Identification of Guiboutia species by NIR-HSI spectroscopy

Xue, Xiaoming; Chen, Zhenan; Wu, Haoqi; Gao, Handong

doi:10.1038/s41598-022-15719-0

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…Air-dried samples (moisture content of 11.5%-12%) were polished using sandpaper to reduce the roughness of surface and cut to 20 cubic mm (sap wood). According to our previous research, a transverse section of wood is more suitable for wood identification [27]. In this study, spectral data were scanned in a transverse section of the samples.…”

Section: Samplesmentioning

confidence: 99%

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Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI)

Xue

Chen

et al. 2023

Forests

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Pterocarpus santalinus is considered among the finest luxury woods in the world and has potential commercial and medicinal value. Due to its rich hue and high price, Pterocarpus santalinus has often been substituted and mislabeled with other woods of lower economic value. To maintain the order of the timber market and the interests of consumers, it is necessary to establish a fast and reliable method for Pterocarpus species identification. In this study, wood samples of Pterocarpus santalinus and nine other wood samples commonly used for counterfeiting were analyzed by visible light/near-infrared (Vis/NIR) hyperspectral imaging (HSI). The spectral data were preprocessed with different algorithms. Principal component analysis (PCA) was applied in different spectral ranges: 400~2500 nm, 400~800 nm, and 800~2500 nm. Partial least squares discriminant analysis (PLS-DA) and square support vector machine (SVM) modeling methods were performed for effective discrimination. The best classification model was SVM combined with a normalization preprocessing method in whole spectral range (400~2500 nm), with prediction accuracy higher than 99.8%. The results suggest that the use of Vis/NIR-HSI in combination with chemometric approaches can be used as an effective tool for the discrimination of Pterocarpus santalinus.

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Section: Samplesmentioning

confidence: 99%

“…Therefore, Vis/NIR-HSI is more suitable for the analysis of heterogeneous samples and allows more reliable qualitative identification using both spatial and spectral information. Although this technique requires strong professional judgment, it has greater potential when combined with machine learning algorithms [27].…”

Section: Introductionmentioning

confidence: 99%

Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI)

Xue

Chen

et al. 2023

Forests

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“…Five air-dried samples (moisture content of 11.5%-12%) were prepared for each tree with the dimensions of 100 mm x 100 mm x 100 mm (sap wood). According to our previous research, transverse section of wood is more suitable for wood identi cation (Xue et al, 2022). In this study, spectral data was scanned in transverse section of the samples.…”

Section: Samplesmentioning

confidence: 99%

“…Compared to NIR, NIR-HSI can be better detect the optical characteristic of samples, which enables both morphological and compositional inspection. Therefore, this technology is more suitable for the analysis of heterogeneous samples and allows more reliable qualitative identi cation using both spectral and spatial information (Xue et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Identification of hybrid tuliptree and parental species by near infrared hyperspectral imaging （NIR-HSI） spectroscopy

Chen

Gao

et al. 2022

Preprint

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Hybrid tuliptree (Liriodendron sion-americanum P.C. Yieh ex Shang et Z.R. Wang) has good wood quality and been widely used in furniture and other fields. Compared with parental species, hybrid tuliptree displayed faster growth rate, better wood quality and wider adaptability. But the morphological properties of hybrid tuliptree and parental species are highly similar, its identification has become an urgent problem. This study aims to evaluate the capabilities of near infrared hyperspectral imaging (NIR-HSI) technique to identify hybrid tuliptree, Chinese tuliptree, and tuliptree. For this purpose, the building models were carried out using Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) after different data treatment. The results demonstrated that NIR-HSI spectroscopy combined with successive projections algorithm (SPA) treatment and SVM model can achieve the highest prediction accuracy (100%). This study shows that NIR-HSI technique has great potential in the genus Liriodendron identification and is suitable for classifying highly similar woods.

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“…By capturing extensive chemical and spatial information across Vis to NIR spectra, HSI enables researchers to detect subtle differences in species and materials through their unique biochemical signatures. This technology is particularly effective in wood science, where it provides detailed insights into the microscopic structure and chemical alterations of wood, allowing for the identification of key constituents like lignin and cellulose which are crucial for assessing wood's mechanical properties and durability (Wang et al, 2022;Xue et al, 2022Xue et al, , 2023. Despite facing challenges related to data storage and processing demands, the future of HSI in these diverse applications looks promising, thanks to ongoing advancements in sensor technology and algorithm development aimed at enhancing data handling and robustness (Amaral et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species

Chen,

Xue,

et al. 2024

Front. Plant Sci.

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IntroductionThis study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade.MethodsWe integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383–982 nm), NIR (982–2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance.ResultsWith optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power.DiscussionThese findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

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Identification of Guiboutia species by NIR-HSI spectroscopy

Cited by 8 publications

References 22 publications

Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI)

Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI)

Identification of hybrid tuliptree and parental species by near infrared hyperspectral imaging （NIR-HSI） spectroscopy

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species

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