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  • 1
    Electronic Resource
    Electronic Resource
    Identification and substructure analysis of oligosaccharide chains derived from glycoproteins by computer retrieval of high-resolution 1H-NMR spectra (1988)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 2 (1988), S. 11-27 
    Details
    ISSN: 0886-9383
    Keywords: Oligosaccharides ; Glycoproteins ; Identification ; Substructure analysis ; High-resolution ; 1H-NMR spectra ; Computer retrieval ; Reproducibility ; Chemical shifts ; Similarity index ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Based on a statistical model of the reproducibility of NMR spectral features, a system for computer retrieval of high-resolution 1H-NMR spectra of glycoprotein carbohydrates has been developed. For corresponding peaks in an unknown and a reference spectrum, a similarity index based on the reproducibility of the chemical shifts is calculated. In addition, a second similarity index, based on the probability distribution of the percentage of non-matching peaks, has been developed. From these two similarity indices, a combined similarity index using the recall-reliability function as the optimizing criterion has been derived.First results indicate that the ‘1H-NMR reproducibility-based retrieval’ (‘1HRR’) system offers good perspectives for both identification and substructure analysis.
    Additional Material: 12 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180020104
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  • 2
    Electronic Resource
    Electronic Resource
    An evaluation of SIMCA. Part 1  -  the reliability of the SIMCA pattern recognition method for a varying number of objects and features (1987)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 1 (1987), S. 221-230 
    Details
    ISSN: 0886-9383
    Keywords: Pattern recognition ; SIMCA ; PCA ; Classification ; Recall ; Cross-validation ; Indicator function ; Leave-x-out ; Pseudo random data sets ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The SIMCA pattern recognition method has been evaluated with pseudo random data sets. The number of objects varied from 5 to 50 and the number of features from 5 to 300.First, the determination of the significant number of PCs in the SIMCA models by the cross-validation method was compared with the indicator function. The results showed that for the lower dimensions (≤ 15 objects or ≤ 15 features) the indicator function produces more reliable results.Second, the classification results with SIMCA were analysed for data sets with two equally sized classes and a varying number of objects and features, using the recall function as the evaluation criterion. The results showed that the SIMCA classifier produces reliable results at the first classification level, even for a low object/feature ratio (5/300). However, at the second level the classification performance of SIMCA decreases rapidly with an increasing number of features, even when the data set consists of two very well separated classes and little random error.
    Additional Material: 6 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180010404
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  • 3
    Electronic Resource
    Electronic Resource
    An evaluation of SIMCA. Part 2  -  classification of pyrolysis mass spectra of pseudomonas and serratia bacteria by pattern recognition using the SIMCA classifier (1987)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 1 (1987), S. 231-241 
    Details
    ISSN: 0886-9383
    Keywords: Pattern recognition ; SIMCA ; PCA ; Classification ; Bacteria ; Pyrolysis mass spectra ; Recall ; Cross-validation ; Indicator function ; Pseudo random data sets ; Leave-x-out ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: As part of a critical evaluation of the pattern recognition method SIMCA, three data sets containing pyrolysis mass spectra from bacteria were analysed using the SIMCA classifier. Each set consisted of two classes, Pseudomonas and Serratia bacteria, each class containing ten mass spectra and each mass spectrum having 285 spectral features.The results indicate that for these py-MS data sets, with low object/feature ratio, the SIMCA classifier produces satisfactory results at the first classification level. At the second level, however, the classification results are not reliable, even after deleting outliers. A comparison of the cross-validation method and Malinowski's indicator function for the determination of the number of significant principal components showed that the cross-validation method is less stable and therefore less reliable than the indicator function.
    Additional Material: 11 Tab.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180010405
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  • 4
    Electronic Resource
    Electronic Resource
    The rational use of a dynamic on-line data processing system for high and low resolution mass spectrometry (1974)
    Chichester : Wiley-Blackwell
    Biological Mass Spectrometry 8 (1974), S. 303-316 
    Details
    ISSN: 0030-493X
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Some of the factors determining the configuration of a mass spectrometry data processing system are discussed. A rationalisation is given for a laboratory situation dealing with a variety of mass spectrometric problems. The effective use of a dynamic on-line system in such an environment is demonstrated. Conversational procedures are described for the acquisition, reduction and interpretation of mass spectral data, using a CRT visual display terminal as a fast man-computer interactive communication medium.
    Additional Material: 11 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/oms.1210080133
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