New Modes for the Prediction of Gas Chromatographic Relative Retention Times of Polybrominated Diphenyl Ethers
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 （LMOC...
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| Published in | Chinese journal of chemistry Vol. 29; no. 11; pp. 2495 - 2504 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Weinheim
WILEY-VCH Verlag
01.11.2011
WILEY‐VCH Verlag Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-604X 1614-7065 |
| DOI | 10.1002/cjoc.201100039 |
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| Abstract | 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. |
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| AbstractList | 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 ( R 2 ) are greater than 0.988, except for the CP‐Sil 19 column, in which Q LMOCV 2 (correlation coefficient of LMOCV), Q LOOCV 2 (correlation coefficient of leave‐one‐out cross validation, LOOCV), and R pre 2 (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. 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. 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 column, in which QLMOCV2 (correlation coefficient of LMOCV), QLOOCV2 (correlation coefficient of leave‐one‐out cross validation, LOOCV), and Rpre2 (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. Correlation coefficients of LMOCV (QLMOCV2) of 10 datasets vs. number of descriptors. 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 column, in which QLMOCV2 (correlation coefficient of LMOCV), QLOOCV2 (correlation coefficient of leave-one-out cross validation, LOOCV), and Rpre2 (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. |
| Author | Zhang, Aiqian Wang, Liansheng Li, Lianchen Yi, Zhongsheng |
| AuthorAffiliation | State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University Nanjing, Jiangsu 210093, China College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, Guangxi 541004, China State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China |
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| Cites_doi | 10.1016/j.chemosphere.2006.07.072 10.1016/j.chroma.2003.07.002 10.1002/9783527615452.ch5 10.1016/S0048-9697(01)00852-X 10.1021/ci010245a 10.1021/ci025626i 10.1021/ci010291a 10.1021/ci020377j 10.1177/026119290503300508 10.1177/026119290503300209 10.1007/s00894-007-0195-6 10.1002/qsar.19940130403 10.1021/es900472k 10.1016/S0169-7439(98)00167-1 10.1021/ac901805d 10.1002/9783527628766 10.1111/j.2517-6161.1977.tb01603.x 10.1016/S0160-4120(03)00108-9 10.1080/01621459.1986.10478291 10.1080/00401706.1999.10485634 10.1016/S0165-9936(03)00607-1 10.1093/toxsci/56.1.95 10.1016/j.envint.2008.11.001 10.1002/9783527613106 10.1016/j.chroma.2010.02.070 10.1016/0169-7439(95)00058-5 10.1002/cem.1180060604 10.1016/j.chroma.2005.11.034 10.1080/01621459.1993.10476299 10.1002/qsar.200610151 10.1021/cr068412z 10.1016/j.chroma.2004.12.059 10.1016/j.chroma.2007.03.108 10.1289/ehp.01109s149 10.1002/qsar.19940130306 10.1080/08927022.2010.503326 10.1214/aos/1176349027 10.1365/s10337-006-0160-z 10.1016/0169-7439(89)80012-7 |
