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Wood Science and Technology

, Volume 52, Issue 5, pp 1411–1427 | Cite as

Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES

  • Filipe A. Snel
  • Jez W. B. Braga
  • Diego da Silva
  • Alex C. Wiedenhoeft
  • Adriana Costa
  • Richard Soares
  • Vera T. R. Coradin
  • Tereza C. M. Pastore
Original

Abstract

Near-infrared spectroscopy (NIRS) is a potential, field-portable wood identification tool. NIRS has been studied as tool to identify some woods but has not been tested for Dalbergia. This study explored the efficacy of hand-held NIRS technology to discriminate, using multivariate analysis, the spectra of some high-value Dalbergia wood species: D. decipularis, D. sissoo, D. stevensonii, D. latifolia, D. retusa, all of which are listed in CITES Appendix II, and D. nigra, which is listed in CITES Appendix I. Identification models developed using partial least squares discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA) were compared regarding their ability to answer two sets of identification questions. The first is the identification of each Dalbergia species among the group of the six above, and the second is the separation of D. nigra from a single group comprising the other species, grouping all Dalbergia as one class. For this latter study, spectra of D. cearensis and D. tucurensis were added to the broader Dalbergia class. These spectra were not included in the first set because the number of specimens was not enough to create an exclusive class for them. PLS-DA presented efficiency rates of over 90% in both situations, while SIMCA presented 52% efficiency at species-level separation and 85% efficiency separating D. nigra from other Dalbergia. It was shown that PLS-DA approaches are far better suited than SIMCA for generating a field-deployable NIRS model for discriminating these Dalbergia.

Notes

Acknowledgements

The authors thank ITTO-CITES Program, Forest Products Laboratory/USDA, Forest Products Laboratory of Brazilian Forest Service, University of Brasília, INCTBio Program, CAPES, CNPq and FAPDF.

Supplementary material

226_2018_1027_MOESM1_ESM.docx (28 kb)
Supplementary material 1 (DOCX 27 kb)

