Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network
Апстракт
A quantitative structure retention relationship technique using an artificial neural network (ANN) has been used for the prediction of the Lee retention indices for PAHs on SE-52 and DB-5 stationary phases. The selected descriptors that appear in the ANN model are the boiling point, molecular weight, connectivity index and the Schabron molecular size descriptor. The network was trained and optimized using a training and validation data sets. For the evaluation of the predictive power of the ANN, the optimized network was used to predict the temperature-prograrnmed Lee retention indices of two unseen testing data sets. The results obtained showed that the mean of relative errors and the correlation coefficients between the calculated ANN and the experimental values of Lee retention indices for the validation and two testing sets are 1.42% and 0.9460 on SE-52; 1.32% and 0.9381; 1.43% and 0.8939 on DB-5 stationary phases, respectively. These values are in good agreement with the relative ...error obtained by experiment. (c) 2006 Elsevier B.V. All rights reserved.
Кључне речи:
neural network / artificial retention indices / quantitative structure retention relationship / PAHsИзвор:
Journal of Chromatography A, 2006, 1108, 2, 279-284
DOI: 10.1016/j.chroma.2006.01.080
ISSN: 0021-9673
PubMed: 16464457
WoS: 000235854900021
Scopus: 2-s2.0-32844473018
Колекције
Институција/група
VinčaTY - JOUR AU - Skrbic, B AU - Onjia, Antonije E. PY - 2006 UR - https://vinar.vin.bg.ac.rs/handle/123456789/2987 AB - A quantitative structure retention relationship technique using an artificial neural network (ANN) has been used for the prediction of the Lee retention indices for PAHs on SE-52 and DB-5 stationary phases. The selected descriptors that appear in the ANN model are the boiling point, molecular weight, connectivity index and the Schabron molecular size descriptor. The network was trained and optimized using a training and validation data sets. For the evaluation of the predictive power of the ANN, the optimized network was used to predict the temperature-prograrnmed Lee retention indices of two unseen testing data sets. The results obtained showed that the mean of relative errors and the correlation coefficients between the calculated ANN and the experimental values of Lee retention indices for the validation and two testing sets are 1.42% and 0.9460 on SE-52; 1.32% and 0.9381; 1.43% and 0.8939 on DB-5 stationary phases, respectively. These values are in good agreement with the relative error obtained by experiment. (c) 2006 Elsevier B.V. All rights reserved. T2 - Journal of Chromatography A T1 - Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network VL - 1108 IS - 2 SP - 279 EP - 284 DO - 10.1016/j.chroma.2006.01.080 ER -
@article{ author = "Skrbic, B and Onjia, Antonije E.", year = "2006", abstract = "A quantitative structure retention relationship technique using an artificial neural network (ANN) has been used for the prediction of the Lee retention indices for PAHs on SE-52 and DB-5 stationary phases. The selected descriptors that appear in the ANN model are the boiling point, molecular weight, connectivity index and the Schabron molecular size descriptor. The network was trained and optimized using a training and validation data sets. For the evaluation of the predictive power of the ANN, the optimized network was used to predict the temperature-prograrnmed Lee retention indices of two unseen testing data sets. The results obtained showed that the mean of relative errors and the correlation coefficients between the calculated ANN and the experimental values of Lee retention indices for the validation and two testing sets are 1.42% and 0.9460 on SE-52; 1.32% and 0.9381; 1.43% and 0.8939 on DB-5 stationary phases, respectively. These values are in good agreement with the relative error obtained by experiment. (c) 2006 Elsevier B.V. All rights reserved.", journal = "Journal of Chromatography A", title = "Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network", volume = "1108", number = "2", pages = "279-284", doi = "10.1016/j.chroma.2006.01.080" }
Skrbic, B.,& Onjia, A. E.. (2006). Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network. in Journal of Chromatography A, 1108(2), 279-284. https://doi.org/10.1016/j.chroma.2006.01.080
Skrbic B, Onjia AE. Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network. in Journal of Chromatography A. 2006;1108(2):279-284. doi:10.1016/j.chroma.2006.01.080 .
Skrbic, B, Onjia, Antonije E., "Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network" in Journal of Chromatography A, 1108, no. 2 (2006):279-284, https://doi.org/10.1016/j.chroma.2006.01.080 . .