ArticleRSC advances2024
Making wood inspection easier: FTIR spectroscopy and machine learning for Brazilian native commercial wood species identification.
Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Research on Intelligent Wood Species Identification Method Based on Multimodal Texture-Dominated Features and Deep Learning Fusion.Plants (Basel, Switzerland) · 2025Article
- Raman Spectroscopy for Testing Wood Pellets.Methods and protocols · 2025Article
- Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants.Scientific reports · 2025Article
- Easy and Fast Discrimination of Female Sand Flies fromAnalytical chemistry · 2025Article
- Effects of Chlortetracycline on Lignin Biosynthesis inInternational journal of molecular sciences · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The molecular structure of wood is mainly based on cellulose, lignin, and hemicellulose. However, low concentrations of lipids, phenolic compounds, terpenoids, fatty acids, resin acids, and waxes can also be found. In general, their color, smell, texture, quantity, and distribution of pores are used in human sensory analysis to identify native wood species, which may lead to erroneous classification, impairing quality control and inspection of commercialized wood. This study developed a fast and accurate method to discriminate Brazilian native commercial wood species using Fourier Transform Infrared Spectroscopy (FTIR) and machine learning algorithms. It not only solves the limitations of traditional methods but also goes beyond as it allows fast analyses to be obtained at low cost and high accuracy. In this work, we provide the identification of five Brazilian native wood species: Angelim-pedra (
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