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Wood and Fiber Science
Journal Prestige (SJR): 0.556  Citation Impact (citeScore): 1 Number of Followers: 2  Subscription journalISSN (Print) 0735-6161 Published by Society of Wood Science and Technology  [1 journal]
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Authors: Jeffrey Morrell
Pages: 1 - 1
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- FOREWARD
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Authors: Cady Lancaster
Pages: 2 - 3
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- USE OF A PORTABLE NEAR INFRARED SPECTROMETER FOR WOOD IDENTIFICATION OF
FOUR DALBERGIA SPECIES FROM MADAGASCAR-
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Authors: Andry Clarel Raobelina, Gilles Chaix, Andriambelo Radonirina Razafimahatratra, Sarobidy Pascal Rakotoniaina, Tahiana Ramananantoandro
Pages: 4 - 17
Abstract: This study focused on the use of Near InfraRed (NIR) Spectroscopy to address the lack of tools and skills for wood identification of Dalbergia species from Madagascar. Two sample sets of 41 wood blocks and 41 wood cores belonging to four Dalbergia species (D. abrahamii, D. chlorocarpa, D.greveana, and D. pervillei) were collected in the northern and western regions of Madagascar. Sapwood and heartwood NIR spectra were measured onwood at 12% moisture content by using a portable VIAVI MicroNIR 1700 spectrometer. Four discrimination models corresponding to sapwood and heartwood of the two sample forms were developed using Partial Least Square Discriminant Analysis (PLSDA). Good accuracy of 83.3% and 81.8% were obtained from the heartwood-based PLSDA models respectively for wood blocks and wood cores samples. All D. chlorocarpa samples were well-classified by the two models. Results highlighted the potential of portable NIR Spectroscopy as a helpful tool tosupport sustain- able management and trade of Madagascar’s Dalbergia species. Further studies are, however, needed for its operational use in identification routine.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- DISTINGUISHING NATIVE AND PLANTATION-GROWN MAHOGANY (SWIETENIA
MACROPHYLLA) TIMBER USING CHROMATOGRAPHY AND HIGH-RESOLUTION QUADRUPOLE
TIME-OF-FLIGHT MASS SPECTROMETRY-
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Authors: Joseph Doh Wook Kim, Philip D. Evans, Pamela Brunswick, Dayue Shang
Pages: 18 - 30
Abstract: Plantation-grown mahogany (Swietenia macrophylla) from Fiji has been preferred as a sus- tainable wood source for the craftingof electric guitars because its trade is not restricted by Convention on International Trade in Endangered Species of Wild Fauna andFlora (CITES), unlike S. macrophylla sourced from native forests. Ability to differentiate between the two wood types would deter sale of illegally harvested native-grown S. macrophylla to luthiers and other artisans. The chemical composition of wood is influenced bycambial age and geographical factors, and there are chemical differences between S. macrophylla grown in different regions. Thisstudy tested the ability of high-resolution mass spectrometry to chemotypically dif- ferentiate plantation-grown Fijian S. macrophylla from the same wood species obtained from native forests. Multiple heartwood specimens of both wood types were extracted and chromatographically profiled using gas and liquid chromatography tandem high-resolution quadrupole time-of-flight massspectrometry (GC/QToF, LC/QToF). Visual comparison of mass spectral ions, together with modern analytical data-mining techniques,were employed to screen the results. Principal component analysis scatter plots with 95% confidence ellipses showed unambiguousseparation of the two wood types by GC/LC/QToF. We conclude that screening of heartwood extractives using high-resolution massspectrometry offers an effective way of identifying and sepa- rating plantation-grown Fijian S. macrophylla from wood grown in native forests.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- THE GLOBAL WOOD SPECIES PRIORITY LIST: A LIVING DATABASE OF TREE SPECIES
MOST AT RISK FOR ILLEGAL LOGGING, UNSUSTAINABLE DEFORESTATION, AND HIGH
RATES OF TRADE GLOBALLY-
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Authors: S.B. Richardson, J.C. Simeone, V. Deklerck
Pages: 31 - 42
Abstract: The illegal timber trade is one of the most impactful natural wildlife crimes, affecting the live- lihood of local communities, naturalresource availability, and the associated carbon storage and biodiver- sity. Many timber species are highly sought after and are atrisk of exhaustion and subsequent extinction. Although several initiatives exist to indicate tree species risk and conservation status, there is no single resource, or prioritized list, that qualifies the most high-risk and highly traded species across the globe. Organizations end up creating their own priority species lists to meet this lack of aggregated information, requiring hours of independent research and resulting in the recreation of similar lists. To provide a one- stop-shop for similar initiatives, World Forest ID developed the Global Priority Wood Species List (GPWSL) to synthesize existing information. Currently, the GPWSLharbors 270 species most at risk for illegal logging, unsustainable deforestation, and high rates of international trade. The database contains rel- evant information on each species; such as natural distribution, conservation listings, and countries of import. Here,we present the list, the methods used in its development, and its potential applications for the wood industry as a whole