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| Notes | gas chromatography relative retention times, polybrominated diphenyl ethers, leave-multiple-out cross validation, quantitative structure-retention relationships, genetic algorithm 31-1547/O6 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. Yi, Zhongsheng Li, Lianchen Zhang, Aiqian Wang, Liansheng（a State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University Nanjing, Jiangsu 210093, China b College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, Guangxi 541004, China c State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China） istex:52A30EF73EB33B975C0690521652A8277D2EB35D the Scientific Research Fund of Guangxi Education Department - No. 200708LX265 the National Nature Foundation Committee of China - No. 21167006 863 Advanced Research Project - No. 2007AA06Z416 ark:/67375/WNG-WZQWCWHZ-H ArticleID:CJOC201100039 the Guangxi Natural Science Foundation - No. 2011GXNSFA018061 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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| References | Baumann, K.. Trac-Trends Anal. Chem., 2003, 22, 395. Rahman, F.; Langford, K.; Scrimshaw, M.; Lester, J.. Sci. Total Environ., 2001, 275, 1. Meerts, I.; van Zanden, J.; Luijks, E.; van Leeuwen-Bol, I.; Marsh, G.; Jakobsson, E.; Bergman, Å.; Brouwer, A.. Toxicol. Sci., 2000, 56, 95. Rayne, S.; Ikonomou, M.. J. Chromatogr. A, 2003, 1016, 235. Dassanayake, R.; Wei, H.; Chen, R.; Li, A.. Anal. Chem., 2009, 81, 9795. Efron, B.. J. Am. Stat. Assoc., 1986, 81, 461. Kubinyi, H.. QSAR Comb. Sci., 1994, 13, 393. H?skuldsson, A.. Chemometr. Intell. Lab. Syst., 1996, 32, 37. Abraham, B.; Chipman, H.; Vijayan, K.. Technometrics, 1999, 41, 135. Wold, S.; Eriksson, L., In Chemometric Methods in Molecular Design, Vol. 2, Ed.: Waterbeemd, H. V. D., Wiley VCH Publishers, Inc., New York, 1995, p. 309. Wang, Y.; Li, A.; Liu, H.; Zhang, Q.; Ma, W.; Song, W.; Jiang, G.. J. Chromatogr. A, 2006, 1103, 314. Mitra, I.; Saha, A.; Roy, K.. Molecular Simulation, 2010, 36, 1067. Gramatica, P.. QSAR Comb. Sci., 2007, 26, 694. Hawkins, D.; Basak, S.; Mills, D.. J. Chem. Inf. Comput. Sci., 2003, 43, 579. Netzeva, T.; Worth, A.; Aldenberg, T.; Benigni, R.; Cronin, M.; Gramatica, P.; Jaworska, J.; Kahn, S.; Klopman, G.; Marchant, C.. Altern. Lab. Anim., 2005, 33, 1. Héberger, K.. J. Chromatogr. A, 2007, 1158, 273. Shao, J.. J. Am. Stat. Assoc., 1993, 88, 486. Kaliszan, R.. Chem. Rev., 2007, 107, 3212. Stone, M.. J. Roy. Stat. Soc. Ser. B (Methodological), 1977, 39, 44. Liu, H. Y.; Liu, S. S.; Qin, L. T.. J. Mol. Model., 2007, 13, 611. Todeschini, R.; Consonni, V., Molecular Descriptors for Chemoinformatics, Wiley VCH, New York, 2009. Xu, H. Y.; Zou, J. W.; Yu, Q. S.; Wang, Y. H.; Zhang, J. Y.; Jin, H. X.. Chemosphere, 2007, 66, 1998. Zhang, P.. The Annals of Statistics, 1993, 21, 299. Korytár, P.; Covaci, A.; de Boer, J.; Gelbin, A.; Brinkman, U.. J. Chromatogr. A, 2005, 1065, 239. Darnerud, P.; Eriksen, G.; Jóhannesson, T.; Larsen, P.; Viluksela, M.. Environ. Health