Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/41320
Título: Wood species identification from Atlantic forest by near infrared spectroscopy
Palavras-chave: Native woods
NIR
Principal components
Partial least squares regression
Madeiras nativas - Identificação
Espectroscopia no infravermelho próximo
Madeira - Mata Atlântica
Regressão parcial de mínimos quadrados
Data do documento: 2019
Editor: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
Citação: PACE, J. H. C. et al. Wood species identification from Atlantic forest by near infrared spectroscopy. Forest Systems, Madrid, v. 28, n. 3, 2019. Paginação irregular.
Resumo: Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species. Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil. Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures. Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples. Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.
URI: https://revistas.inia.es/index.php/fs/article/view/14558
http://repositorio.ufla.br/jspui/handle/1/41320
Aparece nas coleções:DCF - Artigos publicados em periódicos

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