Metabolic abnormalities associated with diabetes mellitus, as investigated by gas chromatography and pattern-recognition analysis of profiles of volatile metabolites
Patterns of volatile metabolites in urine, as obtained by glass-capillary gas chromatography, were investigated by use of a nonparametric pattern-recognition method, in an effort to detect abnormalities associated with diabetes. We used threshold logic unit analysis on a data set consisting of norma...
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| Published in | Clinical chemistry (Baltimore, Md.) Vol. 27; no. 4; pp. 580 - 585 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Am Assoc Clin Chem
01.04.1981
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-9147 1530-8561 |
| DOI | 10.1093/clinchem/27.4.580 |
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| Abstract | Patterns of volatile metabolites in urine, as obtained by glass-capillary gas chromatography, were investigated by use of a nonparametric pattern-recognition method, in an effort to detect abnormalities associated with diabetes. We used threshold logic unit analysis on a data set consisting of normal subjects and those with diabetes mellitus, and could predict patterns for volatile metabolites as belonging to the proper class in 94.83% of the cases examined. In addition, a feature-extraction algorithm isolated those volatile constituents that are most useful in making the normal/diabetic classification. We used gas chromatography/mass spectrometry to identify important profile constituents. Finally, these same pattern-recognition methods indicated strong sex-related patterns in these volatiles. |
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| AbstractList | Patterns of volatile metabolites in urine, as obtained by glass-capillary gas chromatography, were investigated by use of a nonparametric pattern-recognition method, in an effort to detect abnormalities associated with diabetes. We used threshold logic unit analysis on a data set consisting of normal subjects and those with diabetes mellitus, and could predict patterns for volatile metabolites as belonging to the proper class in 94.83% of the cases examined. In addition, a feature-extraction algorithm isolated those volatile constituents that are most useful in making the normal/diabetic classification. We used gas chromatography/mass spectrometry to identify important profile constituents. Finally, these same pattern-recognition methods indicated strong sex-related patterns in these volatiles. Patterns of volatile metabolites in urine, as obtained by glass-capillary gas chromatography, were investigated by use of a nonparametric pattern-recognition method, in an effort to detect abnormalities associated with diabetes. We used threshold logic unit analysis on a data set consisting of normal subjects and those with diabetes mellitus, and could predict patterns for volatile metabolites as belonging to the proper class in 94.83% of the cases examined. In addition, a feature-extraction algorithm isolated those volatile constituents that are most useful in making the normal/diabetic classification. We used gas chromatography/mass spectrometry to identify important profile constituents. Finally, these same pattern-recognition methods indicated strong sex-related patterns in these volatiles.Patterns of volatile metabolites in urine, as obtained by glass-capillary gas chromatography, were investigated by use of a nonparametric pattern-recognition method, in an effort to detect abnormalities associated with diabetes. We used threshold logic unit analysis on a data set consisting of normal subjects and those with diabetes mellitus, and could predict patterns for volatile metabolites as belonging to the proper class in 94.83% of the cases examined. In addition, a feature-extraction algorithm isolated those volatile constituents that are most useful in making the normal/diabetic classification. We used gas chromatography/mass spectrometry to identify important profile constituents. Finally, these same pattern-recognition methods indicated strong sex-related patterns in these volatiles. |
| Author | Miller, M Rhodes, G McConnell, ML Novotny, M |
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| SubjectTerms | Chromatography, Gas - methods Diabetes Mellitus - urine Female Gas Chromatography-Mass Spectrometry - methods Humans Male Pattern Recognition, Automated Sex Factors |
| Title | Metabolic abnormalities associated with diabetes mellitus, as investigated by gas chromatography and pattern-recognition analysis of profiles of volatile metabolites |
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