The characteristic spectral selection method based on forward and backward interval partial least squares
In the near-infrared spectroscopy, the Forward Interval Partial Least Squares (FiPLS) and Backward Interval Partial Least Squares (BiPLS) are commonly used modeling methods, which are based on the wavelength variable selection. These methods are usually of high prediction accuracy, but are strongly...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Beijing Daxue Chubanshe, Peking University Press
2016
|
| Online Access: | http://hdl.handle.net/20.500.11937/22687 |
| _version_ | 1848750939449589760 |
|---|---|
| author | Qu, F. Ren, D. Hou, J. Zhang, Z. Lu, A. Wang, J. Xu, Honglei |
| author_facet | Qu, F. Ren, D. Hou, J. Zhang, Z. Lu, A. Wang, J. Xu, Honglei |
| author_sort | Qu, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In the near-infrared spectroscopy, the Forward Interval Partial Least Squares (FiPLS) and Backward Interval Partial Least Squares (BiPLS) are commonly used modeling methods, which are based on the wavelength variable selection. These methods are usually of high prediction accuracy, but are strongly characteristic of greedy search, which causes that the intervals selected are not good enough to indicate the analyte information. To solve the problem, a spectral characteristic intervals selection strategy (FB-iPLS) based on the combination of FiPLS and BiPLS is proposed. On the basis of spectral segmentation, both FiPLSs are used to select useful intervals, and BiPLS is used to delete useless intervals, so as to perform the selection and deletion of the characteristic variables alternatively, which conducts a two-way choice of the target characteristic variables, and is used to improve the robustness of the model. The experiments on determining the ethanol concentration in pure water are conducted by modeling with FiPLS, BiPLS and the proposed method. Since different size of intervals will affect the result of the model, the experiments here will also examine the model results with different intervals of these three models. When the spectrum is divided into 60 segments, the FB-iPLS method obtains the best prediction performance. The correlation coefficients (r) of the calibration set and validation set are 0.967 7 and 0.967 0 respectively, and the cross-validation root mean square errors (RMSECV) are 0.088 8 and 0.057 1, respectively. Compared with FiPLS and BiPLS, the overall prediction performance of the proposed model is better. The experiments show that the proposed method can further improve the predictive performance of the model by resolving the greedy search feature against BiPLS and FiPLS, which is more efficient for and representative of the selection of characteristic intervals. |
| first_indexed | 2025-11-14T07:44:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-22687 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:44:48Z |
| publishDate | 2016 |
| publisher | Beijing Daxue Chubanshe, Peking University Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-226872017-09-13T13:56:22Z The characteristic spectral selection method based on forward and backward interval partial least squares Qu, F. Ren, D. Hou, J. Zhang, Z. Lu, A. Wang, J. Xu, Honglei In the near-infrared spectroscopy, the Forward Interval Partial Least Squares (FiPLS) and Backward Interval Partial Least Squares (BiPLS) are commonly used modeling methods, which are based on the wavelength variable selection. These methods are usually of high prediction accuracy, but are strongly characteristic of greedy search, which causes that the intervals selected are not good enough to indicate the analyte information. To solve the problem, a spectral characteristic intervals selection strategy (FB-iPLS) based on the combination of FiPLS and BiPLS is proposed. On the basis of spectral segmentation, both FiPLSs are used to select useful intervals, and BiPLS is used to delete useless intervals, so as to perform the selection and deletion of the characteristic variables alternatively, which conducts a two-way choice of the target characteristic variables, and is used to improve the robustness of the model. The experiments on determining the ethanol concentration in pure water are conducted by modeling with FiPLS, BiPLS and the proposed method. Since different size of intervals will affect the result of the model, the experiments here will also examine the model results with different intervals of these three models. When the spectrum is divided into 60 segments, the FB-iPLS method obtains the best prediction performance. The correlation coefficients (r) of the calibration set and validation set are 0.967 7 and 0.967 0 respectively, and the cross-validation root mean square errors (RMSECV) are 0.088 8 and 0.057 1, respectively. Compared with FiPLS and BiPLS, the overall prediction performance of the proposed model is better. The experiments show that the proposed method can further improve the predictive performance of the model by resolving the greedy search feature against BiPLS and FiPLS, which is more efficient for and representative of the selection of characteristic intervals. 2016 Journal Article http://hdl.handle.net/20.500.11937/22687 10.3964/j.issn.1000-0593(2016)02-0593-06 Beijing Daxue Chubanshe, Peking University Press restricted |
| spellingShingle | Qu, F. Ren, D. Hou, J. Zhang, Z. Lu, A. Wang, J. Xu, Honglei The characteristic spectral selection method based on forward and backward interval partial least squares |
| title | The characteristic spectral selection method based on forward and backward interval partial least squares |
| title_full | The characteristic spectral selection method based on forward and backward interval partial least squares |
| title_fullStr | The characteristic spectral selection method based on forward and backward interval partial least squares |
| title_full_unstemmed | The characteristic spectral selection method based on forward and backward interval partial least squares |
| title_short | The characteristic spectral selection method based on forward and backward interval partial least squares |
| title_sort | characteristic spectral selection method based on forward and backward interval partial least squares |
| url | http://hdl.handle.net/20.500.11937/22687 |