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Analytical and Bioanalytical Chemistry

, Volume 410, Issue 25, pp 6549–6560 | Cite as

An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components

  • Chun Kiang Chua
  • Bo Lu
  • Yunbo Lv
  • Xiao Yu Gu
  • Ai Di Thng
  • Hua Jun Zhang
Research Paper

Abstract

Gas chromatography-mass spectrometry (GC-MS) is a versatile analytical method but its data is usually complicated by the presence of severely co-eluting and trace-level components. In this work, we introduce an optimized band-target entropy minimization approach for the analysis of complex mass spectral data. This new approach enables an automated mass spectral analysis which does not require any user-dependent inputs. Moreover, the approach provides improved sensitivity and accuracy for mass spectral reconstruction of severely co-eluting and trace-level components. The accuracy of our approach is compared to the automatic mass spectral deconvolution and identification system (AMDIS) with two controlled mixtures and a sample of Eucalyptus essential oil. Our approach was able to putatively identify 130 compounds in Eucalyptus essential oil, which was 46% in excess of that identified by AMDIS. This new approach is expected to benefit GC-MS analysis of complex mixtures such as biological samples and essential oils, in which the data are often complicated by co-eluting and trace-level components.

Graphical abstract

Keywords

Gas chromatography Mass spectrometry Entropy minimization Spectral deconvolution 

Notes

Acknowledgments

We would like to thank Dr. Ni Wangdong for providing valuable insights on how to draft the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they do not have a conflict of interest.

Supplementary material

216_2018_1260_MOESM1_ESM.pdf (1.2 mb)
ESM 1 (PDF 1220 kb)

