Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation.This...Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation.This approach involves developing models for predicting the retention time(RT)of analytes,thereby accelerating method development and facilitating compound identification.In addition,QSRR can be used to study compound retention mechanisms and support drug screening efforts.This review provides a comprehensive analysis of QSRR workflows and applications,with a special focus on the role of artificial intelligence-an area not thoroughly explored in previous reviews.Moreover,we discuss current limitations in RT prediction and propose promising solutions.Overall,this review offers a fresh perspective on future QSRR research,encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.展开更多
In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a ...In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.展开更多
Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative struc...Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.展开更多
An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligon...An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.展开更多
Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hund...Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.展开更多
The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR...The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.展开更多
Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecu...Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecular electronegativity-distance vector（I-MEDV） was developed. It was used to describe the structures of 37 compounds of styrax japonicus sieb flowers. Through multiple linear regression（MLR）,a QSRR model was built up. The correlation coefficient（R1） of the model was 0.980. Then,4 vectors were selected to build another model through the method of stepwise multiple regression（SMR） ,and the correlation coefficient（R2） of the model was 0.975. Moreover,all the two models were evaluated by performing the crossvalidation with the leave-one-out（LOO） procedure and the correlation coefficients（Rcv） were 0.948 and 0.968,respectively. The results show that the I-MEDV could successfully describe the structures of organic compounds. The stability and predictability of the models were good.展开更多
A molecular structural characterization （MSC） method called reduced molecular electronegativity-distance vector （MEDVR） was used to describe the molecular structures of 55 components of meconopsis integrifolia flo...A molecular structural characterization （MSC） method called reduced molecular electronegativity-distance vector （MEDVR） was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression （SMR） and partial least square （PLS） methods, a model with the correlation coefficient （R1） of 0.987 and the standard deviation （SD1） of 1.377 could be obtained. Then through multiple linear regression （MLR）, another model with the correlation coefficient （R2） of 0.989 and standard deviation （SD2） of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique （SMR）, 8 vectors were selected to build up another model with its correlation coefficient （R3） and standard deviation （SD3） of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out （LOO） procedure, and the correlation coefficients （QCV） were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.展开更多
Sixteen indole derivatives have been computed at B3LYP/6-31 IG^＊＊ level using density functional theory （DFF）. Based on linear solvation energy theory, the structural parameters were employed to present correlatio...Sixteen indole derivatives have been computed at B3LYP/6-31 IG^＊＊ level using density functional theory （DFF）. Based on linear solvation energy theory, the structural parameters were employed to present correlation between the parameters of chromatograph capacity factor （CCF） and molecular structural parameters. As a result, the correlation equation of the reversed phased high performance liquid chromatograph capacity factor to the intercept lgk＇w and slope S of CCF were obtained, from which the correlation coefficients of lgk＇w to the structural parameters are r^2 = 0.9596 and q^2 = 0.9262. While the correlation coefficients of the parameter S r^2 q^2 with structures are = 0.9750 and = 0.9252. Moreover, the effect of water as solvent on the present two models was also considered using SCRF method, and the result shows that the predicting capacity of correlation equation of lgkw＇ increases, while that of the model for S decreases slightly. Both two correlation equations achieved in this work are more advantageous than those using theoretical descriptors from molecular connectivity indices.展开更多
