Mahogany is one of the most valuable woods and was widely used until it was included in Appendix II of the Convention on International Trade in Endangered Species as endangered species. Mahogany wood sometimes is traded under different names. Also, some similar woods belonging to the Meliaceae family are traded as “mahogany” or as being of a “mahogany pattern”. To investigate the feasibility of the use of near infrared spectroscopy for wood discrimination, the mahogany (Swietenia macrophylla King.), andiroba or crabwood (Carapa guianensis Aubl.), cedar (Cedrela odorata L.), and curupixá (Micropholis melinoniana Pierre) woods were examined. Four discrimination models based on partial least squares-discriminant analysis were developed based on a calibration set composed of 88 samples and a test set with 44 samples. Each model corresponds to the discrimination of a wood species from the others. Optimization of the model was performed by means of the OPUS® software followed by statistical analysis software (Matlab®). The observed root mean square errors of predictions were 0.14, 0.09, 0.12, and 0.06 for discriminations of mahogany, cedar, andiroba, and curupixá, respectively. The separations of the species obtained based on the difference in the predicted values was at least 0.38. This makes it possible to perform safe discriminations with a very low probability of misclassifying a sample. This method can be considered accurate and fast.

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Near infrared spectroscopy (NIRS) has been shown effective as a tool for identifying Swietenia when tested as laboratory-processed powder, but testing such powdered wood is not readily adaptable to the fieldidentification of wood. This study explored the efficacy of a fiber optic NIRS scan of solid wood surfaces to separate Swietenia macrophylla King, Carapa guianensis Aubl., Cedrela odorata L., and Micropholis melinoniana Pierre. Transverse, radial, and tangential surfaces were scanned to determine if the surface from which data were collected influenced the spectra recorded. Surfaces were scanned before and after removing the oxidized surface layer of the blocks to test effects of exposure on the spectra. Partial least squares for discriminant analysis models were developed for each taxon separately, based on a calibration set composed of at least 67 samples and a test set with at least 45 samples. The anatomical surface scanned, but not the presence of an oxidized layer, influenced the spectra for each species, necessitating the comparison of the same planes of section. The discriminant models showed small errors for each species, indicating that reliable identifications can be made with NIRS of solid wood surfaces in these species.

Big-leaf mahogany is the world’s most valuable widely traded tropical timber species and Near Infrared Spectroscopy (NIRS) has been applied as a tool for discriminating its wood from similar species using multivariate analysis. In this study four look-alike timbers of Swietenia macrophylla (mahogany or big-leaf mahogany), Carapa guianensis (crabwood), Cedrela odorata (cedar or cedro) and Micropholis melinoniana (curupixá) have been successfully discriminated using NIRS and Partial Least Squares for Discriminant Analysis using solid block and milled samples. Species identification models identified 155 samples of S. macrophylla from 27 countries with a correct classification rate higher than 96.8%. For these specimens, the NIRS spectrum variation was more powerful for species identification than for determining provenance of S. macrophylla at the country level.

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 specieslevel 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.