High-resolution imaging of the physical and chemical properties of Populus wood using SilviScan™ and near-infrared spectroscopy. Renström, A., Scheepers, G., Yassin, Z., Grahn, T., Sivan, P., Niittylä, T., Mellerowicz, E. J., & Tuominen, H. IAWA Journal, -1(aop):1–16, February, 2025. Publisher: Brill![High-resolution imaging of the physical and chemical properties of Populus wood using SilviScan™ and near-infrared spectroscopy [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Summary Spatial information on wood structure and chemistry is crucial for understanding wood functionality. We present a high-throughput and high-resolution near-infrared (NIR) method for combined imaging of the physical and chemical properties of stem sections from Populus trees. Pyrolysis-GC/MS data was used for sensitive and spatially resolved calibration of wood chemistry while SilviScan™ analyses provided reference data for wood physical properties with 25 μm resolution for wood density and 0.2–2.0 mm for microfibril angle (MFA). NIR prediction models were trained and calibrated on material from both field- and greenhouse-grown trees. Thus, the method was developed for NIR imaging of stem samples as small as 4 mm in diameter with an image resolution of 0.03 mm for small-diameter samples and 0.5 mm for samples with multiple annual rings. The NIR model performance, tested against data not used in the training set, reached the coefficient of determination ( R pred 2 ) values for wood density and MFA of 0.60 and 0.72, respectively. The NIR models for wood chemistry showed R pred 2 values of 0.78 and 0.77 for carbohydrates and lignin, respectively. Models for the G-, S- and H-type lignin had R pred 2 values between 0.58 and 0.86. In addition, we developed a prediction model for the determination of tension wood distribution. According to this model, tension wood was frequently observed in young greenhouse samples, which might explain the higher variation found in the chemical and physical properties of wood in greenhouse-grown compared to field-grown trees. The study also demonstrated that NIR-model estimations in image format can capture spatial variations that are not detectable in bulk analyses of wood properties. Examples of the method applied to greenhouse-grown trees highlight the efforts to develop NIR models with good prediction accuracies based on high-resolution data.
@article{renstrom_high-resolution_2025,
title = {High-resolution imaging of the physical and chemical properties of {Populus} wood using {SilviScan}™ and near-infrared spectroscopy},
volume = {-1},
issn = {0928-1541, 2294-1932},
url = {https://brill.com/view/journals/iawa/aop/article-10.1163-22941932-bja10179/article-10.1163-22941932-bja10179.xml},
doi = {10.1163/22941932-bja10179},
abstract = {Summary Spatial information on wood structure and chemistry is crucial for understanding wood functionality. We present a high-throughput and high-resolution near-infrared (NIR) method for combined imaging of the physical and chemical properties of stem sections from Populus trees. Pyrolysis-GC/MS data was used for sensitive and spatially resolved calibration of wood chemistry while SilviScan™ analyses provided reference data for wood physical properties with 25 μm resolution for wood density and 0.2–2.0 mm for microfibril angle (MFA). NIR prediction models were trained and calibrated on material from both field- and greenhouse-grown trees. Thus, the method was developed for NIR imaging of stem samples as small as 4 mm in diameter with an image resolution of 0.03 mm for small-diameter samples and 0.5 mm for samples with multiple annual rings. The NIR model performance, tested against data not used in the training set, reached the coefficient of determination ( R pred 2 ) values for wood density and MFA of 0.60 and 0.72, respectively. The NIR models for wood chemistry showed R pred 2 values of 0.78 and 0.77 for carbohydrates and lignin, respectively. Models for the G-, S- and H-type lignin had R pred 2 values between 0.58 and 0.86. In addition, we developed a prediction model for the determination of tension wood distribution. According to this model, tension wood was frequently observed in young greenhouse samples, which might explain the higher variation found in the chemical and physical properties of wood in greenhouse-grown compared to field-grown trees. The study also demonstrated that NIR-model estimations in image format can capture spatial variations that are not detectable in bulk analyses of wood properties. Examples of the method applied to greenhouse-grown trees highlight the efforts to develop NIR models with good prediction accuracies based on high-resolution data.},
language = {eng},
number = {aop},
urldate = {2025-08-18},
journal = {IAWA Journal},
author = {Renström, Anna and Scheepers, Gerhard and Yassin, Zakiya and Grahn, Thomas and Sivan, Pramod and Niittylä, Totte and Mellerowicz, Ewa J. and Tuominen, Hannele},
month = feb,
year = {2025},
note = {Publisher: Brill},
keywords = {NIR prediction models, NIR-imaging, Py-GC/MS, SilviScan, wood chemistry, wood properties},
pages = {1--16},
}
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We present a high-throughput and high-resolution near-infrared (NIR) method for combined imaging of the physical and chemical properties of stem sections from Populus trees. Pyrolysis-GC/MS data was used for sensitive and spatially resolved calibration of wood chemistry while SilviScan™ analyses provided reference data for wood physical properties with 25 μm resolution for wood density and 0.2–2.0 mm for microfibril angle (MFA). NIR prediction models were trained and calibrated on material from both field- and greenhouse-grown trees. Thus, the method was developed for NIR imaging of stem samples as small as 4 mm in diameter with an image resolution of 0.03 mm for small-diameter samples and 0.5 mm for samples with multiple annual rings. The NIR model performance, tested against data not used in the training set, reached the coefficient of determination ( R pred 2 ) values for wood density and MFA of 0.60 and 0.72, respectively. The NIR models for wood chemistry showed R pred 2 values of 0.78 and 0.77 for carbohydrates and lignin, respectively. Models for the G-, S- and H-type lignin had R pred 2 values between 0.58 and 0.86. In addition, we developed a prediction model for the determination of tension wood distribution. According to this model, tension wood was frequently observed in young greenhouse samples, which might explain the higher variation found in the chemical and physical properties of wood in greenhouse-grown compared to field-grown trees. The study also demonstrated that NIR-model estimations in image format can capture spatial variations that are not detectable in bulk analyses of wood properties. 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The NIR models for wood chemistry showed R pred 2 values of 0.78 and 0.77 for carbohydrates and lignin, respectively. Models for the G-, S- and H-type lignin had R pred 2 values between 0.58 and 0.86. In addition, we developed a prediction model for the determination of tension wood distribution. According to this model, tension wood was frequently observed in young greenhouse samples, which might explain the higher variation found in the chemical and physical properties of wood in greenhouse-grown compared to field-grown trees. The study also demonstrated that NIR-model estimations in image format can capture spatial variations that are not detectable in bulk analyses of wood properties. 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