Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network
Samo za registrovane korisnike
2006
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
neural network / artificial retention indices / quantitative structure retention relationship / PAHsIzvor:
Journal of Chromatography A, 2006, 1108, 2, 279-284Izdavač:
- Elsevier, Amsterdam
DOI: 10.1016/j.chroma.2006.01.080
ISSN: 0021-9673
PubMed: 16464457
WoS: 000235854900021
Scopus: 2-s2.0-32844473018
Institucija/grupa
Tehnološko-metalurški fakultetTY - JOUR AU - Skrbić, Biljana AU - Onjia, Antonije PY - 2006 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/905 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. PB - Elsevier, Amsterdam T2 - Journal of Chromatography A T1 - Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network EP - 284 IS - 2 SP - 279 VL - 1108 DO - 10.1016/j.chroma.2006.01.080 ER -
@article{ author = "Skrbić, Biljana and Onjia, Antonije", 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.", publisher = "Elsevier, Amsterdam", journal = "Journal of Chromatography A", title = "Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network", pages = "284-279", number = "2", volume = "1108", doi = "10.1016/j.chroma.2006.01.080" }
Skrbić, B.,& Onjia, A.. (2006). Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network. in Journal of Chromatography A Elsevier, Amsterdam., 1108(2), 279-284. https://doi.org/10.1016/j.chroma.2006.01.080
Skrbić B, Onjia A. 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 .
Skrbić, Biljana, Onjia, Antonije, "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 . .