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Classifying waste wood from Amazonian species by near-infrared spectroscopy (NIRS) to improve charcoal production

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  • Lima, Michael Douglas Roque
  • Ramalho, Fernanda Maria Guedes
  • Trugilho, Paulo Fernando
  • Bufalino, Lina
  • Dias Júnior, Ananias Francisco
  • Protásio, Thiago de Paula
  • Hein, Paulo Ricardo Gherardi

Abstract

Solutions to differentiate wood wastes in the storage yard of a charcoal production unit in Amazonia, where identification is often mistaken due to broad morphological similarity of species, can improve the efficiency of the carbonization process. This study aimed to develop multivariate models to quickly identify the wood wastes of 12 tropical species based on the spectral signature in the near-infrared (NIR) region, to improve the control of raw material used in charcoal production. The spectral data were subjected to principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA). Spectra acquired from the transverse surface of the wood yielded clearer clusters in the PCA score plot. However, the PLS-DA model fitted with the first derivative of the spectra measured on the radial surface of the wood showed the highest rate of correct classification (97.9%) of the 12 species. Thus, the results proved that the technique is reliable and fast for differentiating wood from branches of several species native to Amazonia, especially to group similar wood species for charcoal production.

Suggested Citation

  • Lima, Michael Douglas Roque & Ramalho, Fernanda Maria Guedes & Trugilho, Paulo Fernando & Bufalino, Lina & Dias Júnior, Ananias Francisco & Protásio, Thiago de Paula & Hein, Paulo Ricardo Gherardi, 2022. "Classifying waste wood from Amazonian species by near-infrared spectroscopy (NIRS) to improve charcoal production," Renewable Energy, Elsevier, vol. 193(C), pages 584-594.
    • Handle: RePEc:eee:renene:v:193:y:2022:i:c:p:584-594
    DOI: 10.1016/j.renene.2022.05.048
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    References listed on IDEAS

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    1. Mancini, M. & Rinnan, Å., 2021. "Near infrared technique as a tool for the rapid assessment of waste wood quality for energy applications," Renewable Energy, Elsevier, vol. 177(C), pages 113-123.
    2. Lima, Michael Douglas Roque & Patrício, Evelym Poliana Santos & Barros Junior, Udson de Oliveira & Silva, Rita de Cássia Carvalho & Bufalino, Lina & Numazawa, Sueo & Hein, Paulo Ricardo Gherardi & Pro, 2021. "Colorimetry as a criterion for segregation of logging wastes from sustainable forest management in the Brazilian Amazon for bioenergy," Renewable Energy, Elsevier, vol. 163(C), pages 792-806.
    3. Numazawa, Camila T.D. & Numazawa, Sueo & Pacca, Sergio & John, Vanderley M., 2017. "Logging residues and CO2 of Brazilian Amazon timber: Two case studies of forest harvesting," Resources, Conservation & Recycling, Elsevier, vol. 122(C), pages 280-285.
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    Cited by:

    1. Lima, Michael Douglas Roque & Bufalino, Lina & Scatolino, Mário Vanoli & Hein, Paulo Ricardo Gherardi & Carneiro, Angélica de Cássia Oliveira & Trugilho, Paulo Fernando & Protásio, Thiago de Paula, 2023. "Segregating Amazonia logging wastes from sustainable forest management improves carbonization in brick kilns," Renewable Energy, Elsevier, vol. 211(C), pages 772-788.

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