References

  1. Acquah GE, Via BK, Billor N, Fasina OO, Eckhardt LG (2016) Identifying plant part composition of forest logging residue using infrared spectral data and linear discriminant analysis. Sensors 16:1375–1390.  https://doi.org/10.3390/s16091375 CrossRefGoogle Scholar
  2. Adedipe OE, Dawson-Andoh B, Slahor J, Osborn L (2008) Classification of red oak (Quercus rubra) and white oak (Quercus alba) wood using a near infrared spectrometer and soft independent modelling of class analogies. J Near Infrared Spectrosc 16(1):49–57.  https://doi.org/10.1255/jnirs.760 CrossRefGoogle Scholar
  3. Ballabio D, Todeschini R (2009) Multivariate classification for qualitative analysis. In: Da-Wen S (ed) Infrared spectroscopy for food quality analysis and control, 1st edn. Academic Press, London, pp 83–104CrossRefGoogle Scholar
  4. Bergo MCJ, Pastore TCM, Coradin VTR, Wiedenhoeft AC, Braga JWB (2016) NIRS identification of Swietenia macrophylla is robust across specimens from 27 countries. IAWA J. 37(3):420–430.  https://doi.org/10.1163/22941932-20160144 CrossRefGoogle Scholar
  5. Botelho BG, Reis N, Oliveira LS, Sena MM (2015) Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA. Food Chem 181:31–37.  https://doi.org/10.1016/j.foodchem.2015.02.077 CrossRefPubMedGoogle Scholar
  6. Braga JWB, Pastore TCM, Coradin VTR, Camargos JAA, Silva AR (2011) The use of near infrared spectroscopy to identify solid wood specimens of Swietenia macrophylla. IAWA J 32:285–296.  https://doi.org/10.1163/22941932-90000058 CrossRefGoogle Scholar
  7. Branden KV, Hubert M (2005) Robust classification in high dimensions based on the SIMCA method. Chemometr Intell Lab Syst 79:10–21.  https://doi.org/10.1016/j.chemolab.2005.03.002 CrossRefGoogle Scholar
  8. Burns D, Ciurczak EW (2007) Handbook of Near-Infrared Analysis. CRC Press, Boca RatonGoogle Scholar
  9. Camargos JAA, Coradin VTR, Czarneski MC, de Oliveira D, Meguerditchian I (2001) Trees of Brazil catalog. IBAMA, BrasiliaGoogle Scholar
  10. Carballo-Meilan A, Goodman AM, Baron MG, Gonzalez-Rodriguez J (2014) A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales. Cellulose 21:261–273.  https://doi.org/10.1007/s10570-013-0093-2 CrossRefGoogle Scholar
  11. Carvalho AM (1997) A synopsis of the genus Dalbergia (Fabaceae: Dalbergieae) in Brazil. Brittonia 49:97–109.  https://doi.org/10.2307/2807701 CrossRefGoogle Scholar
  12. CITES (2017) CITES: Appendices I, II and III to the Convention on International Trade in Endangered Species of Wild Fauna and Flora. CITES. https://www.cites.org/eng/app/appendices.php. Accessed 10 March 2017
  13. De Maesschalck R, Candolfi A, Massart DL, Heuerding S (1999) Decision criteria for soft independent modelling of class analogy applied to near infrared data. Chemometr Intell Lab Syst 47:65–77.  https://doi.org/10.1016/S0169-7439(98)00159-2 CrossRefGoogle Scholar
  14. Espinoza EO, Wiemann MC, Barajas-Morales J, Chavarria GD, McClure PJ (2015) Forensic analysis of CITES-protected Dalbergia timber from the Americas. IAWA J. 36(3):311–325.  https://doi.org/10.1163/22941932-20150102 CrossRefGoogle Scholar
  15. Flåten GR, Grung B, Kvalheim OM (2004) A method for validation of reference sets in SIMCA modelling. Chemom Intell Lab Syst 72(1):101–109.  https://doi.org/10.1016/j.chemolab.2004.03.003 CrossRefGoogle Scholar
  16. Gasson P, Miller R, Stekel DJ, Whinder F, Ziemińska K (2010) Wood identification of Dalbergia nigra (CITES Appendix I) using quantitative wood anatomy, principal components analysis and naïve Bayes classification. Ann Bot 105(1):45–56.  https://doi.org/10.1093/aob/mcp270 CrossRefPubMedGoogle Scholar
  17. Gasson P, Baas P, Wheeler E (2011) Wood anatomy of cited-listed tree species. IAWA J 32:155–198.  https://doi.org/10.1163/22941932-90000050 CrossRefGoogle Scholar
  18. Guzman JAS, Richter HG, Rodriguez AR, Fuentes TFJ (2008) Wood fluorescence of commercial timbers marketed in Mexico. IAWA J 29:311–322.  https://doi.org/10.1163/22941932-90000189 CrossRefGoogle Scholar
  19. Kite GC, Green PWC, Veitch NC, Groves MC, Gasson PE, Simmonds MSJ (2010) Dalnigrin, a neoflavonoid marker for the identification of Brazilian rosewood (Dalbergia nigra) in CITES enforcement. Phytochemistry 71(10):1122–1131.  https://doi.org/10.1016/j.phytochem.2010.04.011 CrossRefPubMedGoogle Scholar
