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An optimum artificial neural network and a partial least square with discriminant analysis regression were developed and tested for accuracy in distinguishing two wood species by using near-infrared (NIR) spectrum. A mixed population of kiln-dried wood boards of western hemlock (Tsuga heterophylla (Raf.) Sarg.) and amabilis fir (Abies amabilis (Dougl.) Forbes) were scanned by NIR and then a random sub-set was water saturated under vacuum conditions and scanned again. This design aimed to capture the effect of moisture content above the fibre saturation point on the separation algorithms. Our results revealed that both modelling techniques can be effective tools for species recognition achieving correct identification of over 86% for fir and 94% for hemlock on either kiln-dried or fully saturated boards. [ABSTRACT FROM PUBLISHER]

The bioincising impact ofPhysisporinus vitreusandXylaria longipeson the porous structure, and permeability of Douglas-fir containing compression wood was investigated. Fungal cultivation was carried out at 25°C and 85% relative humidity in an incubator for three time periods, namely, 15, 30 and 45 days. Safranin solution was injected into wood specimens to evaluate liquid permeability whereas air permeability was measured by the falling-water method. Wood exposure to both fungi enhanced permeability by increasing incubation period. However, the parallel compression strength of wood reduced. Microscopic imagery revealed degradation of bordered pits, bore-hole formation through tracheid walls as well as ray damage which are thought to be main reasons for improvements in air and liquid permeability. No pronounced differences were found between the fungi in their effects on permeability. Parenchyma rays were also shown to play an important role in the radial permeability of Douglas-fir compression wood. [ABSTRACT FROM AUTHOR]

Western hemlock (Tsuga heterophylla), was dried using two different schedules with optional conditioning and seven day post-drying equalisation in a covered and climate controlled space that emulates outdoor conditions in south-central Japan from October to May. Timber quality was evaluated pre- and post-drying, and/or post-equilibration. Drying times, moisture content variation between and within timbers and internal stresses were also assessed. Data analysis revealed that conditioning and aggressive drying reduced casehardening, while milder drying resulted in less twist and diamonding. Although the control run seemed to develop less shape distortions than the treatments, it required longer drying times. With aggressive drying the kiln turnover will be quicker and the dried timber might be stored in a covered area for post-drying equalisation that will level out moisture gradients and alleviate casehardening. As a subsequent step, the timber might be planed to reduce twist, diamonding and superficial checks. [ABSTRACT FROM AUTHOR]

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Extractives have an impact on the processing and commercial value of wood. Ultrasound is an environmentally friendly technology commonly employed to reduce the extractive content and thus enhance the permeability of wood. This study aimed to understand the migration mechanism of extractives inside wood during ultrasonic treatment, which may help to obtain the desired wood properties. The extractive distribution of Ailanthus altissima was observed by using stereo microscopy, optical microscopy, and scanning electron microscopy, the extractive content was determined, and the relationship between the concentration of water-soluble extractives and absorbance was measured using a UV/Vis spectrophotometer, and the migration model of extractives was studied using layered extraction by innovatively combining the weight and the absorbance methods. The results revealed that the extractives were predominantly distributed in the vessels and diminished after ultrasonic treatment. The extractive content gradually decreased over time (0 ∼ 5 h), with a rapid decline observed within the first 2 h. The concentration of the water-soluble extractives exhibited a proportional relationship with the absorbance. Through the comparison of the layered-extractive concentration, accumulating evidence suggested that the migration of the extractives was a dynamic process, which included the extractives migrating towards easy-extracted area, moving along the direction of ultrasound propagation inside the wood, and leaching out of wood during ultrasonic treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

The quality control of thermally modified wood and identifying heat treatment intensity using nondestructive testing methods are critical tasks. This study used near-infrared (NIR) spectroscopy and machine learning modeling to classify thermally modified wood. NIR spectra were collected from the surfaces of untreated and thermally treated (at 170 °C, 212 °C, and 230 °C) western hemlock samples. An explainable machine learning approach was practiced using a TreeNet gradient boosting machine. No dimensionality reduction was performed to better explain the feature ranking results obtained from the model and provide insight into the critical wavelengths contributing to the performance of classification models. NIR spectra in the ranges of 1100-2500 nm, 1400-2500 nm, and 1700-2500 nm were fed into the TreeNet model, which resulted in classification accuracy values (test data) of 94.35%, 89.29%, and 84.52%, respectively. Feature ranking analysis revealed that when using the range of 1100-2500 nm, the changes in wood color resulted in the highest variation in NIR reflectance amongst treatments. As a result, associated features were given higher importance by TreeNet. Limiting the wavelength range increased the significance of features related to water or wood chemistry; however, these predictive models were not as accurate as the one benefiting from the impact of wood color change on the NIR spectra. The developed framework could be applied to different applications in which NIR spectra are used for wood characterization and quality control to provide improved insights into selected NIR wavelengths when developing a machine learning model.