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Journal of Analytical Chemistry

, Volume 72, Issue 14, pp 1419–1425 | Cite as

Use of PLS Discriminant Analysis for Revealing the Absence of a Compound in an Electron Ionization Mass Spectral Database

  • K. M. SotnezovaEmail author
  • A. S. Samokhin
  • I. A. Revelsky
Articles

Abstract

A mathematical model is proposed for revealing the absence of a compound to be identified in an electron impact mass spectral library. The mathematical model (developed based on PLS Discriminant Analysis) can be represented as a “black box” which provides an answer whether a compound to be sought is absent or present in a database. The match factors of top ten candidates among the possible ones were used as input data. More than 5000 objects (mass spectra) were used at the steps of training, validation, and testing. The developed classification model provides correct prediction (of whether a compound is absent from the library) in 28.4% cases, while only 1.2% of compounds present in the database were incorrectly classified as the absent ones.

Keywords

identification of organic compounds GC/MS mass spectral library mass spectral database classification PLS discriminant analysis 

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References

  1. 1.
    Stein, S., Anal. Chem., 2012, vol. 84, no. 17, p. 7274.CrossRefGoogle Scholar
  2. 2.
    Milman, B.L. and Zhurkovich, I.K., TrAC, Trends Anal. Chem., 2016, vol. 80, p. 636.CrossRefGoogle Scholar
  3. 3.
    Stein, S.E. and Scott, D.R., J. Am. Soc. Mass Spectrom., 1994, vol. 5, no. 9, p. 859.CrossRefGoogle Scholar
  4. 4.
    McLafferty, F.W., Hertel, R.H., and Villwock, R.H., Org. Mass Spectrom., 1974, vol. 9, no. 7, p. 690.CrossRefGoogle Scholar
  5. 5.
    Neudert, R., Bremser, W., and Wagner, H., Org. Mass Spectrom., 1978, vol. 22, no. 6, p. 321.CrossRefGoogle Scholar
  6. 6.
    Domokos, L. and Henneberg, D., Anal. Chim. Acta, 1984, vol. 165, p. 75.CrossRefGoogle Scholar
  7. 7.
    Varmuza, K., Int. J. Mass Spectrom. Ion Processes, 1992, vol. 118–119, p. 811.CrossRefGoogle Scholar
  8. 8.
    Domokos, L., Henneberg, D., and Wiemann, B., Anal. Chim. Acta, 1984, vol. 5, p. 316.Google Scholar
  9. 9.
    Samokhin, A., Sotnezova, K., Lashin, V., and Revelsky, I., J. Mass Spectrom, 2015, vol. 50, no. 6, p. 820.CrossRefGoogle Scholar
  10. 10.
    Koo, I., Zhang, X., and Kim, S., Anal. Chem., 2011, vol. 83, no. 14, p. 5631.CrossRefGoogle Scholar
  11. 11.
    Stein, S.E., J. Am. Soc. Mass Spectrom., 1999, vol. 10, no. 8, p. 770.CrossRefGoogle Scholar
  12. 12.
    Meyer, M.R., Peters, F.T., and Maurer, H.H., Clin. Chem., 2010, vol. 56, no. 4, p. 575.CrossRefGoogle Scholar
  13. 13.
    Samokhin, A.S., Revel’skii, A.I., Chepelyanskii, D.A., Revel’skii, I.A., Mass-Spektrom., 2011, vol. 8, no. 1, p. 65.Google Scholar
  14. 14.
    Chemometrics course. Classification. http://rcs.chemometrics. ru/Tutorials/classification.htm. Cited August 24, 2016.Google Scholar
  15. 15.