  20. Lancaster C, Espinoza E (2012) Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement. Rapid Commun Mass Spectrom 26(9):1147–1156.  https://doi.org/10.1002/rcm.6215 CrossRefPubMedGoogle Scholar
  21. Lazarescu C, Hart F, Pirouz Z, Panagiotidis K, Mansfield SD, Barret JD, Avramidis S (2016) Wood species identification by near-infrared spectroscopy. International Wood Products Journal 8:32–35.  https://doi.org/10.1080/20426445.2016.1242270 CrossRefGoogle Scholar
  22. Lorenzi H (1992) Brazilian trees: manual of identification and cultivation of arboreal plants from Brazil. Plantarum, Nova OdessaGoogle Scholar
  23. Martins AR, Talhavini M, Vieira ML, Zacca JJ, Braga JWB (2017) Discrimination of whisky brands and counterfeit identification by UV–Vis spectroscopy and multivariate data analysis. Food Chem 229:142–151.  https://doi.org/10.1016/j.foodchem.2017.02.024 CrossRefPubMedGoogle Scholar
  24. Miller RB, Wiemann MC (2006) Separation of Dalbergia nigra from Dalbergia spruceana. U.S. Department of Agriculture. https://www.fpl.fs.fed.us/documnts/fplrp/fpl_rp632.pdf. Accessed 10 Feb 2017
  25. Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858.  https://doi.org/10.1038/35002501 CrossRefPubMedGoogle Scholar
  26. Pasquini C (2003) Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications. J Braz Chem Soc 14(2):198–219.  https://doi.org/10.1590/s0103-50532003000200006 CrossRefGoogle Scholar
  27. Pastore TCM, Braga JWB, Coradin VTR, Magalhaes WLE, Okino EYA, Camargos JAA, Muniz GIB, Bressan OA, Davrieux F (2011) Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: discrimination of true mahogany, cedar, andiroba, and curupixa. Holzforschung 65:73–80.  https://doi.org/10.1515/HF.2011.010 CrossRefGoogle Scholar
  28. Schwanninger M, Rodrigues JC, Fackler K (2011) A review of band assignments in near infrared spectra of wood and wood components. J Near Infrared Spectrosc 19(5):287–308.  https://doi.org/10.1255/jnirs.955 CrossRefGoogle Scholar
  29. Soares LF, Silva DC, Bergo MCJ, Coradin VTR, Braga JW, Pastore TCM (2017) Evaluation of a NIR handheld device and PLS-DA for discrimination of six similar Amazonian wood species. Quim Nova 40:418–426.  https://doi.org/10.21577/0100-4042.20170014 CrossRefGoogle Scholar
  30. Stehmann JR, Forzza RC, Salino A, Sobral M, Costa DP, Kamino LHY (2009) Plants of Atlantic Forest. Jardim Botânico do Rio de Janeiro, Rio de JaneiroGoogle Scholar
  31. The Plant List (2013) The plant List 2013 Version 1.1. The plant List. http://www.theplantlist.org. Accessed 7 Apr 2017
  32. UNODC—United Nations Office on Drugs and Crime (2016) Best practice guide for forensic timber identification. UNODC. https://www.unodc.org/documents/Wildlife/Guide_Timber.pdf. Accessed 18 Mar 2017
  33. Wiemann MC, Ruffinatto F (2012) Separation of Dalbergia stevensonii from Dalbergia tucurensis. USDA Forest Service. https://www.fpl.fs.fed.us/documnts/fplrp/fpl_rp665.pdf. Accessed 09 May 2017
  34. Wise BM, Shaver JM, Gallagher NB, Windig W, Bro R, Koch RS (2006) PLS Toolbox Version 4.0 for use with MatlabTM. Eigenvector Research Inc. http://mitr.p.lodz.pl/raman/jsurmacki/pliki/zajecia/LMDiT/cw3/LMDiT_PLS_Manual_4.pdf. Accessed 13 March 2017

Copyright information

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

Authors and Affiliations

  • Filipe A. Snel
    • 1
    • 2
  • Jez W. B. Braga
    • 1
  • Diego da Silva
    • 1
    • 2
  • Alex C. Wiedenhoeft
    • 3
    • 4
    • 5
    • 6
  • Adriana Costa
    • 3
    • 4
  • Richard Soares
    • 3
  • Vera T. R. Coradin
    • 2
  • Tereza C. M. Pastore
    • 2
  1. 1.Chemistry InstituteUniversity of BrasíliaBrasíliaBrazil
  2. 2.Forest Products LaboratoryBrazilian Forest ServiceBrasíliaBrazil
  3. 3.Forest Products LaboratoryUSDA Forest ServiceMadisonUSA
  4. 4.Department of BotanyUniversity of WisconsinMadisonUSA
  5. 5.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA
  6. 6.Ciências Biológicas (Botânica)Universidade Estadual Paulista – BotucatuSão PauloBrazil

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