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QSRR Study of GC Retention Indices of Essential-Oil Compounds by Multiple Linear Regression with a Genetic Algorithm

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Abstract

Quantitative structure–retention relationships (QSRR) for components of the essential oil of the plant Bidens pilosa Linn. var. Radiata were studied to enable prediction of their retention indices (I R). A data set was selected consisting of the retention indices of 44 components of the essential oil with a range of more than 635 units. A suitable set of molecular descriptors was then calculated and the best-fitting descriptors were selected by using stepwise multiple linear regression (SW-MLR) and a genetic algorithm (GA-MLR) the selection of variables. Comparison of the results obtained indicated the superiority of the genetic algorithm over the stepwise multiple regression method for feature selection. The predictive quality of the QSRR models was tested for an external prediction set of nine compounds, randomly chosen from the 44 compounds. One GA-MLR model with five selected descriptors was obtained. This model, with high statistical significance (R 2 train = 0.977, SE (%) = 2.33, F = 243.275, R 2 pred = 0.978), could be used to predict the retention indices of the molecules with error <6%.

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References

  1. Kusmenoglu S, Baser KHC, Ozek T (1995) J Essent Oil Res 7:441–445

    CAS  Google Scholar 

  2. Deba F, Xuan TD, Yasuda M, Tawata S (2008) Food Contr 19:346–352

    Article  CAS  Google Scholar 

  3. Dimo T, Rakotonirina SV, Tan PV, Azay J, Dongo E, Cros G (2002) J Ethnopharmacol 83:183–191

    Article  Google Scholar 

  4. Chiang YM, Chuang DY, Wang SY, Kuo YH, Tsai PW, Shyur LF (2004) J Ethnopharmacol 95:409–419

    Article  CAS  Google Scholar 

  5. Rabe T, Staden JV (1997) J Ethnopharmacol 56:81–87

    Article  CAS  Google Scholar 

  6. Yang HL, Chen SC, Chang NW, Chang JM, Lee ML, Tsai PC (2006) Food Chem Toxicol 44:1513–1521

    Article  CAS  Google Scholar 

  7. Moosavi-Movahedi AA, Safarian S, Hakimelahi GH, Ataei G, Ajloo D, Panjehpour S, Riahi S, Mousavi MF, Mardanyan S, Soltani N, Khalafi-Nezhad A, Sharghi H, Moghadamnia H, Saboury AA (2004) Nucleos Nucleot Nucl Acid 23:613–624

    Article  CAS  Google Scholar 

  8. Baczek T, Kaliszan R, Novotna K, Jandera P (2005) J Chromatogr A 1075:109–115

    Article  CAS  Google Scholar 

  9. Put R, Xu QS, Massart DL, Vander Heyden Y (2004) J Chromatogr A 1055:11–19

    Article  CAS  Google Scholar 

  10. Riahi S, Mousavi MF, Shamsipur M (2006) Talanta 69:736–740

    Article  CAS  Google Scholar 

  11. Amboni RDMC, Junkes BS, Heinzen VEF, Yunes RA (2002) J Mol Struct (Theochem) 579:53–62

    Article  CAS  Google Scholar 

  12. Zhou Y, Sun LL, Mei H, Li SZ (2006) Chromatographia 64:565–570

    Article  CAS  Google Scholar 

  13. Mazza CB, Whitehead CE, Breneman CM, Cramer SM (2006) Chromatographia 64:565–570

    Article  Google Scholar 

  14. Jakab A, Schubert G, Prodan M, Forgacs E (2002) Chromatographia 56:55–59

    Article  Google Scholar 

  15. Ghosh P, Chawla B, Joshi PV, Jaffe SB (2006) Energy Fuels 20:609–619

    Article  CAS  Google Scholar 

  16. Jalali-Heravi M, Fatemi MH (2001) J Chromatogr A 915:177–183

    Article  CAS  Google Scholar 

  17. Garakani-Nejad Z, Karlovits M, Demuth W, Stimpfl T, Vycudilik W, Jalali-Heravi M, Varmuza K (2004) J Chromatogr A 1028:287–295

