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Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data

Gherardi Hein Paulo Ricardo. 2010. Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data. Revista Cerne, 16 : pp. 90-96.

Journal article ; Article de revue à facteur d'impact Revue en libre accès total
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Titre portugais : Métodos de regressão multivariada para estimativa da densidade basica da madeira de Eucalyptus por espectroscopia no infravemelho proximo

Quartile : Q4, Sujet : FORESTRY

Abstract : Near infrared (NIR) spectroscopy is a fast and efficient technique to predict a range of wood traits; however, methods for extracting useful information from the NIR spectra could be improved. Thus, the aim of this study was to evaluate the statistic performance of two regression methods for estimating the basic density in Eucalyptus urophylla x grandis wood from near infrared spectroscopic data. The predictive models calibrated by principal component regression (PCR) or partial least square regression (PLSR) method provided fine correlations. The coefficients of determination (R2cv) of the PCR models ranged from 0.78 to 0.85 with standard error of cross-validation (SECV) and the ratio of performance to deviation (RPD) varying from 32.8 to 41.2 kg/m3 and from 1.6 to 1.9, respectively. The PLSR models presented R2cv with relatively lower magnitude (from 0.65 to 0.78); but also lower SECV (from 29.8 to 38.9 kg/m3) and higher RPD values (from 1.6 to 2.1). In short, PCR method provides higher R2 between Lab-measured and NIR-predicted values while PLSR produces lower standard errors of cross-validations. For both regression methods, the pre-treatments on NIR spectra, and the wavelength selection improved the calibration statistics, reducing the SECV and increasing the R2cv and the RPD values. Thus, PCR and PLS regression can be applied successfully for predicting basic density in Eucalyptus urophylla x grandis wood from the near infrared spectroscopic data. (Résumé d'auteur)

Mots-clés Agrovoc : Eucalyptus, Bois, Densité, Méthode statistique, Normalisation, Spectroscopie infrarouge, Mesure, Calibrage

Mots-clés géographiques Agrovoc : Minas Gerais

Mots-clés complémentaires : Densité du bois

Classification Agris : K50 - Processing of forest products
U30 - Research methods

Champ stratégique Cirad : Hors axes (2005-2013)

Auteurs et affiliations

  • Gherardi Hein Paulo Ricardo, CIRAD-PERSYST-UPR Bois tropicaux (FRA)

Autres liens de la publication

Source : Cirad - Agritrop (https://agritrop.cirad.fr/556351/)

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