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Volumn 168, Issue 2-3, 2009, Pages 962-969

A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine

Author keywords

Genetic algorithm; Lower flammability limit; Quantitative structure property relationship; Support vector machine

Indexed keywords

AVERAGE ABSOLUTE ERROR; CHEMOMETRICS; COMPARISON RESULT; DATA SETS; DESCRIPTORS; LOWER FLAMMABILITY LIMIT; LOWER FLAMMABILITY LIMITS; MOLECULAR DESCRIPTORS; OPTIMAL SUBSETS; QSPR MODEL; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP; ROOT MEAN SQUARE ERRORS;

EID: 67649803354     PISSN: 03043894     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhazmat.2009.02.122     Document Type: Article
Times cited : (94)

References (44)
  • 1
    • 1942530857 scopus 로고    scopus 로고
    • A review of estimation methods for flash points and flammability limits
    • Vidal M., Rogers W.J., Holste J.C., and Mannan M.S. A review of estimation methods for flash points and flammability limits. Process Saf. Prog. 23 (2004) 47-55
    • (2004) Process Saf. Prog. , vol.23 , pp. 47-55
    • Vidal, M.1    Rogers, W.J.2    Holste, J.C.3    Mannan, M.S.4
  • 2
    • 0026190793 scopus 로고
    • Group contribution method for predicting the lower and the upper flammable limits of vapors in air
    • Seaton W.H. Group contribution method for predicting the lower and the upper flammable limits of vapors in air. J. Hazard. Mater. 27 (1991) 169-185
    • (1991) J. Hazard. Mater. , vol.27 , pp. 169-185
    • Seaton, W.H.1
  • 4
    • 0028508411 scopus 로고
    • Note: Empirical relationship between lower flammability limits and standard enthalpies of combustion of organic compounds
    • Suzuki T. Note: Empirical relationship between lower flammability limits and standard enthalpies of combustion of organic compounds. Fire Mater. 18 (1994) 333-336
    • (1994) Fire Mater. , vol.18 , pp. 333-336
    • Suzuki, T.1
  • 5
    • 0029336023 scopus 로고
    • Neural network techniques applied to predict flammability limits of organic compounds
    • Suzuki T., and Ishida M. Neural network techniques applied to predict flammability limits of organic compounds. Fire Mater. 19 (1995) 179-189
    • (1995) Fire Mater. , vol.19 , pp. 179-189
    • Suzuki, T.1    Ishida, M.2
  • 6
    • 0033097251 scopus 로고    scopus 로고
    • Predicting heats of combustion and lower flammability limits of organosilicon compounds
    • Hshieh F. Predicting heats of combustion and lower flammability limits of organosilicon compounds. Fire Mater. 23 (1999) 79-89
    • (1999) Fire Mater. , vol.23 , pp. 79-89
    • Hshieh, F.1
  • 7
    • 0036496907 scopus 로고    scopus 로고
    • Using heats of oxidation to evaluate flammability hazards
    • Britton L.G. Using heats of oxidation to evaluate flammability hazards. Process Saf. Prog. 21 (2002) 31-54
    • (2002) Process Saf. Prog. , vol.21 , pp. 31-54
    • Britton, L.G.1
  • 8
    • 53849091731 scopus 로고    scopus 로고
    • Quantitative structure-property relationship for prediction of the lower flammability limit of pure compounds
    • Gharagheizi F. Quantitative structure-property relationship for prediction of the lower flammability limit of pure compounds. Energy Fuels 22 (2008) 3037-3039
    • (2008) Energy Fuels , vol.22 , pp. 3037-3039
    • Gharagheizi, F.1
  • 9
    • 0042424916 scopus 로고    scopus 로고
    • Flammability characteristics of pure hydrocarbons
    • Albahri T.A. Flammability characteristics of pure hydrocarbons. Chem. Eng. Sci. 58 (2003) 3629-3641
    • (2003) Chem. Eng. Sci. , vol.58 , pp. 3629-3641
    • Albahri, T.A.1
  • 10
    • 20544443423 scopus 로고    scopus 로고