Perspect., 2001, 109, 49. Sjödin, A.; Patterson, D.. Environ. Int., 2003, 29, 829. Kubinyi, H.. QSAR Comb. Sci., 1994, 13, 285. N?s, T.. Chemometr. Intell. Lab. Syst., 1989, 5, 155. Wei, H.; Yang, R.; Li, A.; Christensen, E.; Rockne, K.. J. Chromatogr. A, 2010, 1217, 2964. Todeschini, R.; Consonni, V., Handbook of Molecular Descriptors, Wiley VCH, New York, 2000. deBruyn, A.; Meloche, L.; Lowe, C.. Environ. Sci. Technol., 2009, 43, 3700. Liu, S. S.; Liu, H. L.; Yin, C. S.; Wang, L. S.. J. Chem. Inf. Comput. Sci., 2003, 43, 964. Liu, S. S.; Liu, Y.; Yin, D. Q.. Chin. Chem. Lett., 2005, 16, 1559. Yogui, G.; Sericano, J.. Environ. Int., 2009, 35, 655. Martens, H. A.; Dardenne, P.. Chemometr. Intell. Lab. Syst., 1998, 44, 99. Zhang, Y. H.; Liu, S. S.; Liu, H. Y.. Chromatographia, 2007, 65, 319. Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T.. Altern. Lab. Anim., 2005, 33, 445. Yasri, A.; Hartsough, D.. J. Chem. Inf. Comput. Sci., 2001, 41, 1218. Liu, S. S.; Yin, C. S.; Wang, L. S.. J. Chem. Inf. Comput. Sci., 2002, 42, 749. Cruciani, G.; Baroni, M.; Clementi, S.; Costantino, G.; Riganelli, D.; Skagerberg, B.. J. Chemom., 1992, 6, 335. 2003; 1016 1989; 5 2007; 107 2009; 43 2010; 36 2009; 81 1993; 88 1993; 21 2010; 1217 2009 2006; 1103 1999; 41 1995; 2 2001; 109 2007; 13 1996; 32 2001; 41 1998; 44 1992; 6 2001; 275 2009; 35 1986; 81 2000 2002; 42 2000; 56 1977; 39 2007; 1158 2005; 1065 1994; 13 2003; 29 2007; 65 2005; 16 2007; 66 2005; 33 2003; 43 2003; 22 2007; 26 Liu S. S. (e_1_2_1_40_2) 2005; 16 Stone M. (e_1_2_1_31_2) 1977; 39 e_1_2_1_41_2 e_1_2_1_22_2 e_1_2_1_23_2 e_1_2_1_20_2 e_1_2_1_21_2 e_1_2_1_26_2 e_1_2_1_27_2 e_1_2_1_24_2 e_1_2_1_25_2 e_1_2_1_28_2 Efron B. (e_1_2_1_29_2) 1986; 81 e_1_2_1_6_2 e_1_2_1_30_2 e_1_2_1_7_2 e_1_2_1_4_2 e_1_2_1_5_2 e_1_2_1_2_2 e_1_2_1_11_2 e_1_2_1_34_2 e_1_2_1_3_2 e_1_2_1_12_2 e_1_2_1_33_2 e_1_2_1_32_2 e_1_2_1_10_2 Netzeva T. (e_1_2_1_38_2) 2005; 33 e_1_2_1_15_2 e_1_2_1_16_2 e_1_2_1_37_2 e_1_2_1_13_2 e_1_2_1_36_2 e_1_2_1_14_2 e_1_2_1_35_2 e_1_2_1_19_2 e_1_2_1_8_2 e_1_2_1_17_2 e_1_2_1_9_2 e_1_2_1_18_2 e_1_2_1_39_2 |
| References_xml | – reference: Dassanayake, R.; Wei, H.; Chen, R.; Li, A.. Anal. Chem., 2009, 81, 9795. – reference: Liu, S. S.; Yin, C. S.; Wang, L. S.. J. Chem. Inf. Comput. Sci., 2002, 42, 749. – reference: Hawkins, D.; Basak, S.; Mills, D.. J. Chem. Inf. Comput. Sci., 2003, 43, 579. – reference: Héberger, K.. J. Chromatogr. A, 2007, 1158, 273. – reference: deBruyn, A.; Meloche, L.; Lowe, C.. Environ. Sci. Technol., 2009, 43, 3700. – reference: Darnerud, P.; Eriksen, G.; Jóhannesson, T.; Larsen, P.; Viluksela, M.. Environ. Health Perspect., 2001, 109, 49. – reference: Cruciani, G.; Baroni, M.; Clementi, S.; Costantino, G.; Riganelli, D.; Skagerberg, B.. J. Chemom., 1992, 6, 335. – reference: Sjödin, A.; Patterson, D.. Environ. Int., 2003, 29, 829. – reference: Yasri, A.; Hartsough, D.. J. Chem. Inf. Comput. Sci., 2001, 41, 1218. – reference: Shao, J.. J. Am. Stat. Assoc., 1993, 88, 486. – reference: Liu, H. Y.; Liu, S. S.; Qin, L. T.. J. Mol. Model., 