Wood species identification from Atlantic forest by near infrared spectroscopy

José-Henrique Camargo Pace, João-Vicente de Figueiredo Latorraca, Paulo-Ricardo Gherardi Hein, Alexandre Monteiro de Carvalho, Jonnys Paz Castro, Carlos-Eduardo Silveira da Silva

Abstract


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.

Keywords: native woods; NIR spectra; principal components; partial least squares regression.


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References


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DOI: 10.5424/fs/2019283-14558

Webpage: www.inia.es/Forestsystems