, Volume 8, Issue 5, pp 761–770 | Cite as

PTR-ToF-MS and data mining methods: a new tool for fruit metabolomics

  • Luca Cappellin
  • Christos Soukoulis
  • Eugenio Aprea
  • Pablo Granitto
  • Nicola Dallabetta
  • Fabrizio Costa
  • Roberto Viola
  • Tilmann D. Märk
  • Flavia Gasperi
  • Franco BiasioliEmail author
Original Article


Proton Transfer Reaction-Mass Spectrometry (PTR-MS) in its recently developed implementation based on a time-of-flight mass spectrometer (PTR-ToF-MS) has been evaluated as a possible tool for rapid non-destructive investigation of the volatile compounds present in the metabolome of apple cultivars and clones. Clone characterization is a cutting-edge problem in technical management and royalty application, not only for apple, aiming at unveiling real properties which differentiate the mutated individuals. We show that PTR-ToF-MS coupled with multivariate and data mining methods may successfully be employed to obtain accurate varietal and clonal apple fingerprint. In particular, we studied the VOC emission profile of five different clones belonging to three well known apple cultivars, such as ‘Fuji’, ‘Golden Delicious’ and ‘Gala’. In all three cases it was possible to set classification models which can distinguish all cultivars and some of the clones considered in this study. Furthermore, in the case of ‘Gala’ we also identified estragole and hexyl 2-methyl butanoate contributing to such clone characterization. Beside its applied relevance, no data on the volatile profiling of apple clones are available so far, our study indicates the general viability of a metabolomic approach for volatile compounds in fruit based on rapid PTR-ToF-MS fingerprinting.


Proton transfer reaction-mass spectrometry Apple (Malus domesticaCultivars Clones Chemometrics Data mining Marker identification 



Work partially supported by PAT (AP 2009-2011). PMG acknowledges partial support from ANPCyT (grant PICT 237/08).