2007, 13, 611. – reference: Martens, H. A.; Dardenne, P.. Chemometr. Intell. Lab. Syst., 1998, 44, 99. – reference: Gramatica, P.. QSAR Comb. Sci., 2007, 26, 694. – reference: Todeschini, R.; Consonni, V., Molecular Descriptors for Chemoinformatics, Wiley VCH, New York, 2009. – reference: N?s, T.. Chemometr. Intell. Lab. Syst., 1989, 5, 155. – reference: Rayne, S.; Ikonomou, M.. J. Chromatogr. A, 2003, 1016, 235. – reference: Korytár, P.; Covaci, A.; de Boer, J.; Gelbin, A.; Brinkman, U.. J. Chromatogr. A, 2005, 1065, 239. – reference: Zhang, Y. H.; Liu, S. S.; Liu, H. Y.. Chromatographia, 2007, 65, 319. – reference: Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T.. Altern. Lab. Anim., 2005, 33, 445. – reference: Kubinyi, H.. QSAR Comb. Sci., 1994, 13, 393. – reference: Liu, S. S.; Liu, H. L.; Yin, C. S.; Wang, L. S.. J. Chem. Inf. Comput. Sci., 2003, 43, 964. – reference: Xu, H. Y.; Zou, J. W.; Yu, Q. S.; Wang, Y. H.; Zhang, J. Y.; Jin, H. X.. Chemosphere, 2007, 66, 1998. – reference: Efron, B.. J. Am. Stat. Assoc., 1986, 81, 461. – reference: Yogui, G.; Sericano, J.. Environ. Int., 2009, 35, 655. – reference: Abraham, B.; Chipman, H.; Vijayan, K.. Technometrics, 1999, 41, 135. – reference: Wold, S.; Eriksson, L., In Chemometric Methods in Molecular Design, Vol. 2, Ed.: Waterbeemd, H. V. D., Wiley VCH Publishers, Inc., New York, 1995, p. 309. – reference: H?skuldsson, A.. Chemometr. Intell. Lab. Syst., 1996, 32, 37. – reference: Baumann, K.. Trac-Trends Anal. Chem., 2003, 22, 395. – reference: Wei, H.; Yang, R.; Li, A.; Christensen, E.; Rockne, K.. J. Chromatogr. A, 2010, 1217, 2964. – reference: Zhang, P.. The Annals of Statistics, 1993, 21, 299. – reference: Wang, Y.; Li, A.; Liu, H.; Zhang, Q.; Ma, W.; Song, W.; Jiang, G.. J. Chromatogr. A, 2006, 1103, 314. – reference: Rahman, F.; Langford, K.; Scrimshaw, M.; Lester, J.. Sci. Total Environ., 2001, 275, 1. – reference: Netzeva, T.; Worth, A.; Aldenberg, T.; Benigni, R.; Cronin, M.; Gramatica, P.; Jaworska, J.; Kahn, S.; Klopman, G.; Marchant, C.. Altern. Lab. Anim., 2005, 33, 1. – reference: Kaliszan, R.. Chem. Rev., 2007, 107, 3212. – reference: Stone, M.. J. Roy. Stat. Soc. Ser. B (Methodological), 1977, 39, 44. – reference: Kubinyi, H.. QSAR Comb. Sci., 1994, 13, 285. – reference: Mitra, I.; Saha, A.; Roy, K.. Molecular Simulation, 2010, 36, 1067. – reference: Liu, S. S.; Liu, Y.; Yin, D. Q.. Chin. Chem. Lett., 2005, 16, 1559. – reference: Meerts, I.; van Zanden, J.; Luijks, E.; van Leeuwen-Bol, I.; Marsh, G.; Jakobsson, E.; Bergman, Å.; Brouwer, A.. Toxicol. Sci., 2000, 56, 95. – reference: Todeschini, R.; Consonni, V., Handbook of Molecular Descriptors, Wiley VCH, New York, 2000. – volume: 81 start-page: 461 year: 1986 publication-title: J. Am. Stat. Assoc. – volume: 22 start-page: 395 year: 2003 publication-title: Trac‐Trends Anal. Chem. – year: 2009 – volume: 43 start-page: 579 year: 2003 publication-title: J. Chem. Inf. Comput. Sci. – volume: 1016 start-page: 235 year: 2003 publication-title: J. Chromatogr. A – volume: 33 