Skip to main content
Log in

Automated metabolite identification from biological fluid 1H NMR spectra

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript



Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.


This paper introduces a new, automated computational scheme for the identification of metabolites in 1D 1H NMR spectra based on the Human Metabolome Database.


The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.


The proposed scheme has been tested on the 1D 1H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.


This new robust scheme accomplishes to automatically identify peak resonances in 1H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  • Anderson, P. E., Mahle, D. A., Doom, T. E., Reo, N. V., DelRaso, N. J., & Raymer, M. L. (2010). Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data. Metabolomics, 7(2), 179–190. doi:10.1007/s11306-010-0242-7.

    Article  CAS  Google Scholar 

  • Anderson, P. E., Reo, N. V., DelRaso, N. J., Doom, T. E., & Raymer, M. L. (2008). Gaussian binning: A new kernel-based method for processing NMR spectroscopic data for metabolomics. Metabolomics, 4(3), 261–272. doi:10.1007/s11306-008-0117-3.

    Article  CAS  Google Scholar 

  • Chignola, F., Mari, S., Stevens, T. J., Fogh, R. H., Mannella, V., Boucher, W., & Musco, G. (2011). The CCPN metabolomics Project: A fast protocol for metabolite identification by 2D-NMR. Bioinformatics (Oxford, England), 27(6), 885–886. doi:10.1093/bioinformatics/btr013.

    Article  CAS  Google Scholar 

  • Davis, R. A., Charlton, A. J., Godward, J., Jones, S. A., Harrison, M., & Wilson, J. C. (2007). Adaptive binning: An improved binning method for metabolomics data using the undecimated wavelet transform. Chemometrics and Intelligent Laboratory Systems, 85(1), 144–154. doi:10.1016/j.chemolab.2006.08.014.

    Article  CAS  Google Scholar 

  • De Meyer, T., Sinnaeve, D., Van Gasse, B., Tsiporkova, E., Rietzschel, E. R., De Buyzere, M. L., et al. (2008). NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Analytical Chemistry, 80(10), 3783–3790. doi:10.1021/ac7025964.

    Article  CAS  PubMed  Google Scholar 

  • Deng, L., Gu, H., Zhu, J., Nagana Gowda, G. A., Djukovic, D., Chiorean, E. G., Raftery, D. (2016). Combining NMR and LC/MS using backward variable elimination: Metabolomics analysis of colorectal cancer, polyps, and healthy controls. Analytical chemistry, 88(16), 7975–7983. doi:10.1021/acs.analchem.6b00885.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Domingo-Almenara, X., Brezmes, J., Vinaixa, M., Samino, S., Ramirez, N., Ramon-Krauel, M., et al. (2016). eRah: A computational tool integrating spectral deconvolution and alignment with quantification and identification of metabolites in GC/MS-based metabolomics. Analytical Chemistry, 88(19), 9821–9829. doi:10.1021/acs.analchem.6b02927.

    Article  CAS  PubMed  Google Scholar 

  • Everett, J. R. (2015). A new paradigm for known metabolite identification in metabonomics/metabolomics: Metabolite identification efficiency. Computational and Structural Biotechnology Journal, 13, 131–144. doi:10.1016/j.csbj.2015.01.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fischer, K., Kettunen, J., Würtz, P., Haller, T., Havulinna, A. S., Kangas, A. J., et al. (2014). Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: An observational study of 17,345 persons. PLoS Medicine, 11(2), e1001606. doi:10.1371/journal.pmed.1001606.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fotakis, C., Zoga, M., Baskakis, C., Tsiaka, T., Boutsikou, T., Briana, D. D., et al. (2016). Investigating the metabolic fingerprint of term infants with normal and increased fetal growth. RSC Advances, 6(83), 79325–79334. doi:10.1039/C6RA12403H.

    Article  CAS  Google Scholar 

  • Gralka, E., Luchinat, C., Tenori, L., Ernst, B., Thurnheer, M., & Schultes, B. (2015). Metabolomic fingerprint of severe obesity is dynamically affected by bariatric surgery in a procedure-dependent manner. American Journal of Clinical Nutrition, 102(6), 1313–1322. doi:10.3945/ajcn.115.110536.

    Article  CAS  PubMed  Google Scholar 

  • Haddad, R. A., & Akansu, A. N. (1991). A class of fast Gaussian binomial filters for speech and image processing. IEEE Transactions on Signal Processing, 39(3), 723–727. doi:10.1109/78.80892.

    Article  Google Scholar 

  • Hao, J., Astle, W., De Iorio, M., & Ebbels, T. M. D. (2012). BATMAN—An R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics (Oxford, England), 28(15), 2088–2090. doi:10.1093/bioinformatics/bts308.

    Article  CAS  Google Scholar 

  • Hart, C. D., Vignoli, A., Tenori, L., Uy, G. L., Van To, T., Adebamowo, C., et al. (2017). Serum metabolomic profiles identify ER-positive early breast cancer patients at increased risk of disease recurrence in a multicenter population. Clinical Cancer Research, 23(6), 1422–1431. doi:10.1158/1078-0432.CCR-16-1153.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jobard, E., Pontoizeau, C., Blaise, B. J., Bachelot, T., Elena-Herrmann, B., & Trédan, O. (2014). A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer. Cancer Letters, 343(1), 33–41. doi:10.1016/j.canlet.2013.09.011.

    Article  CAS  PubMed  Google Scholar 

  • Kale, N. S., Haug, K., Conesa, P., Jayseelan, K., Moreno, P., Rocca-Serra, P., Nainala, V. C., Spicer, R. A., Williams, M., Li, X., Salek, R. M., Griffin, J. L., & Steinbeck, C. (2016). MetaboLights: An open-access database repository for metabolomics data. Current Protocols in Bioinformatics, 53, 14.13.1–14.13.18. doi:10.1002/0471250953.bi1413s53.