A new molecular structural characterization（MSC） method was constructed in this paper.The structure descriptors were used to describe the structures of 149 compounds.Through multiple linear regression（MLR） and ste...A new molecular structural characterization（MSC） method was constructed in this paper.The structure descriptors were used to describe the structures of 149 compounds.Through multiple linear regression（MLR） and stepwise multiple regression（SMR）,a quantitative structure-retention relationship（QSRR） model with 6 variables was obtained.The correlation coefficient（R） of the model was 0.944.Through partial least-squares regression（PLS）,another QSRR model with 5 principal components was obtained.The correlation coefficient（R） of the model was 0.941.The estimation stability and prediction ability of the two models was strictly analyzed by both internal and external validations.For the internal validation,the Cross-Validation（CV） correlation coefficients（RCV） for Leave-One-Out（LOO） were 0.931 and 0.932,respectively.For the external validation,the correlation coefficients（Rtest） of the two models were 0.907 and 0.932.The results suggested good stability and predictability of the model.The prediction results are in very good agreement with the experimental values.This paper provided a new and effective method for predicting the chromatography retention time.展开更多
Polychlorinated dibenzothiophenes(PCDTs)and their corresponding sulfone(PCDTO2)compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations...Polychlorinated dibenzothiophenes(PCDTs)and their corresponding sulfone(PCDTO2)compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations of electrostatic potentials(ESPs)on molecular surface have been performed for all 135 PCDTs and 135 PCDTO2 congeners at the HF/6-31G*level of theory.A number of statistically-based parameters have been extracted.Linear relationship between gas-chromatographic retention index(RI)and the structural descriptors have been established by multiple linear regression.The result shows that two descriptors derived from positive electrostatic potential on molecular surface,■andπ,together with the molecular volume(Vmc)and the energy of the lowest unoccupied molecular orbital(ELUMO)can be well used to express the quantitative structure-retention relationship(QSRR)of PCDTs and PCDTO2.Predictive capability of the two models has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient(RCV)of 0.996 and 0.997,respectively.Furthermore,the predictive power of the models is further examined for the external test set.Correlation coefficients(R)between the observed and predicted RI values for the external test set are 0.997 and0.998,respectively,validating the robustness and good prediction of our model.The QSRR model established may provide again a powerful method for predicting chromatographic properties of aromatic organosulfur compounds.展开更多
Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 ...Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 PCDTs congeners at the B3LYP/6-31G＊ level of theory.By means of the VSMP（variable selection and modeling based on prediction） program,one optimal descriptor（molecular polarizability,α） was selected to develop a QSRR model for the prediction of gas chromatographic retention indices（GC-RI） of PCDTs.The estimated correlation coefficients（r2） and LOO-validated correlation coefficients（q2）,all more than 0.99,were built by multiple linear regression,which shows a good estimation ability and stability of the models.A prediction power for the external samples was validated by the model built from the training set with 17 polychlorinated dibenzothiophenes.展开更多
The volatile compounds emitted from Mosla chinensis Maxim were analyzed by headspace solid-phase micro- extraction （HS-SPME） and headspace liquid-phase microextraction （HS-LPME） combined with gas chromatography-ma...The volatile compounds emitted from Mosla chinensis Maxim were analyzed by headspace solid-phase micro- extraction （HS-SPME） and headspace liquid-phase microextraction （HS-LPME） combined with gas chromatography-mass spectrometry （GC-MS）. The main volatiles from Mosla chinensis Maxim were studied in this paper. It can be seen that 61 compounds were separated and identified. Forty-nine volatile compounds were identified by SPME method, mainly including myrcene, a-terpinene, p-cymene, （E）-ocimene, thymol, thymol acetate and （E）-fl-farnesene. Forty-five major volatile compounds were identified by LPME method, including a-thujene, a-pinene, camphene, butanoic acid, 2-methylpropyl ester, myrcene, butanoic acid, butyl ester, a-terpinene, p-cymene, （E）-ocimene, butane, 1,1-dibutoxy-, thymol, thymol acetate and （E）-fl-farnesene. After analyzing the volatile compounds, multiple linear regression （MLR） method was used for building the regression model. Then the quantitative structure-retention relationship （QSRR） model was validated by predictive-ability test. The prediction results were in good agreement with the experimental values. The results demonstrated that headspace SPME-GC-MS and LPME-GC-MS are the simple, rapid and easy sample enrichment technique suitable for analysis of volatile compounds. This investigation provided an effective method for predicting the retention indices of new compounds even in the absence of the standard candidates.展开更多