start-page: 1 year: 2005 publication-title: Altern. Lab. Anim. – volume: 35 start-page: 655 year: 2009 publication-title: Environ. Int. – volume: 109 start-page: 49 year: 2001 publication-title: Environ. Health Perspect. – volume: 43 start-page: 3700 year: 2009 publication-title: Environ. Sci. Technol. – volume: 56 start-page: 95 year: 2000 publication-title: Toxicol. Sci. – volume: 1158 start-page: 273 year: 2007 publication-title: J. Chromatogr. A – year: 2000 – volume: 65 start-page: 319 year: 2007 publication-title: Chromatographia – volume: 13 start-page: 611 year: 2007 publication-title: J. Mol. Model. – volume: 32 start-page: 37 year: 1996 publication-title: Chemometr. Intell. Lab. Syst. – volume: 13 start-page: 393 year: 1994 publication-title: QSAR Comb. Sci. – volume: 39 start-page: 44 year: 1977 publication-title: J. Roy. Stat. Soc. Ser. B (Methodological) – volume: 275 start-page: 1 year: 2001 publication-title: Sci. Total Environ. – volume: 41 start-page: 1218 year: 2001 publication-title: J. Chem. Inf. Comput. Sci. – volume: 1217 start-page: 2964 year: 2010 publication-title: J. Chromatogr. A – volume: 16 start-page: 1559 year: 2005 publication-title: Chin. Chem. Lett. – volume: 29 start-page: 829 year: 2003 publication-title: Environ. Int. – volume: 33 start-page: 445 year: 2005 publication-title: Altern. Lab. Anim. – volume: 26 start-page: 694 year: 2007 publication-title: QSAR Comb. Sci. – volume: 2 start-page: 309 year: 1995 – volume: 43 start-page: 964 year: 2003 publication-title: J. Chem. Inf. Comput. Sci. – volume: 42 start-page: 749 year: 2002 publication-title: J. Chem. Inf. Comput. Sci. – volume: 88 start-page: 486 year: 1993 publication-title: J. Am. Stat. Assoc. – volume: 66 start-page: 1998 year: 2007 publication-title: Chemosphere – volume: 81 start-page: 9795 year: 2009 publication-title: Anal. Chem. – volume: 1065 start-page: 239 year: 2005 publication-title: J. Chromatogr. A – volume: 41 start-page: 135 year: 1999 publication-title: Technometrics – volume: 1103 start-page: 314 year: 2006 publication-title: J. Chromatogr. A – volume: 6 start-page: 335 year: 1992 publication-title: J. Chemom. – volume: 5 start-page: 155 year: 1989 publication-title: Chemometr. Intell. Lab. Syst. – volume: 36 start-page: 1067 year: 2010 publication-title: Molecular Simulation – volume: 107 start-page: 3212 year: 2007 publication-title: Chem. Rev. – volume: 44 start-page: 99 year: 1998 publication-title: Chemometr. Intell. Lab. Syst. – volume: 21 start-page: 299 year: 1993 publication-title: The Annals of Statistics – volume: 13 start-page: 285 year: 1994 publication-title: QSAR Comb. Sci. – ident: e_1_2_1_41_2 doi: 10.1016/j.chemosphere.2006.07.072 – ident: e_1_2_1_16_2 doi: 10.1016/j.chroma.2003.07.002 – ident: e_1_2_1_35_2 doi: 10.1002/9783527615452.ch5 – volume: 16 start-page: 1559 year: 2005 ident: e_1_2_1_40_2 publication-title: Chin. Chem. Lett. – ident: e_1_2_1_7_2 doi: 10.1016/S0048-9697(01)00852-X – ident: e_1_2_1_23_2 doi: 10.1021/ci010245a – ident: e_1_2_1_25_2 doi: 10.1021/ci025626i – ident: e_1_2_1_22_2 doi: 10.1021/ci010291a – ident: e_1_2_1_20_2 doi: 10.1021/ci020377j – ident: e_1_2_1_39_2 doi: 10.1177/026119290503300508 – volume: 33 start-page: 1 year: 2005 ident: e_1_2_1_38_2 publication-title: Altern. Lab. Anim. doi: 10.1177/026119290503300209 – ident: e_1_2_1_13_2 doi: 10.1007/s00894-007-0195-6 – ident: e_1_2_1_24_2 doi: 10.1002/qsar.19940130403 – ident: e_1_2_1_4_2 doi: 10.1021/es900472k – ident: e_1_2_1_26_2 doi: 10.1016/S0169-7439(98)00167-1 – ident: e_1_2_1_5_2 doi: 10.1021/ac901805d – ident: e_1_2_1_18_2 doi: 10.1002/9783527628766 – volume: 39 start-page: 44 year: 1977 ident: e_1_2_1_31_2 publication-title: J. Roy. Stat. Soc. Ser. B (Methodological) doi: 10.1111/j.2517-6161.1977.tb01603.x – ident: e_1_2_1_2_2 doi: 10.1016/S0160-4120(03)00108-9 – volume: 81 start-page: 461 year: 1986 ident: e_1_2_1_29_2 publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1986.10478291 – ident: e_1_2_1_19_2 doi: 10.1080/00401706.1999.10485634 – ident: e_1_2_1_34_2 doi: 10.1016/S0165-9936(03)00607-1 – ident: e_1_2_1_8_2 doi: 10.1093/toxsci/56.1.95 – ident: e_1_2_1_3_2 doi: 10.1016/j.envint.2008.11.001 – ident: e_1_2_1_17_2 doi: 10.1002/9783527613106 – ident: e_1_2_1_9_2 doi: 10.1016/j.chroma.2010.02.070 – ident: e_1_2_1_28_2 doi: 10.1016/0169-7439(95)00058-5 – ident: e_1_2_1_33_2 doi: 10.1002/cem.1180060604 – ident: e_1_2_1_12_2 doi: 10.1016/j.chroma.2005.11.034 – ident: e_1_2_1_30_2 doi: 10.1080/01621459.1993.10476299 – ident: e_1_2_1_37_2 doi: 10.1002/qsar.200610151 – ident: e_1_2_1_11_2 doi: 10.1021/cr068412z – ident: e_1_2_1_15_2 doi: 10.1016/j.chroma.2004.12.059 – ident: e_1_2_1_10_2 doi: 10.1016/j.chroma.2007.03.108 – ident: e_1_2_1_6_2 doi: 10.1289/ehp.01109s149 – ident: e_1_2_1_21_2 doi: 10.1002/qsar.19940130306 – ident: e_1_2_1_36_2 doi: 10.1080/08927022.2010.503326 – ident: e_1_2_1_32_2 doi: 10.1214/aos/1176349027 – ident: e_1_2_1_14_2 doi: 10.1365/s10337-006-0160-z – ident: e_1_2_1_27_2 doi: 10.1016/0169-7439(89)80012-7 |
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| Snippet | A series of quantitative structure-retention relationship models were developed to predict gas chromatographic relative retention times （GC-RRTs） for 209... A series of quantitative structure‐retention relationship models were developed to predict gas chromatographic relative retention times (GC‐RRTs) for 209... A series of quantitative structure-retention relationship models were developed to predict gas chromatographic relative retention times (GC-RRTs) for 209... |
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| SubjectTerms | Chromatography Correlation coefficient Ethers Fire resistant materials Fish gas chromatography relative retention times genetic algorithm leave-multiple-out cross validation Polybrominated diphenyl ethers quantitative structure-retention relationships Retention 交叉验证 分子描述符 多溴二苯醚 模型预测 气相色谱 瓦斯预测 相关系数 相对保留时间 |
| Title | New Modes for the Prediction of Gas Chromatographic Relative Retention Times of Polybrominated Diphenyl Ethers |
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