AMAZONIAN WOOD SPECIES. Supervising wood exploitation can be very challenging due to the existence of many similar species and the reduced number of wood identification experts to meet the demand. There is evidence that valuable endangered wood species are being smuggled disguised as other species. Near infrared spectroscopy (NIRS) and chemometrics has been successfully used to discriminate between Amazonian wood species using high resolution instruments. In this study, a handheld spectrometer was evaluated for the discrimination of six visually similar tropical wood species using PLS-DA. Woods of mahogany (Swietenia macrophylla) and cedar (Cedrela odorata), both high value tropical timber species included in Appendixes II and III of the CITES, respectively; crabwood (Carapa guianensis); cedrinho (Erisma uncinatum); curupixá (Micropholis melinoniana); and jatobá (Hymenea coubaril). The data for model development and validation take into account both laboratory and field measurements. Outlier exclusion was performed based on Hotelling T 2 , residuals Q and errors in the estimated class values. The efficiency rates were higher than 90% for all species, showing that the handheld NIR combined with PLS-DA succeeded in discriminate between these species. These results stimulate the application of handheld NIR spectrometers in the supervision of wood exploitation, which can contribute to the species preservation.Keywords: mahogany; cedar; crabwood; NIR; PLS-DA; amazon woods. INTRODUÇÃOA exploração e comercialização de madeiras ilegais contribuem para o crescimento contínuo das taxas de desmatamento das florestas Amazônica e demais do globo terrestre. Atualmente, há um esforço de várias instituições internacionais para combater a exportação de madeira ilegal, que envolve cifras da ordem de bilhões de dólares anuais, respeitando a legislação existente em cada país. Tal esforço tem como finalidades controlar, proibir ou desmotivar a exploração seletiva de espécies florestais produtoras de madeira ou de uma área específica explorada. 1 Apesar de todo o empenho, existe carência em resolver uma questão básica e primordial, a de identificar rapidamente e de maneira confiável, a qual espécie florestal pertence a madeira que está sendo inspecionada. Para a identificação da madeira, geralmente desprovida de qualquer material botânico, da forma como ela é transportada e comercializada, fiscais e agentes ambientais treinados recorrem ao método convencional de anatomia de madeira, que compara os caracteres anatômicos e morfológicos da madeira examinada com a madeira de padrões depositados em xilotecas registradas.2 As chaves de identificação, eletrônicas ou não, reúnem informações anatômica e física da madeira e facilitam a análise anatômica.3 Contudo, ainda é necessária elevada experiência do analista para a aplicação do método com o nível de confiança necessário para realizar uma apreensão de carga ilegal. Adicionalmente, apesar dos ótimos resultados apresentados pelo método anatômico, em muitas regiões e postos de fiscalização n...
A rapid and reliable identification of the country of origin of protected timbers is one of the measures for combating illegal logging. Mahogany (Swietenia macrophyllaKing) trees are distributed from Mexico to Bolivia and the Brazilian Amazon and are included in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Near-infrared spectroscopy (NIRS) has been proven to be a promising technique for calibration based and rapid species identification. There are only a few studies concerning the origin determination of mahogany wood. The present study is dedicated to trace back mahogany wood from Bolivia, Brazil, Guatemala, Mexico and Peru by means of two different handheld NIR spectrometers. The spectra were obtained directly from the wood samples, and soft independent modeling of class analogy (SIMCA) and partial least squares for discriminant analysis (PLS-DA) models were developed for data evaluation. The SIMCA model was efficient and 67–100% and 70–98% of the origins were identified based on the spectral ranges from 1595 to 2396 nm and 950 to 1650 nm, respectively. The best results were obtained by the PLS-DA approach, in which the efficiency rates (EFR) vary from 90 to 100% with both spectrometers. In summary, both instruments were highly effective and are suitable for preliminary identification of the country of origin for mahogany wood.
The analysis of multivariate chemical data is commonplace in fields ranging from metabolomics to forensic classification. Many of these studies rely on exploratory visualization methods that represent the multidimensional data in spaces of lower dimensionality, such as hierarchical cluster analysis (HCA) or principal components analysis (PCA). However, such methods rely on assumptions of independent measurement errors with uniform variance and can fail to reveal important information when these assumptions are violated, as they often are for chemical data.This work demonstrates how two alternative methods, maximum likelihood principal components analysis (MLPCA) and projection pursuit analysis (PPA), can reveal chemical information hidden from more traditional techniques. Experimental data to compare different methods consists of near-infrared (NIR) reflectance spectra from 108 samples of wood that are derived from four different species of Brazilian trees. The measurement error characteristics of the spectra are examined and it is shown that, by incorporating measurement error information into the data analysis (through MLPCA) or using alternative projection criteria (i.e. PPA), samples can be separated by species. These techniques are proposed as powerful tools for multivariate data analysis in chemistry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.