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- IDENTIFICATION AND RECOGNIZATION OF BAMBOO BASED ON CROSS-SECTIONAL IMAGES
USING COMPUTER VISION-
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Authors: Ziwei Wang, Fukuan Dai, Xianghua Yue, Tuhua Zhong, Hankun Wang, Genlin Tian
Pages: 43 - 52
Abstract: Identification of bamboo is of great importance to its conservation and uses. However, identify bamboo manually is complicated, expensive, and time-consuming. Here, we analyze the most evident and characteristic anatomical elements of cross section images, that’s a particularly vital breakthrough point. Meanwhile, we present a novel approach with respect to the automatic identification of bamboo on the basis of the cross-sectional images through computer vision.Two diverse transfer learning strategies were applied for the learning process, namely fine-tuning with fully connected layers and all layers, the results indicated that fine-tuning with all layers being trained with the dataset consisting of cross-sectional images of bamboo is an effective tool to identify and recognize intergenericbamboo, 100% accuracy on the training dataset was achieved while 98.7% accuracy was output on the testing dataset, suggesting the proposed method is quite effective and feasible, it’s beneficial to identify bamboo and protect bamboo in coutilization. More collection of bamboo species in the dataset in the near futuremight make EfficientNet more promising for identifying bamboo.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- COMPARING GC×GC-TOFMS-BASED METABOLOMIC PROFILING AND WOOD ANATOMY FOR
FORENSIC IDENTIFICATION OF FIVE MELIACEAE (MAHOGANY) SPECIES-
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Authors: Isabelle Duchesne, Dikshya Dixit Lamichhane, Ryan P. Dias, Paulina de la Mata, Martin Williams, Manuel Lamothe, James J. Harynuk, Nathalie Isabel, Alain Cloutier
Pages: 53 - 82
Abstract: Illegal logging and associated trade have increased worldwide. Such environmental crimes represent a major threat to forest ecosystems and society, causing distortions in market prices, economic instability, ecological deterioration, and poverty. To prevent illegal imports of forest products, there is a need to develop wood identification methods for identifying tree species regulated by the Convention on International Trade in Species of Wild Fauna andFlora in Trade (CITES) and other look-alike species. In this exploratory study, we applied metabolomic profiling of five species (Swietenia mahagoni, Swietenia macrophylla, Cedrela odorata, Khaya ivorensis, and Toona ciliata) using two-dimensional gas chromatog- raphy combined with time-of-flight mass spectrometry (GC3GC-TOFMS). We also performed qualitative, quantitative (based on the measurement of vessel area, tangential vessel lumina diameter,vessel element length, ray height, and ray width), and machine-vision aided (XyloTron) wood anatomy on a subsample of wood specimens to explore thepotential and limits of each approach. Fifty dried xylaria wood specimens were ground, extracted with methanol, and subsequently analyzed by GC3GC-TOFMS. In this study, the four genera could easily be identified using qualitative wood anatomy and chemical profiling. At the spe- cies level, Swietenia macrophylla and Swietenia mahagoni specimens were found to share many major metabolites and could only be differentiated after feature selection guided by cluster resolution (FS-CR) and visualization using Principal Component Analysis (PCA). Expectedly, specimens from the two Swiete- nia spp. could not be distinguished based on qualitative wood anatomy. However, significant differences in quantitative anatomical features were obtained for these two species. Excluding T. ciliata that was not included in the reference database of end grain images at the time of testing (2021), the XyloTron could successfully identify the majority of the specimens to the right genus and 50% of the specimens to the right species. The machine-vision tool was particularly successful at identifying Cedrela odorata samples, where all samples were correctly identified. Despite the limited number of specimens available for thisstudy, our preliminary results indicate that GC3GC-TOFMS-based metabolomic profiles could be used as comple- mentary method to differentiate CITES-regulated wood specimens at the genus and species levels.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- PRESERVATIVE TREATMENT OF TASMANIAN PLANTATION EUCALYPTUS NITENS USING
