Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy
Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, v...
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MDPI AG
2022-01-01
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| Online Access: | https://www.mdpi.com/1999-4907/13/1/62 |
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| author | Ying Li Guozhong Wang Gensheng Guo Yaoxiang Li Brian K. Via Zhiyong Pei |
| author_facet | Ying Li Guozhong Wang Gensheng Guo Yaoxiang Li Brian K. Via Zhiyong Pei |
| author_sort | Ying Li |
| collection | DOAJ |
| description | Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (<i>Populus tomentosa carriere</i>) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.870; RMSEP = 13 Kg/m<sup>3</sup>; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>RPD</mi></mrow><mi>p</mi></msub></mrow></semantics></math></inline-formula> = 2.774). |
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| language | English |
| last_indexed | 2024-03-10T01:27:46Z |
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| spelling | doaj.art-775cf82539474e33b71d0b0f4733879a2023-11-23T13:47:12ZengMDPI AGForests1999-49072022-01-011316210.3390/f13010062Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared SpectroscopyYing Li0Guozhong Wang1Gensheng Guo2Yaoxiang Li3Brian K. Via4Zhiyong Pei5College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Engineering and Technology, Northeast Forestry University, Harbin 150040, ChinaForest Products Development Center, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USACollege of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaWood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (<i>Populus tomentosa carriere</i>) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.870; RMSEP = 13 Kg/m<sup>3</sup>; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>RPD</mi></mrow><mi>p</mi></msub></mrow></semantics></math></inline-formula> = 2.774).https://www.mdpi.com/1999-4907/13/1/62Vis-NIR spectroscopywood densityspectral pre-processingchemometrics |
| spellingShingle | Ying Li Guozhong Wang Gensheng Guo Yaoxiang Li Brian K. Via Zhiyong Pei Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy Forests Vis-NIR spectroscopy wood density spectral pre-processing chemometrics |
| title | Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy |
| title_full | Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy |
| title_fullStr | Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy |
| title_full_unstemmed | Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy |
| title_short | Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy |
| title_sort | spectral pre processing and multivariate calibration methods for the prediction of wood density in chinese white poplar by visible and near infrared spectroscopy |
| topic | Vis-NIR spectroscopy wood density spectral pre-processing chemometrics |
| url | https://www.mdpi.com/1999-4907/13/1/62 |
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