Identification of Guiboutia species by NIR-HSI spectroscopy
Abstract Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15719-0 |
| _version_ | 1811224087015456768 |
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| author | Xiaoming Xue Zhenan Chen Haoqi Wu Handong Gao |
| author_facet | Xiaoming Xue Zhenan Chen Haoqi Wu Handong Gao |
| author_sort | Xiaoming Xue |
| collection | DOAJ |
| description | Abstract Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate , G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis. |
| first_indexed | 2024-04-12T08:44:11Z |
| format | Article |
| id | doaj.art-a4cf4153c36b4551a78026ec312aa5dc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T08:44:11Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a4cf4153c36b4551a78026ec312aa5dc2022-12-22T03:39:47ZengNature PortfolioScientific Reports2045-23222022-07-0112111110.1038/s41598-022-15719-0Identification of Guiboutia species by NIR-HSI spectroscopyXiaoming Xue0Zhenan Chen1Haoqi Wu2Handong Gao3Nanjing Forest Police CollegeNanjing Forestry UniversityNanjing Forestry UniversityNanjing Forestry UniversityAbstract Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate , G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.https://doi.org/10.1038/s41598-022-15719-0 |
| spellingShingle | Xiaoming Xue Zhenan Chen Haoqi Wu Handong Gao Identification of Guiboutia species by NIR-HSI spectroscopy Scientific Reports |
| title | Identification of Guiboutia species by NIR-HSI spectroscopy |
| title_full | Identification of Guiboutia species by NIR-HSI spectroscopy |
| title_fullStr | Identification of Guiboutia species by NIR-HSI spectroscopy |
| title_full_unstemmed | Identification of Guiboutia species by NIR-HSI spectroscopy |
| title_short | Identification of Guiboutia species by NIR-HSI spectroscopy |
| title_sort | identification of guiboutia species by nir hsi spectroscopy |
| url | https://doi.org/10.1038/s41598-022-15719-0 |
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