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Wood Science and Technology

, Volume 51, Issue 2, pp 273–295 | Cite as

Assessing VOC emission by different wood cores using the PTR-ToF-MS technology

  • C. Taiti
  • C. Costa
  • W. Guidi NissimEmail author
  • S. Bibbiani
  • E. Azzarello
  • E. Masi
  • C. Pandolfi
  • F. Pallottino
  • P. Menesatti
  • S. Mancuso
Original

Abstract

To date, the chemical composition and the amount and diversity of volatile organic compounds (VOCs) released by different woody plants samples have been measured and characterized through one of the most common techniques, the gas chromatography–mass spectrometry. However, this technique is very time-consuming and requires sample preparation. By contrast, the Proton-Transfer-Reaction Time-of-Flight Mass spectrometry (PTR-ToF-MS) represents an innovative tool able to provide the whole mass spectra of VOCs with short response time, high mass resolution and without sample preparation. This technique is fast, non-invasive, highly sensitive with a rapid detection system and a very low mass fragmentation of the volatile molecules. The goal of this study was to characterize the VOCs profile of different wood sample cores using a PTR-ToF-MS tool and thereafter to assess whether VOC emissions were specific for some groups of trees. VOCs released from core wood samples belonging to 14 different species were analyzed and subsequently, using an advanced multivariate class-modeling approach, the groups (softwood and hardwood) and the different tree species were discriminated. PTR-ToF-MS was able to detect VOCs from wood and to discriminate between hardwood and softwood and among different species. The great potential and the rapidity of this analysis method allow the PTR-ToF-MS to become a commercial standard tool for monitoring VOCs emitted by wood.

Keywords

VOCs Monoterpene Partial Little Square Discriminant Analysis Juglans Regia Partial Little Square Discriminant Analysis Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Some activities in this study were funded by the project “ALForLab” (PON03PE_00024_1) co-funded by the National Operational Programme for Research and Competitiveness (PON R&C) 2007–2013, through the European Regional Development Fund (ERDF) and national resource [Revolving Fund—Cohesion Action Plan (CAP) MIUR]. The authors wish to thank Dr. Maurizio Capuana from Institute of Biosciences and Bioresources (CNR) for his technical support.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • C. Taiti
    • 1
  • C. Costa
    • 2
  • W. Guidi Nissim
    • 1
    Email author
  • S. Bibbiani
    • 3
  • E. Azzarello
    • 1
  • E. Masi
    • 1
  • C. Pandolfi
    • 1
  • F. Pallottino
    • 2
  • P. Menesatti
    • 2
  • S. Mancuso
    • 1
  1. 1.Dipartimento di Scienze delle Produzioni Agroalimentari e dell’AmbienteUniversità degli Studi di FirenzeSesto FiorentinoItaly
  2. 2.Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) – Unità di ricerca per l’ingegneria agrariaMonterotondo ScaloItaly
  3. 3.Dipartimento di BiologiaUniversità degli Studi di FirenzeFlorenceItaly

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