QSPR study of GC retention indices for saturated esters on seven stationary phases based on novel topological indices

Fengping, Liu; Liang, Yi‐Zeng; Cao, Chenzhong; Zhou, Neng

doi:10.1016/j.talanta.2007.01.038

Cited by 57 publications

(33 citation statements)

References 24 publications

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“…For esters the results obtained by single linear regression using the I SET are better than those reported by Lu et al (Lu et al, 2006). For SE-30 and OV-7 stationary phases the results are also better than those found by Liu et al (Liu et al, 2007) and for more polar stationary phases the statistical parameters differ only slightly. Both of these studies use multiple linear regression (MLR) between RI and the topological indices for 90 saturated esters on stationary phases with different polarities.…”

Section: Development Of Qspr Models Using the I Setcontrasting

confidence: 57%

Molecular Interactions in Chromatographic Retention: A Tool for QSRR/QSPR/QSAR Studies

Heinzen¹,

Junkes²,

Kuhnen³

et al. 2012

Molecular Interactions

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Section: Development Of Qspr Models Using the I Setcontrasting

confidence: 57%

Molecular Interactions in Chromatographic Retention: A Tool for QSRR/QSPR/QSAR Studies

Heinzen¹,

Junkes²,

Kuhnen³

et al. 2012

Molecular Interactions

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“…For the purpose of prediction and to obtain insights regarding the mechanism of chromatographic retention, correlations between retention indices on stationary phases having different polarities and molecular descriptors have been investigated [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure–retention relationship modelling of esters on stationary phases of different polarity

Souza

Kuhnen

Junkes

et al. 2009

Journal of Molecular Graphics and Modelling

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“…In QSRR studies, the volatile compounds can be presented by molecular descriptors. [31][32][33] At present, some types of descriptors such as spatial, structural, conformational, electronic, thermodynamic and topological descriptors have been proposed to describe the structural features of molecules. The main aim of QSRR is to predict retention indices or related data.…”

Section: Introductionmentioning

confidence: 99%

QSRR Study of GC Retention Indices of Volatile Compounds Emitted from Mosla chinensis Maxim by Multiple Linear Regression

Chen

2011

Chin. J. Chem.

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The volatile compounds emitted from Mosla chinensis Maxim were analyzed by headspace solid-phase microextraction (HS-SPME) and headspace liquid-phase microextraction (HS-LPME) combined with gas chromatography-mass spectrometry (GC-MS). The main volatiles from Mosla chinensis Maxim were studied in this paper. It can be seen that 61 compounds were separated and identified. Forty-nine volatile compounds were identified by SPME method, mainly including myrcene, α-terpinene, p-cymene, (E)-ocimene, thymol, thymol acetate and (E)-β-farnesene. Forty-five major volatile compounds were identified by LPME method, including α-thujene, α-pinene, camphene, butanoic acid, 2-methylpropyl ester, myrcene, butanoic acid, butyl ester, α-terpinene, p-cymene, (E)-ocimene, butane, 1,1-dibutoxy-, thymol, thymol acetate and (E)-β-farnesene. After analyzing the volatile compounds, multiple linear regression (MLR) method was used for building the regression model. Then the quantitative structure-retention relationship (QSRR) model was validated by predictive-ability test. The prediction results were in good agreement with the experimental values. The results demonstrated that headspace SPME-GC-MS and LPME-GC-MS are the simple, rapid and easy sample enrichment technique suitable for analysis of volatile compounds. This investigation provided an effective method for predicting the retention indices of new compounds even in the absence of the standard candidates.

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QSPR study of GC retention indices for saturated esters on seven stationary phases based on novel topological indices

Cited by 57 publications

References 24 publications

Molecular Interactions in Chromatographic Retention: A Tool for QSRR/QSPR/QSAR Studies

Molecular Interactions in Chromatographic Retention: A Tool for QSRR/QSPR/QSAR Studies

Quantitative structure–retention relationship modelling of esters on stationary phases of different polarity

QSRR Study of GC Retention Indices of Volatile Compounds Emitted from Mosla chinensis Maxim by Multiple Linear Regression

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