  1. Annesley, T. M. (2003). Ion suppression in mass spectrometry. Clinical Chemistry, 49, 1041–1044. doi: 10.1373/49.7.1041.PubMedCrossRefGoogle Scholar
  2. Aprea, E., Biasioli, F., Sani, G., et al. (2006). Proton transfer reaction-mass spectrometry (PTR-MS) headspace analysis for rapid detection of oxidative alteration of olive oil. Journal of Agriculture and Food Chemistry, 54, 7635–7640.CrossRefGoogle Scholar
  3. Aprea, E., Biasioli, F., Carlin, S., et al. (2007). Rapid white truffle headspace analysis by proton transfer reaction mass spectrometry and comparison with solid-phase microextraction coupled with gas chromatography/mass spectrometry. Rapid Communications in Mass Spectrometry, 21, 2564–2572.PubMedCrossRefGoogle Scholar
  4. Aprea, E., Biasioli, F., Carlin, S., et al. (2009). Investigation of Volatile compounds in two raspberry cultivars by two headspace techniques: Solid-phase microextraction/gas chromatography–mass spectrometry (SPME/GC–MS) and proton-transfer reaction–mass spectrometry (PTR–MS). Journal of Agriculture and Food Chemistry, 57, 4011–4018. doi: 10.1021/jf803998c.CrossRefGoogle Scholar
  5. Aprea, E., Gika, H., Carlin, S., et al. (2011). Metabolite profiling on apple volatile content based on solid phase microextraction and gas-chromatography time of flight mass spectrometry. Journal of Chromatography,. doi: 10.1016/j.chroma.2011.05.019.PubMedGoogle Scholar
  6. Araghipour, N., Colineau, J., Koot, A., et al. (2008). Geographical origin classification of olive oils by PTR-MS. Food Chemistry, 108, 374–383. doi: 10.1016/j.foodchem.2007.10.056.CrossRefGoogle Scholar
  7. Biasioli, F., Gasperi, F., Aprea, E., et al. (2006). Correlation of PTR-MS spectral fingerprints with sensory characterisation of flavour and odour profile of “Trentingrana” cheese. Food Quality and Preference, 17, 63–75. doi: 10.1016/j.foodqual.2005.06.004.CrossRefGoogle Scholar
  8. Biasioli, F., Yeretzian, C., Gasperi, F., & Märk, T. D. (2011a). PTR-MS monitoring of VOCs and BVOCs in food science and technology. TRAC-Trends in Analytical Chemistry, 30, 968–977. doi: 10.1016/j.trac.2011.03.009.CrossRefGoogle Scholar
  9. Biasioli, F., Yeretzian, C., Märk, T. D., et al. (2011b). Direct-injection mass spectrometry adds the time dimension to (B)VOC analysis. TRAC-Trends in Analytical Chemistry, 30, 1003–1017. doi: 10.1016/j.trac.2011.04.005.CrossRefGoogle Scholar
  10. Blake, R., Monks, P., & Ellis, A. (2009). Proton-transfer reaction mass spectrometry. Chemical Reviews, 109, 861–896. doi: 10.1021/cr800364q.PubMedCrossRefGoogle Scholar
  11. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
  12. Cajka, T., Riddellova, K., Tomaniova, M., & Hajslova, J. (2010). Ambient mass spectrometry employing a DART ion source for metabolomic fingerprinting/profiling: a powerful tool for beer origin recognition. Metabolomics,. doi: 10.1007/s11306-010-0266-z.Google Scholar
  13. Cappellin, L., Biasioli, F., Fabris, A., et al. (2010a). Improved mass accuracy in PTR-TOF-MS: Another step towards better compound identification in PTR-MS. International Journal of Mass Spectrometry and Ion Physics, 290, 60–63. doi: 10.1016/j.ijms.2009.11.007.Google Scholar
  14. Cappellin, L., Probst, M., Limtrakul, J., et al. (2010b). Proton transfer reaction rate coefficients between H3O + and some sulphur compounds. International Journal of Mass Spectrometry and Ion Physics, 295, 43–48. doi: 10.1016/j.ijms.2010.06.023.Google Scholar
  15. Cappellin, L., Biasioli, F., Granitto, P., et al. (2011a). On data analysis in PTR-TOF-MS: From raw spectra to data mining. Sensor and Actuators B-Chemical, 155, 183–190. doi: 10.1016/j.snb.2010.11.044.CrossRefGoogle Scholar
  16. Cappellin, L., Biasioli, F., Schuhfried, E., et al. (2011b). Extending the dynamic range of proton transfer reaction time-of-flight mass spectrometers by a novel dead time correction. Rapid Communications in Mass Spectrometry, 25, 179–183.PubMedCrossRefGoogle Scholar
  17. Costa, F., Peace, C. P., Stella, S., et al. (2010a). QTL dynamics for fruit firmness and softening around an ethylene-dependent polygalacturonase gene in apple (Malusxdomestica Borkh.). Journal of Experimental Botany, 61, 3029–3039. doi: 10.1093/jxb/erq130.PubMedCrossRefGoogle Scholar
  18. Costa, F., Alba, R., Schouten, H., et al. (2010b). Use of homologous and heterologous gene expression profiling tools to characterize transcription dynamics during apple fruit maturation and ripening. BMC Plant Biology, 10, 229. doi: 10.1186/1471-2229-10-229.PubMedCrossRefGoogle Scholar
