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    Author(s): 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
    Date: 2018
    Source: Wood Science and Technology. 52(5): 1411-1427.
    Publication Series: Scientific Journal (JRNL)
    Station: Forest Products Laboratory
    PDF: Download Publication  (1.0 MB)


    Near-infrared spectroscopy (NIRS) is a potential, feld-portable wood identifcation tool. NIRS has been studied as tool to identify some woods but has not been tested for Dalbergia. This study explored the efcacy 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. Identifcation 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 identifcation questions. The frst is the identifcation 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 frst set because the number of specimens was not enough to create an exclusive class for them. PLS-DA presented efciency rates of over 90% in both situations, while SIMCA presented 52% efciency at specieslevel separation and 85% efciency separating D. nigra from other Dalbergia. It was shown that PLS-DA approaches are far better suited than SIMCA for generating a feld-deployable NIRS model for discriminating these Dalbergia.

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    Snel, Filipe A.; Braga, Jez W. B.; da Silva, Diego; Wiedenhoeft, Alex C.; Costa, Adriana; Soares, Richard; Coradin, Vera T. R.; Pastore, Tereza C. M. 2018. Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Science and Technology. 52(5): 1411-1427.


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    CITES, Dalbergia, NIR spectroscopy, wood identification

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