).Tables are reported for D-statistics (useful for simulating populations of two categories with selectable theoretical sensitivity and specificity) and for critical values of the Mahalanobis distance obtained from β-statistics.For objects of the training set the estimator of the Mahalanobis distance based on the estimate of the covariance matrix produces models with the optimum sensitivity. The same model has too low a sensitivity for objects of the model category in the evaluation set, but good specificity for objects of outer categories.The estimator with the Defrise-Gussenhoven correction produces enlarged models with too high a sensitivity for objects in the training set, good sensitivity for objects of the model category in the evaluation set and low specificity for objects of outer categories.
    Additional Material: 6 Ill.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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