  19. De Gouw, J., & Warneke, C. (2007). Measurements of volatile organic compounds in the earth’s atmosphere using proton-transfer-reaction mass spectrometry. Mass Spectrometry Reviews, 26, 223–257. doi: 10.1002/mas.20119.PubMedCrossRefGoogle Scholar
  20. De Gouw J, Goldan P, Warneke C, et al. (2003) Validation of proton transfer reaction-mass spectrometry (PTR-MS) measurements of gas-phase organic compounds in the atmosphere during the New England air quality study (NEAQS) in 2002. Journal of Geophysics Research Atmosphere 108. doi: 10.1029/2003JD003863.
  21. Dunn, W. B., & Ellis, D. I. (2005). Metabolomics: Current analytical platforms and methodologies. TrAC-Trends in Analytical Chemistry, 24, 285–294. doi: 10.1016/j.trac.2004.11.021.CrossRefGoogle Scholar
  22. Dunn, W. B., Bailey, N. J. C., & Johnson, H. E. (2005). Measuring the metabolome: Current analytical technologies. Analyst, 130, 606. doi: 10.1039/b418288j.PubMedCrossRefGoogle Scholar
  23. Fabris, A., Biasioli, F., Granitto, P., et al. (2010). PTR-TOF-MS and data-mining methods for rapid characterisation of agro-industrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese. Journal of Mass Spectrometry and Ion Physics, 45, 1065–1074.Google Scholar
  24. Favé, G., Beckmann, M., Lloyd, A. J., et al. (2011). Development and validation of a standardized protocol to monitor human dietary exposure by metabolite fingerprinting of urine samples. Metabolomics,. doi: 10.1007/s11306-011-0289-0.PubMedGoogle Scholar
  25. Forneck, A. (2005). Plant breeding: Clonality—a concept for stability and variability during vegetative propagation. In K. Esser, U. Lüttge, W. Beyschlag, & J. Murata (Eds.), Progress in botany (pp. 164–183). Berlin: Springer.CrossRefGoogle Scholar
  26. Granitto, P., Furlanello, C., Biasioli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83, 83–90. doi: 10.1016/j.chemolab.2006.01.007.CrossRefGoogle Scholar
  27. Granitto, P., Biasioli, F., Aprea, E., et al. (2007a). Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques. Sensor Actuators B-Chemistry, 121, 379–385. doi: 10.1016/j.snb.2006.03.047.CrossRefGoogle Scholar
  28. Granitto, P., Gasperi, F., Biasioli, F., et al. (2007b). Modern data mining tools in descriptive sensory analysis: A case study with a random forest approach. Food Quality and Preferences, 18, 681–689. doi: 10.1016/j.foodqual.2006.11.001.CrossRefGoogle Scholar
  29. Greenwald, R., Fitzpatrick, A. M., Gaston, B., et al. (2010). Breath formate is a marker of airway s-nitrosothiol depletion in severe asthma. PLoS ONE, 5, e11919. doi: 10.1371/journal.pone.0011919.PubMedCrossRefGoogle Scholar
  30. Gu, H., Pan, Z., Xi, B., et al. (2011). Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer. Analytica Chimica Acta, 686, 57–63. doi: 10.1016/j.aca.2010.11.040.PubMedCrossRefGoogle Scholar
  31. Han, J., Datla, R., Chan, S., & Borchers, C. H. (2009). Mass spectrometry-based technologies for high-throughput metabolomics. Bioanalysis, 1, 1665–1684. doi: 10.4155/bio.09.158.PubMedCrossRefGoogle Scholar
  32. Herbig, J., Müller, M., Schallhart, S., et al. (2009). On-line breath analysis with PTR-TOF. Journal of Breath Research, 3, 027004. doi: 10.1088/1752-7155/3/2/027004.PubMedCrossRefGoogle Scholar
  33. Højer-Pedersen, J., Smedsgaard, J., & Nielsen, J. (2008). The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry. Metabolomics, 4, 393–405. doi: 10.1007/s11306-008-0132-4.CrossRefGoogle Scholar
  34. Jolliffe, I. (2002). Principal component analysis. New York: Springer.Google Scholar
  35. Jordan, A., Haidacher, S., Hanel, G., et al. (2009). A high resolution and high sensitivity proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS). International Journal of Mass Spectrometry and Ion Physics, 286, 122–128. doi: 10.1016/j.ijms.2009.07.005.Google Scholar
  36. Lindinger, W., Hansel, A., & Jordan, A. (1998). On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MS)—Medical applications, food control and environmental research. International Journal of Mass Spectrometry and Ion Physics, 173, 191–241.CrossRefGoogle Scholar
  37. Mattoli, L., Cangi, F., Ghiara, C., et al. (2010). A metabolite fingerprinting for the characterization of commercial botanical dietary supplements. Metabolomics,. doi: 10.1007/s11306-010-0268-x.Google Scholar
  38. McDougall, G., Martinussen, I., & Stewart, D. (2008). Towards fruitful metabolomics: High throughput analyses of polyphenol composition in berries using direct infusion mass spectrometry☆. Journal of Chromatography B, 871, 362–369. doi: 10.1016/j.jchromb.2008.06.032.CrossRefGoogle Scholar