A series of quantitative structure-retention relationship models were developed to predict gas chromatographic relative retention times （GC-RRTs） for 209 polybrominated diphenyl ether （PBDE） congeners on 10 statio...A series of quantitative structure-retention relationship models were developed to predict gas chromatographic relative retention times （GC-RRTs） for 209 polybrominated diphenyl ether （PBDE） congeners on 10 stationary phases. A genetic algorithm with twofold leave-multiple-out cross validation （LMOCV） was used to select optimal subsets from large-size molecular descriptors. Overall multiple-linear regression fitting correlation coefficients （R2） are greater than 0.988, except for the CP-Sil 19 colunm, in which Q^uocv （correlation coefficient of LMOCV）, Q^oocv （correlation coefficient of leave-one-out cross validation, LOOCV）, and Rp2re （correlation coefficients of prediction set） are larger than 0.98. The excellent statistical parameters reveal that the models are robust and have high internal and external predictive capability. According to the descriptors for constructing the models, the GC-RRTs in various stationary phases are highly dependent on distances among atoms, branches of molecules, and molecular properties. PBDE congeners with 1, 9, and 10 bromines are major outliers based on the results of the application domain.展开更多
基金supported by the Shanghai Sailing Program,China(Grant No.:23YF1413300).
文摘Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation.This approach involves developing models for predicting the retention time(RT)of analytes,thereby accelerating method development and facilitating compound identification.In addition,QSRR can be used to study compound retention mechanisms and support drug screening efforts.This review provides a comprehensive analysis of QSRR workflows and applications,with a special focus on the role of artificial intelligence-an area not thoroughly explored in previous reviews.Moreover,we discuss current limitations in RT prediction and propose promising solutions.Overall,this review offers a fresh perspective on future QSRR research,encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.
基金Supported by Shanghai Education Committee Project (No. 11YZ224)Shanghai Leading Academic Discipline Project (No. J51503)
文摘In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.
基金supported by the Foundation of Returned Scholars (Main Program) of Shanxi Province (200902)
文摘Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.
基金supported by the National Natural Science Foundation of China (10901169)National 111 Programme of Introducing Talents of Discipline to Universities (0507111106)+2 种基金Innovation Ability Training Foundation of Chongqing University (CDCX008)Innovative Group Program for Graduates of Chongqing University,ScienceInnovation Fund (200711C1A0010260)
文摘An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.
基金financial supports from the National Key R&D Program of China(Grant Nos.:2022YFC3400700,2022YFA0806400,and 2020YFE0201600)Shanghai Municipal Science and Technology Major Project(Grant No.:2017SHZDZX01)the National Natural Science Foundation of China(Grant No.:31821002).
文摘Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.
文摘The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.
基金supported by the Youth Foundation of Education Bureau,Sichuan Province (09ZB036)Technology Bureau,Sichuan Province (2006j13-141)
文摘Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecular electronegativity-distance vector（I-MEDV） was developed. It was used to describe the structures of 37 compounds of styrax japonicus sieb flowers. Through multiple linear regression（MLR）,a QSRR model was built up. The correlation coefficient（R1） of the model was 0.980. Then,4 vectors were selected to build another model through the method of stepwise multiple regression（SMR） ,and the correlation coefficient（R2） of the model was 0.975. Moreover,all the two models were evaluated by performing the crossvalidation with the leave-one-out（LOO） procedure and the correlation coefficients（Rcv） were 0.948 and 0.968,respectively. The results show that the I-MEDV could successfully describe the structures of organic compounds. The stability and predictability of the models were good.
基金supported by the Foundation of Education Bureau, Sichuan Province (09ZB036)Technology Bureau, Sichuan Province (2006j13-141)
文摘A molecular structural characterization （MSC） method called reduced molecular electronegativity-distance vector （MEDVR） was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression （SMR） and partial least square （PLS） methods, a model with the correlation coefficient （R1） of 0.987 and the standard deviation （SD1） of 1.377 could be obtained. Then through multiple linear regression （MLR）, another model with the correlation coefficient （R2） of 0.989 and standard deviation （SD2） of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique （SMR）, 8 vectors were selected to build up another model with its correlation coefficient （R3） and standard deviation （SD3） of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out （LOO） procedure, and the correlation coefficients （QCV） were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.
基金This work was supported by the National Basic Research Program of China (2003CB415002) and the China Postdoctoral Science Foundation (No. 2003033486) and the Natural Science Research Fund of University in Jiangsu (04KJB150149)
文摘Sixteen indole derivatives have been computed at B3LYP/6-31 IG^＊＊ level using density functional theory （DFF）. Based on linear solvation energy theory, the structural parameters were employed to present correlation between the parameters of chromatograph capacity factor （CCF） and molecular structural parameters. As a result, the correlation equation of the reversed phased high performance liquid chromatograph capacity factor to the intercept lgk＇w and slope S of CCF were obtained, from which the correlation coefficients of lgk＇w to the structural parameters are r^2 = 0.9596 and q^2 = 0.9262. While the correlation coefficients of the parameter S r^2 q^2 with structures are = 0.9750 and = 0.9252. Moreover, the effect of water as solvent on the present two models was also considered using SCRF method, and the result shows that the predicting capacity of correlation equation of lgkw＇ increases, while that of the model for S decreases slightly. Both two correlation equations achieved in this work are more advantageous than those using theoretical descriptors from molecular connectivity indices.
基金supported by the Foundation of Education Bureau,Sichuan Province (09ZB036)Technology Bureau,Sichuan Province (2006j13-141)
文摘A new molecular structural characterization（MSC） method was constructed in this paper.The structure descriptors were used to describe the structures of 149 compounds.Through multiple linear regression（MLR） and stepwise multiple regression（SMR）,a quantitative structure-retention relationship（QSRR） model with 6 variables was obtained.The correlation coefficient（R） of the model was 0.944.Through partial least-squares regression（PLS）,another QSRR model with 5 principal components was obtained.The correlation coefficient（R） of the model was 0.941.The estimation stability and prediction ability of the two models was strictly analyzed by both internal and external validations.For the internal validation,the Cross-Validation（CV） correlation coefficients（RCV） for Leave-One-Out（LOO） were 0.931 and 0.932,respectively.For the external validation,the correlation coefficients（Rtest） of the two models were 0.907 and 0.932.The results suggested good stability and predictability of the model.The prediction results are in very good agreement with the experimental values.This paper provided a new and effective method for predicting the chromatography retention time.
基金supported by the Science and Technology Project of Zhejiang Province(2016C33039)the Public Technology Research Project(Analysis and Measurement)of Zhejiang Province(LGC19B070004)+1 种基金State Key Laboratory of Environmental Chemistry and Ecotoxicology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences(KF2018-15)Natural Science Foundation of Zhejiang Province(LY18C030003)
文摘Polychlorinated dibenzothiophenes(PCDTs)and their corresponding sulfone(PCDTO2)compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations of electrostatic potentials(ESPs)on molecular surface have been performed for all 135 PCDTs and 135 PCDTO2 congeners at the HF/6-31G*level of theory.A number of statistically-based parameters have been extracted.Linear relationship between gas-chromatographic retention index(RI)and the structural descriptors have been established by multiple linear regression.The result shows that two descriptors derived from positive electrostatic potential on molecular surface,■andπ,together with the molecular volume(Vmc)and the energy of the lowest unoccupied molecular orbital(ELUMO)can be well used to express the quantitative structure-retention relationship(QSRR)of PCDTs and PCDTO2.Predictive capability of the two models has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient(RCV)of 0.996 and 0.997,respectively.Furthermore,the predictive power of the models is further examined for the external test set.Correlation coefficients(R)between the observed and predicted RI values for the external test set are 0.997 and0.998,respectively,validating the robustness and good prediction of our model.The QSRR model established may provide again a powerful method for predicting chromatographic properties of aromatic organosulfur compounds.