    • How chemical structure determines physical, chemical, and technological properties: an overview illustrating the potential of quantitative structure-property relationships for fuels science
    • Katritzky A.R., and Fara D.C. How chemical structure determines physical, chemical, and technological properties: an overview illustrating the potential of quantitative structure-property relationships for fuels science. Energy Fuels 19 (2005) 922-935
    • (2005) Energy Fuels , vol.19 , pp. 922-935
    • Katritzky, A.R.1    Fara, D.C.2
  • 11
    • 0001321370 scopus 로고
    • QSPR: the correlation and quantitative prediction of chemical and physical properties from structure
    • Katritzky A.R., Lobanov V.S., and Karelson M. QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem. Soc. Rev. 24 (1995) 279-287
    • (1995) Chem. Soc. Rev. , vol.24 , pp. 279-287
    • Katritzky, A.R.1    Lobanov, V.S.2    Karelson, M.3
  • 12
    • 0002905234 scopus 로고    scopus 로고
    • Structurally diverse quantitative-structure-property relationship correlations of technologically relevant physical properties
    • Katritzky A.R., Maran U., Lobanov V., and Karelson M. Structurally diverse quantitative-structure-property relationship correlations of technologically relevant physical properties. J. Chem. Inf. Comput. Sci. 40 (2000) 1-18
    • (2000) J. Chem. Inf. Comput. Sci. , vol.40 , pp. 1-18
    • Katritzky, A.R.1    Maran, U.2    Lobanov, V.3    Karelson, M.4
  • 14
    • 0043235835 scopus 로고    scopus 로고
    • Prediction of physicochemical properties based on neural network modeling
    • Taskinen J., and Yliruusi J. Prediction of physicochemical properties based on neural network modeling. Adv. Drug Deliv. Rev. 55 (2003) 1163-1183
    • (2003) Adv. Drug Deliv. Rev. , vol.55 , pp. 1163-1183
    • Taskinen, J.1    Yliruusi, J.2
  • 17
    • 0037191113 scopus 로고    scopus 로고
    • A flexible classification approach with optimal generalisation performance: support vector machines
    • Belousov A.I., Verzakov S.A., and Frese J.V. A flexible classification approach with optimal generalisation performance: support vector machines. Chemomet. Intell. Lab. Syst. 64 (2002) 15-25
    • (2002) Chemomet. Intell. Lab. Syst. , vol.64 , pp. 15-25
    • Belousov, A.I.1    Verzakov, S.A.2    Frese, J.V.3
  • 18
    • 2342505830 scopus 로고    scopus 로고
    • Fault diagnosis based on fisher discriminant analysis and support vector machines
    • Chiang L.H., Kotanchek M.E., and Kordon A.K. Fault diagnosis based on fisher discriminant analysis and support vector machines. Comput. Chem. Eng. 28 (2004) 1389-1401
    • (2004) Comput. Chem. Eng. , vol.28 , pp. 1389-1401
    • Chiang, L.H.1    Kotanchek, M.E.2    Kordon, A.K.3
  • 19
    • 0742304284 scopus 로고    scopus 로고
    • Support vector classification with parameter tuning assisted by agent-based technique
    • Kulkarni A., Jayaraman V.K., and Kulkarni B.D. Support vector classification with parameter tuning assisted by agent-based technique. Comput. Chem. Eng. 28 (2004) 311-318
    • (2004) Comput. Chem. Eng. , vol.28 , pp. 311-318
    • Kulkarni, A.1    Jayaraman, V.K.2    Kulkarni, B.D.3
  • 20
    • 25844517176 scopus 로고    scopus 로고
    • Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process
    • Kulkarni A., Jayaraman V.K., and Kulkarni B.D. Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process. Comput. Chem. Eng. 29 (2005) 2128-2133
    • (2005) Comput. Chem. Eng. , vol.29 , pp. 2128-2133
    • Kulkarni, A.1    Jayaraman, V.K.2    Kulkarni, B.D.3
  • 21
    • 36048965518 scopus 로고    scopus 로고
    • Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines
    • Niazi A., Ghasemi J., and Zendehdel M. Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines. Talanta 74 (2007) 247-254
    • (2007) Talanta , vol.74 , pp. 247-254
    • Niazi, A.1    Ghasemi, J.2    Zendehdel, M.3
  • 22
    • 34848926240 scopus 로고    scopus 로고
    • Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid-search support vector machine
    • Wang J., Du H.Y., Yao X.J., and Hu Z.D. Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid-search support vector machine. Anal. Chim. Acta 601 (2007) 156-163
    • (2007) Anal. Chim. Acta , vol.601 , pp. 156-163
    • Wang, J.1    Du, H.Y.2    Yao, X.J.3    Hu, Z.D.4
  • 24
    • 35348816323 scopus 로고    scopus 로고
    • A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine
    • Fatemi M.H., and Gharaghani S. A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine. Bioorg. Med. Chem. 15 (2007) 7746-7754
    • (2007) Bioorg. Med. Chem. , vol.15 , pp. 7746-7754
    • Fatemi, M.H.1    Gharaghani, S.2
  • 25
    • 40149083386 scopus 로고    scopus 로고
    • Prediction of selectivity coefficients of univalent anions for anion-selective electrode using support vector machine
    • Fatemi M.H., Gharaghani S., Mohammadkhani S., and Rezaie Z. Prediction of selectivity coefficients of univalent anions for anion-selective electrode using support vector machine. Electrochim. Acta 53 (2008) 4276-4282
    • (2008) Electrochim. Acta , vol.53 , pp. 4276-4282
    • Fatemi, M.H.1    Gharaghani, S.2    Mohammadkhani, S.3    Rezaie, Z.4
  • 26
    • 38749114675 scopus 로고    scopus 로고
    • Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines
    • Niazi A., Jameh-Bozorghi S., and Nori-Shargh D. Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines. J. Hazard. Mater. 151 (2008) 603-609
    • (2008) J. Hazard. Mater. , vol.151 , pp. 603-609
    • Niazi, A.1    Jameh-Bozorghi, S.2    Nori-Shargh, D.3
  • 27
    • 43849095644 scopus 로고    scopus 로고
    • Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds
    • Pan Y., Jiang J.C., Wang R., and Cao H.Y. Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds. Chemomet. Intell. Lab. Syst. 92 (2008) 169-178
    • (2008) Chemomet. Intell. Lab. Syst. , vol.92 , pp. 169-178
    • Pan, Y.1    Jiang, J.C.2    Wang, R.3    Cao, H.Y.4
  • 28
    • 55249100505 scopus 로고    scopus 로고
    • Quantitative structure-property relationship studies for predicting flash points of organic compounds using support vector machines
    • Pan Y., Jiang J.C., Wang R., Cao H.Y., and Zhao J.B. Quantitative structure-property relationship studies for predicting flash points of organic compounds using support vector machines. QSAR Comb. Sci. 27 (2008) 1013-1019
    • (2008) QSAR Comb. Sci. , vol.27 , pp. 1013-1019
    • Pan, Y.1    Jiang, J.C.2    Wang, R.3    Cao, H.Y.4    Zhao, J.B.5
  • 29
    • 34447649349 scopus 로고    scopus 로고
    • Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine
    • Wang J., Du H.Y., Liu H.X., Yao X.J., Hu Z.D., and Fan B.T. Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine. Talanta 73 (2007) 147-156
    • (2007) Talanta , vol.73 , pp. 147-156
    • Wang, J.1    Du, H.Y.2    Liu, H.X.3    Yao, X.J.4    Hu, Z.D.5    Fan, B.T.6
  • 30
    • 34250628103 scopus 로고    scopus 로고
    • Principles of QSAR models validation: internal and external
    • Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26 (2007) 694-701
    • (2007) QSAR Comb. Sci. , vol.26 , pp. 694-701
    • Gramatica, P.1
  • 31
    • 0001728908 scopus 로고    scopus 로고
    • Quantum-chemical descriptors in QSAR/QSPR studies
    • Karelson M., Lobanov V.S., and Katritzky A.R. Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev. 96 (1996) 1027-1043
    • (1996) Chem. Rev. , vol.96 , pp. 1027-1043
    • Karelson, M.1    Lobanov, V.S.2    Katritzky, A.R.3
  • 32
    • 0001074001 scopus 로고    scopus 로고
    • Handbook of molecular descriptors
