Gas chromatographic behaviour of monosubstituted benzenes, benzaldehydes and acetophenones on OV polymethylphenylsilicone stationary phases

Hassani, Aïcha; Meklati, B. Y.

doi:10.1007/bf02276193

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…The data set of kovats retention indices was taken from the values reported by Hassani and Meklati [18]. The molecules in the data set include monosubstituted benzenes, o-, m-, p-substituted benzaldehydes and acetophenones.…”

Section: Data Setmentioning

confidence: 99%

Simultaneous Modeling of the Kovats Retention Indices on Phenyl OV Stationary Phases with Different Polarity using MLR and ANN

Ebrahimi

Hadjmohammadi

2006

QSAR Comb. Sci.

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A QSRR study was performed to develop a predictive model correlating the observed kovats retention indices of three congeneric aromatic series with their molecular structures in gas chromatography. The congeneric aromatic series included the substituted benzene, benzaldehyde and acethophenone compounds, which had been studied, previously, on six OV stationary phases with different phenyl percentages. At first, a model was generated for six columns separately, using only calculated descriptors and MLR technique. Then a combined model, added a polarity term of stationary phase (M), was also developed for all these columns, and the result was apparently satisfactory (R 2 ¼ 0.991, F ¼ 1009.828, SE ¼ 23.98). Since the intercept had a high value in this model, the neural network back propagation algorithm was applied for comparison, and it was found that the neural network could exceed the level of the multiple regression method. The stability and validity of both models were tested by cross-validation technique and by predicted response values for the prediction set and test set. The results of the study indicated that a seven parameter equation can be utilized for prediction of retention indices of compounds on different OV stationary phases simultaneously using ANN technique, for which there are no empirical RI values.

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Section: Data Setmentioning

confidence: 99%