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Cited by 24 publications
(15 citation statements)
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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%
See 1 more Smart Citation
“…[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%
“…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%
“…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%