    Article  Google Scholar 

  18. Acevedo-Martinez J, Escalona-Arranz JC, Villar-Rojas A, Tellez-Palmero F, Perez-Roses R, Gonzalez L, Carrasco-Velar R (2006) J Chromatogr A 1102:238–244

    Article  CAS  Google Scholar 

  19. Tulasamma P, Reddy KS (2006) J Mol Graph Model 25:507–513

    Article  CAS  Google Scholar 

  20. Heberger K, Kowalska T (1999) Chemometr Intell Lab Syst 47:205–217

    Article  CAS  Google Scholar 

  21. Depczynski U, Frost VJ, Molt K (2000) Anal Chim Acta 420:217–227

    Article  CAS  Google Scholar 

  22. Alsberg BK, Marchand-Geneste N, King RD (2000) Chemom Intell Lab Syst 54:75–91

    Article  CAS  Google Scholar 

  23. Jouanrimbaud D, Massart DL, Leardi R, Denoord OE (1995) Anal Chem 67:4295–4301

    Article  CAS  Google Scholar 

  24. Herrero A, Cruz OM (1999) Anal Chim Acta 378:245–259

    Article  CAS  Google Scholar 

  25. Leardi R, Boggia R, Terrile M, (1992) J Chemom 6:267–281

    Article  CAS  Google Scholar 

  26. Lucasius CB, Beckers MLM, Kateman G, (1994) Anal Chim Acta 286:135–153

    Article  CAS  Google Scholar 

  27. Todeschini R, Consonni V, Pavana M (Online) available: http://www.disat.unimib.it/vhm/

  28. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley–VCH, Weinheim

    Google Scholar 

  29. Kier LB, Hall LH (1986) Molecular connectivity in structure–activity analysis. RSP–Wiley, Chichester, UK

    Google Scholar 

  30. Konstantinova EV (1997) J Chem Inf Comput Sci 36:54–57

    Article  Google Scholar 

  31. Rucker G, Rucker C (1993) J Chem Inf Comput Sci 33:683–695

    Article  Google Scholar 

  32. Galvez J, Garcia R, Salabert MT, Soler R (1994) J Chem Inf Comput Sci 34:520–525

    Article  CAS  Google Scholar 

  33. Broto P, Moreau G, Vandicke C (1984) J Med Chem 19:66–70

    CAS  Google Scholar 

  34. Hunger J, Huttner G (1999) J Comput Chem 20:455–471

    Article  CAS  Google Scholar 

  35. Ahmad S, Gromiha MM (2003) J Comput Chem 24:1313–1320

    Article  CAS  Google Scholar 

  36. Waller CL, Bradley MP (1999) J Chem Inf Comput Sci 39:345–355

    CAS  Google Scholar 

  37. Aires-de-Sousa J, Hemmer MC, Gasteiger J (2002) Anal Chem 74:80–90

    Article  CAS  Google Scholar 

  38. Miller JC, Miller JN (1993) Statistics for analytical chemistry. 3rd edn. Ellis Horwood, Chichester

    Google Scholar 

  39. Kier LB, Hall LH (1986) Molecular connectivity in structure–activity analysis. Research Studies Press, New York

    Google Scholar 

  40. Tham SY, Agatonovic-Kustrin S (2002) J Pharm Biomed Anal 28:581–590

    Article  CAS  Google Scholar 

  41. Verloop A, Hoogenstraaten W, Tysker J (1976) Drug design, vol 7. Academic Press, New York

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Authors and Affiliations

  1. Institute of Petroleum Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran

    Siavash Riahi

  2. Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran, Iran

    Siavash Riahi, Mohammad Reza Ganjali, Eslam Pourbasheer & Parviz Norouzi

Authors
  1. Siavash Riahi
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  2. Mohammad Reza Ganjali
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  3. Eslam Pourbasheer
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  4. Parviz Norouzi
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Correspondence to Siavash Riahi.

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Riahi, S., Ganjali, M.R., Pourbasheer, E. et al. QSRR Study of GC Retention Indices of Essential-Oil Compounds by Multiple Linear Regression with a Genetic Algorithm. Chroma 67, 917–922 (2008). https://doi.org/10.1365/s10337-008-0608-4

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