References

  1. 1.
    Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat Protoc. 2006;1(1):387–96.CrossRefPubMedGoogle Scholar
  2. 2.
    Lai Z, Fiehn O. Mass spectral fragmentation of trimethylsilylated small molecules. Mass Spectrom Rev. 2016;37(3):245–57.CrossRefPubMedGoogle Scholar
  3. 3.
    Fiehn O. Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. TrAC Trends Anal Chem. 2008;27(3):261–9.CrossRefGoogle Scholar
  4. 4.
    Yang X, Zhang H, Liu Y, JW ZYC, Dong AJ, Zhao HT, et al. Multiresidue method for determination of 88 pesticides in berry fruits using solid-phase extraction and gas chromatography–mass spectrometry: determination of 88 pesticides in berries using SPE and GC–MS. Food Chem. 2011;127(2):855–65.CrossRefPubMedGoogle Scholar
  5. 5.
    de Freitas Ventura F, de Oliveira J, dos Reis Pedreira Filho W, Ribeiro MG. GC-MS quantification of organophosphorous pesticides extracted from XAD-2 sorbent tube and foam patch matrices. Anal Methods. 2012;4(11):3666–73.CrossRefGoogle Scholar
  6. 6.
    Stein SE. An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J Am Soc Mass Spectrom. 1999;10:770–81.CrossRefGoogle Scholar
  7. 7.
    Hiller K, Hangebrauk J, Jager C, Spura J, Schreiber K, Schomburg D. MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Anal Chem. 2009;81:3429–39.CrossRefPubMedGoogle Scholar
  8. 8.
    Duran AL, Yang J, Wang L, Sumner LW. Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics. 2003;19(17):2283–93.CrossRefPubMedGoogle Scholar
  9. 9.
    Broeckling CD, Reddy IR, Duran AL, Zhao X, Sumner LW. MET-IDEA: data extraction tool for mass spectrometry-based metabolomics. Anal Chem. 2006;78(13):4334–41.CrossRefPubMedGoogle Scholar
  10. 10.
    Luedemann A, Strassburg K, Erban A, Kopka J. TagFinder for the quantitative analysis of gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling experiments. Bioinformatics. 2008;24(5):732–7.CrossRefPubMedGoogle Scholar
  11. 11.
    O'Callaghan S, De Souza DP, Isaac A, Wang Q, Hodkinson L, Olshansky M, et al. PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools. BMC Bioinformatics. 2012;13(1):115.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ni Y, Su M, Qiu Y, Jia W, Du X. ADAP-GC 3.0: improved peak detection and deconvolution of co-eluting metabolites from GC/TOF-MS data for metabolomics studies. Anal Chem. 2016;88(17):8802–11.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Skov T, Bro R. Solving fundamental problems in chromatographic analysis. Anal Bioanal Chem. 2008;390(1):281–5.CrossRefPubMedGoogle Scholar
  14. 14.
    Amigo JM, Popielarz MJ, Callejón RM, Morales ML, Troncoso AM, Petersen MA, et al. Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis. J Chromatogr A. 2010;1217(26):4422–9.CrossRefPubMedGoogle Scholar
  15. 15.
    Kvalheim OM, Liang YZ. Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution. Anal Chem. 1992;64(8):936–46.CrossRefGoogle Scholar
  16. 16.
    Boelens HFM, Dijkstra RJ, Eilers PHC, Fitzpatrick F, Westerhuis JA. New background correction method for liquid chromatography with diode array detection, infrared spectroscopic detection and Raman spectroscopic detection. J Chromatogr A. 2004;1057(1–2):21–30.CrossRefPubMedGoogle Scholar
  17. 17.
    Li H, Hou J, Wang K, Zhang F. Resolution of multicomponent overlapped peaks. Talanta. 2006;70(2):336–43.CrossRefPubMedGoogle Scholar
  18. 18.
    Liland KH, Almøy T, Mevik B-H. Optimal choice of baseline correction for multivariate calibration of spectra. Appl Spectrosc. 2010;64(9):1007–16.CrossRefPubMedGoogle Scholar
  19. 19.
    Domingo-Almenara X, Perera A, Ramírez N, Cañellas N, Correig X, Brezmes J. Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. J Chromatogr A. 2015;1409:226–33.CrossRefPubMedGoogle Scholar
  20. 20.
    Ma P, Zhang Z, Zhou X, Yun Y, Liang Y, Lu H. Feature extraction from resolution perspective for gas chromatography-mass spectrometry datasets. RSC Adv. 2016;6(115):113997–4004.CrossRefGoogle Scholar
  21. 21.
    Bro R, Andersson CA, Kiers HAL. PARAFAC2—part II. Modeling chromatographic data with retention time shifts. J Chemom. 1999;13(3–4):295–309.CrossRefGoogle Scholar
  22. 22.
    Lu H, Liang Y, Dunn WB, Shen H, Kell DB. Comparative evaluation of software for deconvolution of metabolomics data based on GC-TOF-MS. TrAC Trends Anal Chem. 2008;27:215–27.CrossRefGoogle Scholar
  23. 23.
    Du X, Zeisel SH. Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives. Comput Struct Biotechnol J. 2013;4(5):e201301013.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chew W, Widjaja E, Garland M. Band-target entropy minimization (BTEM): an advanced method for recovering unknown pure component spectra. Application to the FTIR spectra of unstable organometallic mixtures. Organometallics. 2002;21(9):1982–90.CrossRefGoogle Scholar