    Article  Google Scholar 

  • Kang, J., Zhu, L., Lu, J., & Zhang, X. (2015). Application of metabolomics in autoimmune diseases: Insight into biomarkers and pathology. Journal of Neuroimmunology, 279, 25–32. doi:10.1016/j.jneuroim.2015.01.001.

    Article  CAS  PubMed  Google Scholar 

  • Kordalewska, M., & Markuszewski, M. J. (2015). Metabolomics in cardiovascular diseases. Journal of Pharmaceutical and Biomedical Analysis, 113, 121–136. doi:10.1016/j.jpba.2015.04.021.

    Article  CAS  PubMed  Google Scholar 

  • Larive, C. K., Barding, G. A., & Dinges, M. M. (2015). NMR spectroscopy for metabolomics and metabolic profiling. Analytical Chemistry, 87(1), 133–146. doi:10.1021/ac504075g.

    Article  CAS  PubMed  Google Scholar 

  • Lenz, E. M., & Wilson, I. D. (2007). Analytical strategies in metabonomics. Journal of Proteome Research, 6(2), 443–458. doi:10.1021/pr0605217.

    Article  CAS  PubMed  Google Scholar 

  • Li, L., Li, R., Zhou, J., Zuniga, A., Stanislaus, A. E., Wu, Y., et al. (2013). MyCompoundID: Using an evidence-based metabolome library for metabolite identification. Analytical Chemistry, 85(6), 3401–3408. doi:10.1021/ac400099b.

    Article  CAS  PubMed  Google Scholar 

  • Lindon, J. C., & Nicholson, J. K. (2008). Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery. TrAC Trends in Analytical Chemistry, 27(3), 194–204. doi:10.1016/j.trac.2007.08.009.

    Article  CAS  Google Scholar 

  • Mercier, P., Lewis, M. J., Chang, D., Baker, D., & Wishart, D. S. (2011). Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra. Journal of biomolecular NMR, 49(3–4), 307–323. doi:10.1007/s10858-011-9480-x.

    Article  CAS  PubMed  Google Scholar 

  • Mihaleva, V. V., Verhoeven, H. A., de Vos, R. C. H., Hall, R. D., & van Ham, R. C. H. J. (2009). Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index. Bioinformatics (Oxford, England), 25(6), 787–794. doi:10.1093/bioinformatics/btp056.

    Article  CAS  Google Scholar 

  • Psychogios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., et al. (2011). The human serum metabolome. PloS ONE, 6(2), e16957. doi:10.1371/journal.pone.0016957.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ravanbakhsh, S., Liu, P., Bjorndahl, T. C., Bjordahl, T. C., Mandal, R., Grant, J. R., et al. (2015). Accurate, fully-automated NMR spectral profiling for metabolomics. PloS ONE, 10(5), e0124219. doi:10.1371/journal.pone.0124219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Singh, A., Sharma, R. K., Chagtoo, M., Agarwal, G., George, N., Sinha, N., & Godbole, M. M. (2017). 1H NMR metabolomics reveals association of high expression of inositol 1, 4, 5 trisphosphate receptor and metabolites in breast cancer patients. PloS ONE, 12(1), e0169330. doi:10.1371/journal.pone.0169330.

    Article  PubMed  PubMed Central  Google Scholar 

  • Smolinska, A., Blanchet, L., Buydens, L. M. C., & Wijmenga, S. S. (2012). NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review. Analytica Chimica Acta, 750, 82–97. doi:10.1016/j.aca.2012.05.049.

    Article  CAS  PubMed  Google Scholar 

  • Sousa, S. A. A., Magalhães, A., & Ferreira, M. M. C. (2013). Optimized bucketing for NMR spectra: Three case studies. Chemometrics and Intelligent Laboratory Systems, 122, 93–102. doi:10.1016/j.chemolab.2013.01.006.

    Article  CAS  Google Scholar 

  • Tardivel, P. J. C., Canlet, C., Lefort, G., Tremblay-Franco, M., Debrauwer, L., Concordet, D., & Servien, R. (2017). ASICS: An automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, 13(10), 109. doi:10.1007/s11306-017-1244-5.

    Article  CAS  Google Scholar 

  • Tulpan, D., Léger, S., Belliveau, L., Culf, A., & Cuperlović-Culf, M. (2011). MetaboHunter: An automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures. BMC Bioinformatics, 12, 400. doi:10.1186/1471-2105-12-400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Research, 41(Database issue), D801-7. doi:10.1093/nar/gks1065.

    Article  CAS  PubMed  Google Scholar 

  • Wruck, W., Kashofer, K., Rehman, S., Daskalaki, A., Berg, D., Gralka, E., et al. (2015). Multi-omic profiles of human non-alcoholic fatty liver disease tissue highlight heterogenic phenotypes. Scientific Data, 2, 150068. doi:10.1038/sdata.2015.68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zheng, C., Zhang, S., Ragg, S., Raftery, D., & Vitek, O. (2011). Identification and quantification of metabolites in (1)H NMR spectra by Bayesian model selection. Bioinformatics (Oxford, England), 27(12), 1637–1644. doi:10.1093/bioinformatics/btr118.

    Article  CAS  Google Scholar 

Download references


This work was funded by a State Scholarships Foundation (IKY) Fellowship of Excellence for postgraduate studies in Greece—Siemens Program. The authors confirm that the funder had no influence over the study design, content of the paper, or selection of this journal.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Panagiotis Zoumpoulakis.

Additional information

Binary file freely available for download at

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Filntisi, A., Fotakis, C., Asvestas, P. et al. Automated metabolite identification from biological fluid 1H NMR spectra. Metabolomics 13, 146 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: