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Principal component analysis of Kováts indices for carbonyl compounds in capillary gas chromatography
Principal component analysis was performed on a data matrix consisting of Kováts indices of 35 aliphatic ketones and aldehydes. The calculations were carried out on the correlation matrices of Kováts indices. The Kováts indices were determined on capillary columns with four different stationary phas...
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Published in: | Journal of Chromatography A 1999-06, Vol.845 (1), p.21-31 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Principal component analysis was performed on a data matrix consisting of Kováts indices of 35 aliphatic ketones and aldehydes. The calculations were carried out on the correlation matrices of Kováts indices. The Kováts indices were determined on capillary columns with four different stationary phases, namely bonded methyl- (HP-1), methylphenyl- (HP-50), and trifluoropropylmethylsiloxane (DB-210), as well as polyethylene glycol (HP-Innowax) at four different temperatures. It was found that one principal component accounts for more than 94% of the total variance in the data, indicating that the temperature does not change the dominant pattern in the data. The physical meaning attributable to the principal components, thus the most influential ones are as follows: the first principal component accounts for the boiling point (and/or the molecular mass) of carbonyl compounds whereas the second is responsible for the temperature dependence. The plots of component loadings showed a characteristic pattern (counterclockwise increasing temperature) whereas that of component scores showed a triangular structure and some groupings of oxo compounds. Abstract retention data (free from influence of temperature and column polarity) are non-linear functions of boiling points of solutes. Similarity among the solutes from the point of view of retention is represented by characteristic plots. |
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ISSN: | 0021-9673 |
DOI: | 10.1016/S0021-9673(99)00323-4 |