QSRR Models to Predict Retention Indices of Cyclic Compounds of Essential Oils

Noorizadeh, Hadi; Farmany, Abbas

doi:10.1365/s10337-010-1660-4

Cited by 29 publications

(16 citation statements)

References 22 publications

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“…The set of optimal parameter values were listed in Table 2 and the predicting results of test set on OV-1 and SE-54 were listed in Figures 1 and 2. It can be seen from the figures that the predictive values of gas chromatography retention index of oxygen-organic compounds applications of ANN have been adequately described elsewhere [7][8][9][10]. Extensive use of ANN, which has inherent ability to incorporate nonlinear and cross-product terms into the model and does not require prior knowledge of the mathematical function as well, largely rests on its flexibility and less sensitivity to collinearity among variables.…”

Section: Modelsmentioning

confidence: 99%

Study on Quantitative Structure-Retention Relationships (QSRR) for Oxygen-Containing Organic Compounds Based on Gene Expression Programming (GEP)

Zhang¹,

Shi²,

Ding³

et al. 2015

J Chromatogr Sep Tech

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Section: Modelsmentioning

confidence: 99%

Study on Quantitative Structure-Retention Relationships (QSRR) for Oxygen-Containing Organic Compounds Based on Gene Expression Programming (GEP)

Zhang¹,

Shi²,

Ding³

et al. 2015

J Chromatogr Sep Tech

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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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“…A number of reports, deals with QSRR retention indices calculation of several compounds have been published in the literature. [11][12][13] The QSRR models apply to partial least squares(PLS) method often combined with genetic algorithms (GA) for feature selection. 14,15 Because of the complexity of relationships between the property of molecules and structures, nonlinear models are also used to model the structure-property relationships.…”

Section: Introductionmentioning

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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QSRR Models to Predict Retention Indices of Cyclic Compounds of Essential Oils

Cited by 29 publications

References 22 publications

Study on Quantitative Structure-Retention Relationships (QSRR) for Oxygen-Containing Organic Compounds Based on Gene Expression Programming (GEP)

Study on Quantitative Structure-Retention Relationships (QSRR) for Oxygen-Containing Organic Compounds Based on Gene Expression Programming (GEP)

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

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

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