    • Mannhold R., Kubinyi H., and Timmerman H. (Eds), Wiley-VCH, Weinheim
    • Todeschini R., and Consonni V. Handbook of molecular descriptors. In: Mannhold R., Kubinyi H., and Timmerman H. (Eds). Methods and Principles in Medicinal Chemistry (2000), Wiley-VCH, Weinheim
    • (2000) Methods and Principles in Medicinal Chemistry
    • Todeschini, R.1    Consonni, V.2
  • 33
    • 67649730401 scopus 로고    scopus 로고
    • R. Todeschini, V. Consonni, M. Pavan, DRAGON. Software for the calculation of molecular descriptors, web version 2.1, 2002
    • R. Todeschini, V. Consonni, M. Pavan, DRAGON. Software for the calculation of molecular descriptors, web version 2.1, 2002. http://www.disat.unimib.it/chm/.
  • 35
    • 85002377847 scopus 로고
    • Genetic algorithms as a strategy for feature selection
    • Leardi R., Boggia R., and Terrile M. Genetic algorithms as a strategy for feature selection. J. Chemomet. 6 (1992) 267-281
    • (1992) J. Chemomet. , vol.6 , pp. 267-281
    • Leardi, R.1    Boggia, R.2    Terrile, M.3
  • 36
    • 84984302791 scopus 로고
    • Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection
    • Leardi R. Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection. J. Chemomet. 8 (1994) 65-79
    • (1994) J. Chemomet. , vol.8 , pp. 65-79
    • Leardi, R.1
  • 37
    • 0037533737 scopus 로고    scopus 로고
    • Prediction of bioconcentration factor using genetic algorithm and artificial neural network
    • Fatemi M.H., Jalali-Heravi M., and Konuze E. Prediction of bioconcentration factor using genetic algorithm and artificial neural network. Anal. Chim. Acta 486 (2003) 101-108
    • (2003) Anal. Chim. Acta , vol.486 , pp. 101-108
    • Fatemi, M.H.1    Jalali-Heravi, M.2    Konuze, E.3
  • 38
    • 29944436858 scopus 로고    scopus 로고
    • Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks
    • Fatemi M.H. Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks. Anal. Chim. Acta 556 (2006) 355-363
    • (2006) Anal. Chim. Acta , vol.556 , pp. 355-363
    • Fatemi, M.H.1
  • 39
    • 33645638355 scopus 로고    scopus 로고
    • Prediction of chromatographic retention times for aromatic hydrocarbons
    • Ghosh P., Chawla B., Joshi P.V., and Jaffe S.B. Prediction of chromatographic retention times for aromatic hydrocarbons. Energy Fuels 20 (2006) 609-619
    • (2006) Energy Fuels , vol.20 , pp. 609-619
    • Ghosh, P.1    Chawla, B.2    Joshi, P.V.3    Jaffe, S.B.4
  • 40
    • 33746322753 scopus 로고    scopus 로고
    • An improved structure-property model for predicting melting-point temperatures
    • Godavarthy S.S., Robinson R.L., and Gasem K.A.M. An improved structure-property model for predicting melting-point temperatures. Ind. Eng. Chem. Res. 45 (2006) 5117-5126
    • (2006) Ind. Eng. Chem. Res. , vol.45 , pp. 5117-5126
    • Godavarthy, S.S.1    Robinson, R.L.2    Gasem, K.A.M.3
  • 41
    • 0345019845 scopus 로고    scopus 로고
    • Genetic algorithms applied to feature selection in PLS regression: how and when to use them
    • Leardi R., and Lupiáñez A. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemomet. Intell. Lab. Syst. 41 (1998) 195-207
    • (1998) Chemomet. Intell. Lab. Syst. , vol.41 , pp. 195-207
    • Leardi, R.1    Lupiáñez, A.2
  • 43
    • 0043132440 scopus 로고    scopus 로고
    • Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs
    • Eriksson L., Jaworska J., Worth A.P., Cronin M.T.D., McDowell R.M., and Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs. Environ. Health Perspect. 111 (2003) 1361-1375
    • (2003) Environ. Health Perspect. , vol.111 , pp. 1361-1375
    • Eriksson, L.1    Jaworska, J.2    Worth, A.P.3    Cronin, M.T.D.4    McDowell, R.M.5    Gramatica, P.6


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