Anatomical, physical, and chemical characterization of native Caatinga wood species using integrated microscopic and spectroscopic techniques

Abstract The Caatinga biome in northeastern Brazil hosts diverse tree species with anatomical and chemical adaptations to semi-arid conditions. This study characterizes the wood of seven native species – Anadenanthera colubrina var. cebil (Griseb.) Altschul, Poincianella bracteosa (Tul.) L. P. Queiroz, Cnidoscolus quercifolius Pohl, Mimosa ophthalmocentra Mart. ex Benth, Mimosa tenuiflora (Willd.) Poir., Bauhinia cheilantha (Bong.) Steud., and Aspidosperma pyrifolium Mart. – through anatomical, physical, and chemical analyses. Wood structure was assessed by light and scanning electron microscopy; basic density was determined by immersion. FTIR and Raman spectroscopy were used to identify functional groups and calculate cellulose crystallinity. Density ranged from 430 to 850 kg/m 3 ; crystallinity ranged from 34.5 % to 78.3 %. Higher density species are suitable for structural, and composite uses due to their superior mechanical properties, while lower-density woods may favor pulp production, though higher density is generally preferred for energy purposes. This integrated analysis supports species identification and highlights the corrected use and relevance of Caatinga woods.

Authors

Institutions

Publication Details

Journal
Holzforschung
Published
2025-07-25
DOI
https://doi.org/10.1515/hf-2025-0048
Primary Topic
Agricultural and Food Sciences
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Anatomical, physical, and chemical characterization of native Caatinga wood species using integrated microscopic and spectroscopic techniques

Rinaldo Luíz Caraciolo Ferreira, Uasmim Lira Zidanes, Matheus Cordazzo Dias, Helena Cristina Vieira et al.
3 links
Holzforschung
Agricultural and Food Sciences
article

Anatomical, physical, and chemical characterization of native Caatinga wood species using integrated microscopic and spectroscopic techniques

Rinaldo Luíz Caraciolo Ferreira, Uasmim Lira Zidanes, Matheus Cordazzo Dias, Helena Cristina Vieira, Giuseppe Lima, Fábio Akira Mori, Rafael Leite Braz, Arjuman Lima
article en

Abstract

Abstract The Caatinga biome in northeastern Brazil hosts diverse tree species with anatomical and chemical adaptations to semi-arid conditions. This study characterizes the wood of seven native species – Anadenanthera colubrina var. cebil (Griseb.) Altschul, Poincianella bracteosa (Tul.) L. P. Queiroz, Cnidoscolus quercifolius Pohl, Mimosa ophthalmocentra Mart. ex Benth, Mimosa tenuiflora (Willd.) Poir., Bauhinia cheilantha (Bong.) Steud., and Aspidosperma pyrifolium Mart. – through anatomical, physical, and chemical analyses. Wood structure was assessed by light and scanning electron microscopy; basic density was determined by immersion. FTIR and Raman spectroscopy were used to identify functional groups and calculate cellulose crystallinity. Density ranged from 430 to 850 kg/m 3 ; crystallinity ranged from 34.5 % to 78.3 %. Higher density species are suitable for structural, and composite uses due to their superior mechanical properties, while lower-density woods may favor pulp production, though higher density is generally preferred for energy purposes. This integrated analysis supports species identification and highlights the corrected use and relevance of Caatinga woods.

HolzforschungVol. 79(10)
Universidade Federal de Lavras (BR), Universidade Estadual do Amapá (BR), Universidade Federal Rural de Pernambuco (BR)
Openalex Percentile: Top 76%
Agricultural and Food Sciences
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.