    Dahn, V. and Nguyen David, M., Bioinformatics, 2002, vol. 18, no. 1, p. 39.CrossRefGoogle Scholar
  16. 16.
    Zhu, W., Wang, X., Ma, Y., Rao, M., Glimm, J., and Kovach, J.S., Proc. Natl. Acad. Sci. U. S. A., 2003, vol. 100, no. 25, p. 14666.CrossRefGoogle Scholar
  17. 17.
    Gupta, S., Variyar, P.S., and Sharma, A., Radiat. Phys. Chem., 2015, vol. 106, p. 348.CrossRefGoogle Scholar
  18. 18.
    Ruiz-Samblas, C., Tres, A., Koot, A., Ruth, S.M., Gonzalez-Casado, A., and Cuadross-Rodriguez, L., Food Chem., 2012, vol. 134, no. 1, p. 589.CrossRefGoogle Scholar
  19. 19.
    Baroni, M.V., Nores, M.L., Del Pilar Diaz, M., Chiabrando, G.A., Fassano, J.P., Costa, C., and Wunderlin, D.A., J. Agric. Food Chem., 2006, vol. 54, no. 19, p. 7235.CrossRefGoogle Scholar
  20. 20.
    Villagra, E., Santos, L.S., Gontijovaz, B., Eberlin, M.N., and Laurie, F.V., Food Chem., 2012, vol. 131, no. 2, p. 692.CrossRefGoogle Scholar
  21. 21.
    Cynkar, W., Dambergs, R., Smith, P., and Cozzolino, D., Anal. Chim. Acta, 2010, vol. 660, nos. 1–2, p. 227.CrossRefGoogle Scholar
  22. 22.
    Curry, Bo. and Rumelhart, D.E., Tetrahedron Comput. Methodol., 1990, vol. 3, nos. 3–4, p. 213.CrossRefGoogle Scholar
  23. 23.
    Lohninger, H. and Varmuza, K., Anal. Chem., 1987, vol. 59, no. 2, p. 236.CrossRefGoogle Scholar
  24. 24.
    Varmuza, K. and Werther, W., Mass spectral classifiers for supporting systematic structure elucidation, J. Chem. Inf. Comput. Sci., 1996, vol. 36, no. 2, p. 323.CrossRefGoogle Scholar
  25. 25.
    Werther, W., Lohninger, H., Stancl, F., and Varmuza, K., Chemom. Intell. Lab. Syst., 1994, vol. 22, no. 1, p. 63.CrossRefGoogle Scholar
  26. 26.
    Vogt, L., Groger, T., and Zimmermann, R., J. Chromatogr., A, 2007, vol. 1150, nos. 1–2, p. 2.CrossRefGoogle Scholar
  27. 27.
    Stein, S.E., J. Am. Soc. Mass Spectrom., 1994, vol. 5, no. 4, p. 316.CrossRefGoogle Scholar
  28. 28.
    NIST MS Search User Guide, Gaithersburg: Natl. Inst. Standards Technol., 2008.Google Scholar
  29. 29.
    Ausloos, P., Clifton, C.L., Mikaya, A.I., Stein, S.E., Tchekhovskoi, D.V., Sparkman, O.D., Zaikin, V., and Zhu, D., J. Am. Soc. Mass Spectrom., 1999, vol. 10, no. 4, p. 287.CrossRefGoogle Scholar
  30. 30.
    http://www.chemometrics.ru/materials/textbooks/projection. htm. Cited August 29, 2016.Google Scholar
  31. 31.
    Pomerantsev, A.L., Chemometrics in Excel, Wiley, 2014.CrossRefGoogle Scholar
  32. 32.
    Sekulic, S., Seasholtz, M.-B., Wang, Z., Kowalski, B., Lee, S., and Holt, B., Anal. Chem., 1993, vol. 65, no. 19, p. 835.CrossRefGoogle Scholar
  33. 33.
    Berglund, A. and Wold, S., J. Chemom., 1997, vol. 11, no. 2, p. 141.CrossRefGoogle Scholar
  34. 34.
    Geladi, P., Hadjiiski, L., and Hopke, P., Chemom. Intell. Lab. Syst., 1999, vol. 47, no. 2, p. 165.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • K. M. Sotnezova
    • 1
    Email author
  • A. S. Samokhin
    • 1
  • I. A. Revelsky
    • 1
  1. 1.Department of ChemistryMoscow State UniversityMoscowRussia

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