  39. Müller, M., Graus, M., Ruuskanen, T. M., et al. (2010). First eddy covariance flux measurements by PTR-TOF. Atmospheric Measurement Techniques, 3, 387–395. doi: 10.5194/amt-3-387-2010.CrossRefGoogle Scholar
  40. Paillard, N. M. M. (1990). The flavour of apples, pears and quinces. In I. D. Morton & A. J. Macleod (Eds.), Food flavours part c the flavour of fruit (pp. 1–41). Amsterdam: Elsevier.Google Scholar
  41. Peñuelas, J., & Staudt, M. (2010). BVOCs and global change. Trends in Plant Science, 15, 133–144. doi: 10.1016/j.tplants.2009.12.005.PubMedCrossRefGoogle Scholar
  42. Rowan, D. D., Lane, H. P., Allen, J. M., et al. (1996). Biosynthesis of 2-methylbutyl, 2-methyl-2-butenyl and 2-methylbutanoate esters in Red Delicious and Granny Smith apples using deuterium-labeled substrates. Journal of Agriculture and Food Chemistry, 44, 3276–3285.CrossRefGoogle Scholar
  43. Schaffer, R. J., Friel, E. N., Souleyre, E. J. F., et al. (2007). A genomics approach reveals that aroma production in apple is controlled by ethylene predominantly at the final step in each biosynthetic pathway. Plant Physiology, 144, 1899–1912. doi: 10.1104/pp.106.093765.PubMedCrossRefGoogle Scholar
  44. Sedov, E. N., & Makarkina, M. A. (2008). Biochemical composition of fruit of apple cultivar clones and tetraploid forms. Russian Agricultural Science, 34, 71–73. doi: 10.3103/S1068367408020018.CrossRefGoogle Scholar
  45. Soukoulis, C., Aprea, E., Biasioli, F., et al. (2010). Proton transfer reaction time-of-flight mass spectrometry monitoring of the evolution of volatile compounds during lactic acid fermentation of milk. Rapid Communications in Mass Spectrometry, 24, 2127–3134. doi: 10.1002/rcm.4617.PubMedCrossRefGoogle Scholar
  46. Španěl, P., & Smith, D. (2011). Progress in SIFT-MS: Breath analysis and other applications. Mass Spectrometry Reviews, 30, 236–267. doi: 10.1002/mas.20303.PubMedCrossRefGoogle Scholar
  47. Sterner, J. L., Johnston, M. V., Nicol, G. R., & Ridge, D. P. (2000). Signal suppression in electrospray ionization Fourier transform mass spectrometry of multi-component samples. Journal of Mass Spectrometry and Ion Physics, 35, 385–391. doi: 10.1002/(SICI)1096-9888(200003)35:3<385:AID-JMS947>3.0.CO;2-O.Google Scholar
  48. Taylor, A. (2000). Atmospheric pressure chemical ionisation mass spectrometry for in vivo analysis of volatile flavour release. Food Chemistry, 71, 327–338. doi: 10.1016/S0308-8146(00)00182-5.CrossRefGoogle Scholar
  49. Tholl, D., Boland, W., Hansel, A., et al. (2006). Practical approaches to plant volatile analysis. Plant Journal, 45, 540–560. doi: 10.1111/j.1365-313X.2005.02612.x.PubMedCrossRefGoogle Scholar
  50. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.Google Scholar
  51. Venturi, S., Dondini, L., Donini, P., & Sansavini, S. (2005). Retrotransposon characterisation and fingerprinting of apple clones by S-SAP markers. Theoretical and Applied Genetics, 112, 440–444. doi: 10.1007/s00122-005-0143-8.PubMedCrossRefGoogle Scholar
  52. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., et al. (2008). Assessment of PLSDA cross validation. Metabolomics, 4, 81–89. doi: 10.1007/s11306-007-0099-6.CrossRefGoogle Scholar
  53. White, A. (1991). The Gala apple. Fruit Varieties Journal, 45, 2–3.Google Scholar
  54. Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory System, 58, 109–130.CrossRefGoogle Scholar
  55. Young, J. C., Chu, C. L. G., Lu, X., & Zhu, H. (2004). Ester variability in apple varieties as determined by solid-phase microextraction and gas chromatography–mass spectrometry. Journal of Agriculture and Food Chemistry, 52, 8086–8093. doi: 10.1021/jf049364r.CrossRefGoogle Scholar
  56. Zini, E., Biasioli, F., Gasperi, F., et al. (2005). QTL mapping of volatile compounds in ripe apples detected by proton transfer reaction-mass spectrometry. Euphytica, 145, 269–279. doi: 10.1007/s10681-005-1645-9.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Luca Cappellin
    • 1
    • 2
  • Christos Soukoulis
    • 1
  • Eugenio Aprea
    • 1
  • Pablo Granitto
    • 3
  • Nicola Dallabetta
    • 4
  • Fabrizio Costa
    • 1
  • Roberto Viola
    • 1
  • Tilmann D. Märk
    • 2
  • Flavia Gasperi
    • 1
  • Franco Biasioli
    • 1
    Email author
  1. 1.IASMA Research and Innovation CentreFondazione Edmund MachS. Michele a/AItaly
  2. 2.Institut für Ionenphysik und Angewandte PhysikLeopold-Franzens Universität InnsbruckInnsbruckAustria
  3. 3.CIFASIS, French Argentina International Center for Information and Systems SciencesUPCAM (France)/UNR-CONICET (Argentina)RosarioArgentina
  4. 4.IASMA Consulting and Services Centre, Fondazione Edmund MachS. Michele a/AItaly

Personalised recommendations