Quantitative structure-retention relationships analysis of retention index of essential oils

Noorizadeh, Hadi; Farmany, Abbas; Noorizadeh, Mehrab

doi:10.1590/s0100-40422011000200014

Cited by 26 publications

(15 citation statements)

References 21 publications

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“…[14] In the present study, we were able to isolate over 50 [20,23] 1896, [16] 1898 [19] 1926 1921, [18] 1925, [23] 1935, [19] 1946 [18] 2-Phenylethanol Mean and standard deviation were calculated with the experiments of six central points (7, 10, 12 and 16-18) performed under the same conditions.…”

Section: Composition Analysis Of Volatile Compoundsmentioning

confidence: 68%

“…1683 1677, [17] 1687, [18,19] 1704 [18] Diethyl butanedioate 37.63 MS+LRI 2179 2152 [20] Nonanoic acid 44.74 MS+Std 1845 1839, [20] 1850, [17] 1856 [18] [20] 2270, [18] 2298, [23] 2314 [18] Decanoic acid 228.04 MS+LRI 2053 2054, [17] 2070 [18] Ethyl tetradecanoate 495.65 MS+LRI 2461 2448 [20] Benzoic acid 289.82 ms 2165 2151, [17] 2179 [18] Ethyl pentadecanoate 188.93 ms 2712 2713 [23] Tetradecanoic acid 162.02 MS+Std 2281 2261, [17] 2288 [18] Ethyl [25] 3184 [26] (9Z)-9-Octadecenoic acid 20415.09 MS+Std 2475 2483 [16] Ethyl octadecanoate 3781.82 MS+Std 3232 3157, [23] 3193 [23] (9Z,12Z [17] 2574 [16] Ethyl (9Z,12Z)-9, 12-octadecadienoate 18394.13 MS+Std…”

Section: Ms+lrimentioning

confidence: 99%

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Optimization by response surface methodology of supercritical carbon dioxide extraction of flavour compounds from Chinese liquor vinasse

Tao

et al. 2015

Flavour & Fragrance J

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Liquor vinasse is of interest under the biorefinery context and the aim of the present study was the recovery of flavour compounds. Supercritical carbon dioxide (SC-CO 2 ) extraction is an optimal method for liquor vinasse flavour compounds recovery. Gas chromatography with mass spectrometry analysis showed that more than 50 compounds were identified in the SC-CO 2 extract. The predominant compounds were esters and acids, and ethyl (9Z)-9-octadecenoate was the major ester. The effects of different extraction parameters, including pressure (8.2-41.8 MPa), temperature (32.8-67.2°C) and CO 2 rate (6.4-23.6 l/h), on the yield of volatile compounds with SC-CO 2 extraction were furthermore investigated. The most efficient extraction parameters for the maximum flavour compounds yield and better selection for ethyl (9Z)-9-octadecenoate were the following: pressure of 24.98 MPa, temperature of 51.28°C and a CO 2 rate of 13.48 l/h.

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Section: Composition Analysis Of Volatile Compoundsmentioning

confidence: 68%

Section: Ms+lrimentioning

confidence: 99%

Optimization by response surface methodology of supercritical carbon dioxide extraction of flavour compounds from Chinese liquor vinasse

Tao

et al. 2015

Flavour & Fragrance J

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“…An artificial neural network (ANN) with a layered structure is a mathematical system that stimulates biological neural network, consisting of computing units named neurons and connections between neurons named synapses. [29][30][31] All feed-forward ANN used in this paper are three-layer networks. Each neuron in any layer is fully connected with the neurons of a succeeding layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography

Noorizadeh

Sobhanardakani

Raoofi

et al. 2011

Drug Testing and Analysis

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Genetic algorithm and partial least square (GA-PLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention time and descriptors for drug metabolites which obtained by two-dimensional liquid chromatography. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of four models. Both methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R(2) value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA-PLS models.

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“…Liao et al 10 reported an MLR model for predicting retention indices of 106 oxygen-containing organic compounds using the hydrogen-association classified molecular electronegativity-distance vector (H-MEDV) descriptors. Noorizadeh and Fermany, 11 and Noorizadeh et al 12,13 built several QSRR models: (GA-MLR, GA-PLS, Kernel PLS and the Levenberg-Marquardt artificial neural network (L-M ANN)) for the retention indices of essential oils. The GA-MLR model for the prediction of the retention indices of 32 compounds was investigated by Azar et al 14 Qin et al 7 developed a QSRR model (based on the MLR method) for the prediction of the retention indices of 169 compounds in essential oils.…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae)

Driouche

Messadi

2019

J Serb Chem Soc

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In this paper, a quantitative structure-retention relationship (QSRR) model was developed for predicting the retention indices (log RI) of 36 constituents of essential oils. First, the chemical structure of each compound was sketched using HyperChem software. Then, molecular descriptors covering different information of molecular structures were calculated by Dragon software. The results illustrated that linear techniques, such as multiple linear regression (MLR), combined with a successful variable selection procedure are capable of generating an efficient QSRR model for predicting the retention indices of different compounds. This model, with high statistical significance (R 2 = 0.9781, Q 2 LOO = 0.9691, Q 2 ext = 0.9546, Q 2 L(5)O = 0.9667, F = 245.27), could be used adequately for the prediction and description of the retention indices of other essential oil compounds. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-5-out cross-validation, bootstrap, randomization test and validation through the test set.

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Quantitative structure-retention relationships analysis of retention index of essential oils

Cited by 26 publications

References 21 publications

Optimization by response surface methodology of supercritical carbon dioxide extraction of flavour compounds from Chinese liquor vinasse

Optimization by response surface methodology of supercritical carbon dioxide extraction of flavour compounds from Chinese liquor vinasse

Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography

Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae)

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