基金Sponsored by the NSF of Guangxi Province (No. 2011XNSFA018059)Guangxi Key Laboratory Research Fund of Environmental Engineering and Protection Assessment (No. 0801Z026)+1 种基金Major Science of Water Pollution Control and Management (No. 2008ZX07317-02)the Guangxi Zhuang Autonomous Region Department of Education Research (No. 201010LX174) Funding
文摘Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 PCDTs congeners at the B3LYP/6-31G＊ level of theory.By means of the VSMP（variable selection and modeling based on prediction） program,one optimal descriptor（molecular polarizability,α） was selected to develop a QSRR model for the prediction of gas chromatographic retention indices（GC-RI） of PCDTs.The estimated correlation coefficients（r2） and LOO-validated correlation coefficients（q2）,all more than 0.99,were built by multiple linear regression,which shows a good estimation ability and stability of the models.A prediction power for the external samples was validated by the model built from the training set with 17 polychlorinated dibenzothiophenes.
基金Project supported by the Natural Science Foundation Programof Zhejiang Province （No. Y407308）, the Ministry of Science and Technology of Zhejiang Province （No. 201 OR 10044） and the Sprout Talented Project Program of Zhejiang Province （No. 2008R40G2020019）.
文摘The volatile compounds emitted from Mosla chinensis Maxim were analyzed by headspace solid-phase micro- extraction （HS-SPME） and headspace liquid-phase microextraction （HS-LPME） combined with gas chromatography-mass spectrometry （GC-MS）. The main volatiles from Mosla chinensis Maxim were studied in this paper. It can be seen that 61 compounds were separated and identified. Forty-nine volatile compounds were identified by SPME method, mainly including myrcene, a-terpinene, p-cymene, （E）-ocimene, thymol, thymol acetate and （E）-fl-farnesene. Forty-five major volatile compounds were identified by LPME method, including a-thujene, a-pinene, camphene, butanoic acid, 2-methylpropyl ester, myrcene, butanoic acid, butyl ester, a-terpinene, p-cymene, （E）-ocimene, butane, 1,1-dibutoxy-, thymol, thymol acetate and （E）-fl-farnesene. After analyzing the volatile compounds, multiple linear regression （MLR） method was used for building the regression model. Then the quantitative structure-retention relationship （QSRR） model was validated by predictive-ability test. The prediction results were in good agreement with the experimental values. The results demonstrated that headspace SPME-GC-MS and LPME-GC-MS are the simple, rapid and easy sample enrichment technique suitable for analysis of volatile compounds. This investigation provided an effective method for predicting the retention indices of new compounds even in the absence of the standard candidates.
基金Project supported by the Guangxi Natural Science Foundation （No. 2011GXNSFA018061）, the Scientific Research Fund of Guangxi Education Department （No. 200708LX265）, the National Nature Foundation Committee of China （No. 21167006）, and 863 Advanced Research Project （No. 2007AA06Z416）.
文摘A series of quantitative structure-retention relationship models were developed to predict gas chromatographic relative retention times （GC-RRTs） for 209 polybrominated diphenyl ether （PBDE） congeners on 10 stationary phases. A genetic algorithm with twofold leave-multiple-out cross validation （LMOCV） was used to select optimal subsets from large-size molecular descriptors. Overall multiple-linear regression fitting correlation coefficients （R2） are greater than 0.988, except for the CP-Sil 19 colunm, in which Q^uocv （correlation coefficient of LMOCV）, Q^oocv （correlation coefficient of leave-one-out cross validation, LOOCV）, and Rp2re （correlation coefficients of prediction set） are larger than 0.98. The excellent statistical parameters reveal that the models are robust and have high internal and external predictive capability. According to the descriptors for constructing the models, the GC-RRTs in various stationary phases are highly dependent on distances among atoms, branches of molecules, and molecular properties. PBDE congeners with 1, 9, and 10 bromines are major outliers based on the results of the application domain.