  25. 25.
    Widjaja E, Li C, Garland M. Semi-batch homogeneous catalytic in-situ spectroscopic data. FTIR spectral reconstructions using band-target entropy minimization (BTEM) without spectral preconditioning. Organometallics. 2002;21(9):1991–7.CrossRefGoogle Scholar
  26. 26.
    Tan S-T, Zhu H, Chew W. Self-modeling curve resolution of multi-component vibrational spectroscopic data using automatic band-target entropy minimization (AutoBTEM). Anal Chim Acta. 2009;639(1):29–41.CrossRefPubMedGoogle Scholar
  27. 27.
    Zhang HJ, Garland M, Zeng YZ, Wu P. Weighted two-band target entropy minimization for the reconstruction of pure component mass spectra: simulation studies and the application to real systems. J Am Soc Mass Spectrom. 2003;14(11):1295–305.CrossRefPubMedGoogle Scholar
  28. 28.
    Zhang HJ, Chew W, Garland M. The multi-reconstruction entropy minimization method: unsupervised spectral reconstruction of pure components from mixture spectra, without the use of a priori information. Appl Spectrosc. 2007;61(12):1366–72.CrossRefPubMedGoogle Scholar
  29. 29.
    Widjaja E, Crane N, Chen TC, Morris MD, Ignelzi MA, McCreadie BR. Band-target entropy minimization (BTEM) applied to hyperspectral Raman image data. Appl Spectrosc. 2003;57(11):1353–62.CrossRefPubMedGoogle Scholar
  30. 30.
    Widjaja E, Seah RKH. Application of Raman microscopy and band-target entropy minimization to identify minor components in model pharmaceutical tablets. J Pharm Biomed Anal. 2008;46(2):274–81.CrossRefPubMedGoogle Scholar
  31. 31.
    Gao F, Zhang H, Guo L, Garland M. Application of the BTEM family of algorithms to reconstruct individual UV-Vis spectra from multi-component mixtures. Chemom Intell Lab Syst. 2009;95(1):94–100.CrossRefGoogle Scholar
  32. 32.
    Chua CK, Lv Y, Zhang HJ, Gu XY. Dynamic background noise removal from overlapping GC-MS peaks via an entropy minimization algorithm. Anal Methods. 2017;9(18):2667–72.CrossRefGoogle Scholar
  33. 33.
    Xia Z, Liu Y, Cai W, Shao X. Band target entropy minimization for retrieving the information of individual components from overlapping chromatographic data. J Chromatogr A. 2015;1411:110–5.CrossRefPubMedGoogle Scholar
  34. 34.
    Meija J, Mester Z, D’Ulivo A. Mass spectrometric separation and quantitation of overlapping isotopologues. H2O/HOD/D2O and H2Se/HDSe/D2Se mixtures. J Am Soc Mass Spectrom. 2006;17(7):1028–36.CrossRefPubMedGoogle Scholar
  35. 35.
    Guo LF, Kooli F, Garland M. A general method for the recovery of pure powder XRD patterns from complex mixtures using no a priori information: application of band-target entropy minimization (BTEM) to materials characterization of inorganic mixtures. Anal Chim Acta. 2004;517(1–2):229–36.CrossRefGoogle Scholar
  36. 36.
    Guo L, Sprenger P, Garland M. A combination of spectral re-alignment and BTEM for the estimation of pure component NMR spectra from multi-component non-reactive and reactive systems. Anal Chim Acta. 2008;608(1):48–55.CrossRefPubMedGoogle Scholar
  37. 37.
    National Pharmacopoeia Committee. Pharmacopoeia of the People's Republic of China. Beijing: Chemical Industry Press; 2005.Google Scholar
  38. 38.
    Zeng YZ, Garland M. An improved algorithm for estimating pure component spectra in exploratory chemometric studies based on entropy minimization. Anal Chim Acta. 1998;359(2):303–10.CrossRefGoogle Scholar
  39. 39.
    Tyagi AK, Malik A. Antimicrobial potential and chemical composition of Eucalyptus globulus oil in liquid and vapour phase against food spoilage microorganisms. Food Chem. 2011;126(1):228–35.CrossRefGoogle Scholar
  40. 40.
    Barbosa LC, Filomeno CA, Teixeira RR. Chemical variability and biological activities of Eucalyptus spp. essential oils. Molecules. 2016;21(12):1671.CrossRefGoogle Scholar
  41. 41.
    Likić VA. Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS). BioData Min. 2009;2(1):6.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chun Kiang Chua
    • 1
  • Bo Lu
    • 2
  • Yunbo Lv
    • 1
  • Xiao Yu Gu
    • 3
  • Ai Di Thng
    • 1
  • Hua Jun Zhang
    • 2
    • 4
  1. 1.Chemopower Technology Pte. Ltd.SingaporeSingapore
  2. 2.State Key Laboratory for Enzyme Technology, National Engineering Research Center for Non-food Biorefinery, Guangxi Key Laboratory of BiorefineryGuangxi Academy of SciencesNanningChina
  3. 3.Guangxi Botanical Garden of Medicinal PlantsNanningChina
  4. 4.National University of Singapore Suzhou Research InstituteSuzhouChina

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