Quantitative relationships between molecular structures, environmental temperatures and solid vapor pressures of PCDD/Fs

Ding, Guanghui; Chen, Jingwen; Qiao, Xianliang; Huang, Liping; Lin, Jie; Chen, Xiaoyang

doi:10.1016/j.chemosphere.2005.04.110

Cited by 29 publications

(17 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PLS regression was adopted here to develop QSAR model, for this method can analyze data with strongly collinear, noisy and numerous predictor variables [9,13]. PLS regression was carried out using the Simca-S package (Umetrics AB, Sweden).…”

Section: Pls Methodsmentioning

confidence: 99%

A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

Liu

Cao

2015

Emerging Contaminants

View full text Add to dashboard Cite

Quantitative structure activity relationship (QSAR) Partial least square (PLS) Support vector machine (SVM) a b s t r a c t Quantitative structure-activity relationships (QSARs) were determined using partial least square (PLS) and support vector machine (SVM). The predicted values by the final QSAR models were in good agreement with the corresponding experimental values. Chemical estrogenic activities are related to atomic properties (atomic Sanderson electronegativities, van der Waals volumes and polarizabilities). Comparison of the results obtained from two models, the SVM method exhibited better overall performances. Besides, three PLS models were constructed for some specific families based on their chemical structures. These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.

show abstract

Section: Pls Methodsmentioning

confidence: 99%

A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

Liu

Cao

2015

Emerging Contaminants

View full text Add to dashboard Cite

show abstract

“…Nevertheless, it is found that most of the QSPR studies are limited to predicting vapor pressures at a constant temperature. Furthermore, P S values at different temperatures, not only at 25 • C, it is therefore desirable to determine or predict P S values at different temperatures [22].…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative structure-property relationship (QSPR) method provides a convenient tool to predict physicochemical properties of chemicals only from molecular structural information, and it may also provide insight into main factors that influence physicochemical properties of chemicals [22]. These QSPR parameters are usually obtained from on-line computation of the structure of the whole molecule using molecular mechanics or quantum mechanical methods.…”

Section: Introductionmentioning

confidence: 99%

Estimation of solid vapor pressures of pure compounds at different temperatures using a multilayer network with particle swarm algorithm

Lazzús¹

2010

Fluid Phase Equilibria

View full text Add to dashboard Cite

“…This process was repeated until only two variables were left. The model with the highest Q 2 cum was selected as the optimal model from all the models obtained [14].…”

Section: Descriptors and Statistical Analysismentioning

confidence: 99%

“…The quantitative structure-property relationship (QSPR) method provides a convenient tool to predict physicochemical properties of chemicals from only molecular structural information, and this may also provide insight into the main factors that influence physicochemical properties of chemicals [14][15][16][17]. R air was influenced by the molecular weight, the substitution patterns of chlorine in PCB, and K oa of POP [2,10,11,18].…”

mentioning

confidence: 99%

QSPR study about sampling rates of semipermeable membrane devices for monitoring of organochlorine pesticides in Alps air

et al. 2011

Self Cite

View full text Add to dashboard Cite

Organochlorine pesticides (OCP) were monitored at the Weissfluhjoch site (Switzerland) and the top of Mt. Sonnblick (Austria) with a low volume active air sampler and semipermeable membrane devices (SPMD). The air sampling rates (R air ) of the SPMD for OCP were calculated. Statistical tests showed that there was no significant difference between R air at the two different sampling sites. Quantitative structure-property relationship (QSPR) models of the R air of the SPMD were developed for OCP using partial least square (PLS) regression. Quantum chemical descriptors computed by the semi-empirical PM6 method were used as predictor variables. The cumulative variance of the dependent variable explained by the PLS components and determined by cross-validation (Q 2 cum ) was >0.818 for each optimal model. This indicates that the model has good predictive ability and robustness. The R air of the SPMD for OCP is related to the total energy, the van der Waals area and the total dipole moment of the OCP molecules. The main factors governing R air values of OCP are intermolecular interactions and the energy required for cavity-formation in dissolution of OCP into triolein of the SPMD. The linear correlation coefficient between predicted and experimental values were all >0.921. air sampling rate, semipermeable membrane device, organochlorine pesticides, quantitative structure-property relationship Citation:Zhu X H, Ding G H, Levy W, et al. QSPR study about sampling rates of semipermeable membrane devices for monitoring of organochlorine pesticides in Alps air.

show abstract

Quantitative relationships between molecular structures, environmental temperatures and solid vapor pressures of PCDD/Fs

Cited by 29 publications

References 39 publications

A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

Estimation of solid vapor pressures of pure compounds at different temperatures using a multilayer network with particle swarm algorithm

QSPR study about sampling rates of semipermeable membrane devices for monitoring of organochlorine pesticides in Alps air

Contact Info

Product

Resources

About