Investigation of Retention Behaviors of Essential Oils by Using QSRR

Noorizadeh, Hadi; Farmany, Abbas; Khosravi, Afra

doi:10.1002/jccs.201000137

Cited by 16 publications

(18 citation statements)

References 31 publications

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“…A detailed description of the GA can be found in the literature 20–22. GA is a simulated method based on ideas from Darwin's theory of natural selection and evolution (the struggle for life).…”

Section: Methodsmentioning

confidence: 99%

Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods

Noorizadeh

Farmany

2011

Drug Testing and Analysis

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The quantitative structure-property relationship (QSPR) of drug molecules against the immobilized liposome chromatography partitioning (log K(s)) was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions (e.g. partial least squares (PLS)) as well as the non-linear regressions (e.g. the kernel PLS (KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN)) were utilized to construct the linear and non-linear QSPR models. The correlation coefficient cross validation (Q(2)) and relative error for calibration, prediction and test sets L-M ANN model are (0.987, 0.971, 0.952) and (3.14, 3.54, 6.61), respectively. The obtained results using L-M ANN were compared with those of GA-PLS and GA-KPLS, exhibiting that the L-M ANN model demonstrated a better performance than that of the other models. This is the first research on the QSPR of the drug molecules against the log K(s) using the GA-KPLS and L-M ANN.

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Section: Methodsmentioning

confidence: 99%

Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods

Noorizadeh

Farmany

2011

Drug Testing and Analysis

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“…29 However, this assumption is in many cases incorrect and can be that exist the lack of the correlation between the high Q 2 and the high predictive ability of QSPR/QSRR models has been established and corroborated recently. 27 Thus, the high value of Q 2 appears to be necessary but not sufficient condition for the models to have a high predictive power. These authors stated that Probability of mutation 1%…”

Section: Cross Validation Techniquementioning

confidence: 99%

“…27 The accuracy of proposed models was illustrated using the evaluation techniques such as leave-group-out cross validation (LGO-CV) procedure and validation through an external test set.…”

Section: Model Validationmentioning

confidence: 99%

Application of GA-PLS and GA-KPLS calculations for the prediction of the retention indices of essential oils

Noorizadeh¹,

Farmany²,

Noorizadeh³

2011

Quím. Nova

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Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 117 diverse compounds in essential oils from 5 Pimpinella species gathered from central Turkey which were obtained by gas chromatography and gas chromatography-mass spectrometry. The square correlation coefficient leave-group-out cross validation (LGO-CV) (Q 2 ) between experimental and predicted RI for training set by GA-PLS and GA-KPLS was 0.940 and 0.963, respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure-retention relationship (QSRR) studies.

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“…A literature survey reveals that prediction on retention indices of the oil constituents have been reported, frequently 29,[39][40][41][42] . Some of the most popular ways to model this parameter have been summarized in Table 9.…”

Section: Comparison Of the Model With Similar Reportsmentioning

confidence: 99%

Exploring Novel QSRRs for Simulation of Gas Chromatographic Retention Indices of Diverse Sets of Terpenoids inPistacia LentiscusL. Essential Oil Using Stepwise and Genetic Algorithm Multiple Linear Regressions

Mohammadhosseini

Deeb

Alavi-Gharabagh

et al. 2012

Analytical Chemistry Letters

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The present work describes convenient and interpretable models by step-wise multiple linear regression (SW-MLR) and genetic algorithm-multiple linear regression (GA-MLR). These quantitative structure-retention relationship (QSRR) based strategies have been successfully used to predict the retention indices (RIs) of a series of natural compounds found in the essential oil of Pistacia lentiscus L. The dataset was divided into training set (51 compounds) and test set (25 compounds), randomly. The prediction capabilities of both approaches have been appraised by their exertion to test set compounds. The fitness statistics of the models were assessed to be satisfactory which resulted in accurate predictions. The best optimized models for SW-MLR and GA-MLR consisted 5 (X0, Qtot, Dv, HATS3e and MATS4e) and 6 (MATS2e, DELS, ATS4e, PW5, PCD and W) molecular descriptors, respectively. Our investigation revealed the superiority of SW-MLR (R 2 =0.96, Q 2 LOO =0.95 and Q 2 LGO =0.94 for training set; REP=3.7 for test set) standpoint against the best GA-MLR model (R 2 =0.955, Q 2 LOO =0.937 and Q 2 LGO =0.934 for training set; REP=7.5 for test set) for estimation of RIs of similar or unknown compounds.

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Investigation of Retention Behaviors of Essential Oils by Using QSRR

Cited by 16 publications

References 31 publications

Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods

Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods

Application of GA-PLS and GA-KPLS calculations for the prediction of the retention indices of essential oils

Exploring Novel QSRRs for Simulation of Gas Chromatographic Retention Indices of Diverse Sets of Terpenoids inPistacia LentiscusL. Essential Oil Using Stepwise and Genetic Algorithm Multiple Linear Regressions

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