Estimation of Water Solubility of Polycyclic Aromatic Hydrocarbons Using Quantum Chemical Descriptors and Partial Least Squares

2007 ◽  
Vol 27 (5) ◽  
pp. 618-626 ◽  
Author(s):  
Gui-Ning Lu ◽  
Zhi Dang ◽  
Xue-Qin Tao ◽  
Chen Yang ◽  
Xiao-Yun Yi
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Least Squares ◽  
Partial Least Squares ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Water Solubility ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Chemical Descriptors

scholarly journals Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

2008 ◽  
Vol 6 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Gui-Ning Lu ◽  
Xue-Qin Tao ◽  
Zhi Dang ◽  
Xiao-Yun Yi ◽  
Chen Yang
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Density Functional ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

ESTIMATION OF SOIL SORPTION COEFFICIENTS OF POLYCYCLIC AROMATIC HYDROCARBONS BY QUANTUM CHEMICAL DESCRIPTORS

2008 ◽  
Vol 07 (01) ◽  
pp. 67-79 ◽  
Author(s):  
GUI-NING LU ◽  
CHEN YANG ◽  
XUE-QIN TAO ◽  
XIAO-YUN YI ◽  
ZHI DANG
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Soil Sorption ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Soils And Sediments ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic soil sorption coefficients (log K OC ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed using density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for log K OC of PAHs. The correlation coefficient of the optimal model was 0.993, and the results of a cross-validation test ([Formula: see text]) showed this optimal model had high fitting precision and good predicting ability. The log K OC values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent tend to more easily adsorb and accumulate in soils and sediments, whereas those with higher molecular total energy and larger energy gap between the lowest unoccupied and the highest occupied molecular orbital adsorb and accumulate in soils and sediments less readily.

QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIPS ON DISSOLVABILITY OF PCDD/Fs USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

2010 ◽  
Vol 09 (supp01) ◽  
pp. 9-22 ◽  
Author(s):  
GUI-NING LU ◽  
XUE-QIN TAO ◽  
ZHI DANG ◽  
WEILIN HUANG ◽  
ZHONG LI
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Quantum Chemical ◽  
Good Prediction ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantum Chemical Descriptors ◽  
Validation Test ◽  
Pls Analysis ◽  
Chemical Descriptors

The environmental fate of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) has become a major issue in recent decades. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, QSPR models were established for estimating water solubility (- log S W ) and n-octanol/water partition coefficient ( log KOW) of PCDD/Fs. Quantum chemical descriptors computed with density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for - log S W and log K OW of PCDD/Fs. Optimized models with high correlation coefficients (R2 > 0.983) were obtained for estimating - log S W and log K OW of PCDD/Fs. Both the internal cross validation test [Formula: see text] and external validation test (R2 > 0.965) results showed that the obtained models had high-precision and good prediction capability. The - log S W } and log K OW values predicted by the obtained models are very close to those observed. The PLS analysis indicated that PCDD/Fs with larger electronic spatial extent (R e ), lower molecular total energy (E T ), and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals (E LUMO -E HOMO ) tend to be less soluble in water but more lipophilic.

scholarly journals Application of Partial Least-Squares Calibration to Phosphorimetric Data for Determination of Polycyclic Aromatic Hydrocarbons in Spiked Environmental Samples

2000 ◽  
Vol 83 (2) ◽  
pp. 391-398
Author(s):  
A Segura Carretero ◽  
M Martinez Galera ◽  
C Cruces Blanco ◽  
M D Gil García ◽  
A Fernández Gutiérrez ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Least Squares ◽  
Partial Least Squares ◽  
Aromatic Hydrocarbons ◽  
Road Dust ◽  
Calibration Method ◽  
Ternary Mixtures ◽  
Relative Standard ◽  
Polycyclic Aromatic

Abstract A partial least-squares calibration method is proposed, for the first time, for phosphorescence signals. The proposed method is based on the determination of phenanthrene, fluoranthene, and benz[a]anthracene by room temperature phosphorimetry, using microemulsion solutions. The emission and first-derivative emission spectra of the ternary mixtures were tested to perform the calibration matrix. Improved recoveries were found for the prior differentiation step in the analysis of ternary mixtures of these polycyclic aromatic hydrocarbons in road dust samples. The proposed method yielded recoveries ranging from 93.2 to 115.3%, with relative standard deviations of < 6.8%.

QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY

2008 ◽  
Vol 16 (02) ◽  
pp. 279-293 ◽  
Author(s):  
CHANIN NANTASENAMAT ◽  
THEERAPHON PIACHAM ◽  
TANAWUT TANTIMONGCOLWAT ◽  
THANAKORN NAENNA ◽  
CHARTCHALERM ISARANKURA-NA-AYUDHYA ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Quantum Chemical ◽  
Partial Least Squares Regression ◽  
Homoserine Lactone ◽  
Acyl Homoserine Lactone ◽  
Least Squares Regression ◽  
Quantum Chemical Descriptors ◽  
Data Set ◽  
Chemical Descriptors

A quantitative structure-activity relationship (QSAR) study was performed to model the lactonolysis activity of N-acyl-homoserine lactone lactonase. A data set comprising of 20 homoserine lactones and related compounds was taken from the work of Wang et al. Quantum chemical descriptors were calculated using the semiempirical AM1 method. Partial least squares regression was utilized to construct a predictive model. This computational approach reliably reproduced the lactonolysis activity with high accuracy as illustrated by the correlation coefficient in excess of 0.9. It is demonstrated that the combined use of quantum chemical descriptors with partial least squares regression are suitable for modeling the AHL lactonolysis activity.

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