Making wood inspection easier: FTIR spectroscopy and machine learning for Brazilian native commercial wood species identification-Reference-Cited by-同舟云学术

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Making wood inspection easier: FTIR spectroscopy and machine learning for Brazilian native commercial wood species identification

Published:2024 Issue:11 Volume:14 Page:7283-7289
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Jesus Everton¹,Franca Thiago¹,Calvani Camila¹,Lacerda Miller¹,Gonçalves Daniel²³^ORCID,Oliveira Samuel L.¹,Marangoni Bruno¹^ORCID,Cena Cicero¹^ORCID

Affiliation:

1. UFMS – Universidade Federal de Mato Grosso do Sul, Optics and Photonic Lab (SISFOTON-UFMS), Campo Grande, MS, Brazil

2. UFGD – Universidade Federal da Grande Dourados, Dourados, MS, Brazil

3. UEMS – Universidade Estadual de Mato Grosso do Sul, Dourados, MS, Brazil

Abstract

Sawdust molecular spectra are used as input data for the machine-learning algorithm to classify/identify different wood species.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D4RA00174E

Reference33 articles.

1. Qualitative and quantitative analysis of wood samples by Fourier transform infrared spectroscopy and multivariate analysis

2. Deep learning for biological image classification

3. Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species

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1. Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics;Forensic Science International: Reports;2024-12

2. New insights into the influencing mechanisms of hemicellulose removal on dynamic wood-water interactions characterized with low-field NMR;Industrial Crops and Products;2024-11

3. Constructing Thermal Convection Film for Low Heat Loss and High Salt Resistance in Wood‐Based Solar Evaporators;Small;2024-06-14

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