SUPERCRITICAL FLUIDS-
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Authors: K.C. Wood, A.W. Kjellow, M.J. Konkler, G. Presley, J.J. Morrell
Pages: 83 - 93
Abstract: Short rotation plantation forests in Tasmania, Australia, are dominated by Eucalyptus nitens (common name: shining gum). These forests were primarily planted to provide material for pulp and paper production, but the timber is increasingly sought after for higher value and more enduring applications. Plantation E. nitens has a high proportion of low durability heartwood that resists penetration by conventional fluid preservatives. This limits its use to indoor applications. One approach to overcoming the refractory nature of E. nitens is to modify the treatment fluid. We investigated the use of supercritical carbon dioxide to deliver biocides deep into the wood. Timbers varying in thickness from 19 to 35 mm and 900 mm long were treated with a multicomponent biocide under supercritical conditions in a commercial facility in Denmark. The resulting timber was cut into zones inward from the surface. Wood from these zones was ground and extracted for HPLC analysis for tebuconazole and propiconazole. Preservative was detected in the inner portion of every sample examined, indicating that the process resulted in treatment throughout the boards, with concentrations meeting and on average exceeding the targeted amounts.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- CASE STUDY OF 3-PLY COMMERCIAL SOUTHERN PINE CLT MECHANICAL PROPERTIES AND
DESIGN VALUES-
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Authors: L. M. Spinelli Correa, R. Shmulsky, F. J. N. França
Pages: 94 - 99
Abstract: This work elucidates on a case study of industrially manufactured cross-laminated timber (CLT). Two methods are used to calculate specimens section modulus: Sgross and Seffective. The first assumes that specimens behave as a continuous material, whereas the second considers the cross laminations (shear analogy method). Although the shear analogy method is indicated for construction purposes, applications, such as trench shoring, matting, and work platforms, could benefit from a simpler calculation method. There- fore, theobjective of this work was to conduct a case study of Modulus of Rupture (MOR) and Modulus of Elasticity (MOE) of southern pine CLT to compare the previously mentioned calculation methods. Both parametric and nonparametric fifth percentiles and associated Fb values are reported and were substantially higher than those of the constituent lumber. For MOE, empirical testing and calculation based on gross moment of inertia provided lower values as compared with the constituent lumber.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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- FIBER QUALITY PREDICTION USING NIR SPECTRAL DATA: TREE-BASED ENSEMBLE
LEARNING VS DEEP NEURAL NETWORKS-
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Authors: Vahid Nasir, Syed Danish Ali, Ahmad Mohammadpanah, Sameen Raut, Mohamad Nabavi, Joseph Dahlen, Laurence Schimleck
Pages: 100 - 115
Abstract: The growing applications of near infrared (NIR) spectroscopy in wood quality control and monitoring necessitates focusing on data-driven methods to develop predictive models. Despite the advancements in analyzing NIR spectral data, literature on wood science and engineering has mainly uti- lizedthe classic model development methods, such as principal component analysis (PCA) regression or partial least squares (PLS) regression, with relatively limited studies conducted on evaluating machine learning (ML) models, and specifically, artificial neural networks (ANNs). This couldpotentially limit the performance of predictive models, specifically for some wood properties, such as tracheid width that are both time-consuming tomeasure and challenging to predict using spectral data. This study aims to enhance the prediction accuracy for tracheid width using deep neural networks and tree-based ensemble learning algorithms on a dataset consisting of 2018 samples and 692 features (NIR spectra wavelengths). Accord- ingly, NIR spectra were fed into multilayer perceptron (MLP), 1 dimensional-convolutional neural net- works (1D-CNNs), random forest, TreeNet gradient-boosting, extreme gradient-boosting (XGBoost), and light gradient-boosting machine (LGBM). It was of interest to study the performance of the models with and without applying PCA to assess how effective they would perform when analyzing NIR spectra with- out employing dimensionality reduction on data. It was shown that gradient-boosting machines outper- formed the ANNs regardless of the number of features (data dimension). Allthe models performed better without PCA. It is concluded that tree-based gradient-boosting machines could be effectively used for wood characterization utilizing a medium-sized NIR spectral dataset.
PubDate: 2023-07-05
Issue No: Vol. 55, No. 1 (2023)
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