Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy
Near-infrared (NIR) spectroscopy is an effective method for identifying wool and cashmere fibers, with high spectral data providing a wealth of information. However, a key issue is that the accuracy and robustness of subsequent estimates can be reduced by redundant and interfering wavelengths. For t...
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| Format: | Article |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Natural Fibers |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2024.2409877 |
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| author | Xin Chen Qingle Lan Yaolin Zhu Jinni Chen |
| author_facet | Xin Chen Qingle Lan Yaolin Zhu Jinni Chen |
| author_sort | Xin Chen |
| collection | DOAJ |
| description | Near-infrared (NIR) spectroscopy is an effective method for identifying wool and cashmere fibers, with high spectral data providing a wealth of information. However, a key issue is that the accuracy and robustness of subsequent estimates can be reduced by redundant and interfering wavelengths. For this reason, a novel interval-wavelength cascaded optimization method is proposed. Initially, the collected spectral data are preprocessed by standard normal variate transformation (SNV) to eliminate the scattering effect. Then, the backward interval partial least squares (BiPLS) algorithm is applied for the preliminary selection of spectral intervals, followed by the application of three different variable selection algorithms, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and whale optimization algorithm (WOA), for secondary wavelength optimization, respectively. Finally, both support vector machine (SVM) and random forest (RF) discriminant models are built to identify the extracted subset of wavelengths. In the experimental stage, the cascade method BiPLS-WOA selects 36 wavelengths, in SVM, the accuracy of the validation set reaches 96.9%, and the area under the ROC curve (AUC) can reach 99.3%. The results demonstrate that the proposed method can eliminate redundant and collinear variables, thereby validating the effectiveness of distinguishing wool and cashmere fibers. |
| format | Article |
| id | doaj-art-9ce981c45cee4e348ca7b90b2cd51fb5 |
| institution | OA Journals |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-9ce981c45cee4e348ca7b90b2cd51fb52025-08-20T02:22:02ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2024-12-0121110.1080/15440478.2024.2409877Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR SpectroscopyXin Chen0Qingle Lan1Yaolin Zhu2Jinni Chen3School of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaNear-infrared (NIR) spectroscopy is an effective method for identifying wool and cashmere fibers, with high spectral data providing a wealth of information. However, a key issue is that the accuracy and robustness of subsequent estimates can be reduced by redundant and interfering wavelengths. For this reason, a novel interval-wavelength cascaded optimization method is proposed. Initially, the collected spectral data are preprocessed by standard normal variate transformation (SNV) to eliminate the scattering effect. Then, the backward interval partial least squares (BiPLS) algorithm is applied for the preliminary selection of spectral intervals, followed by the application of three different variable selection algorithms, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and whale optimization algorithm (WOA), for secondary wavelength optimization, respectively. Finally, both support vector machine (SVM) and random forest (RF) discriminant models are built to identify the extracted subset of wavelengths. In the experimental stage, the cascade method BiPLS-WOA selects 36 wavelengths, in SVM, the accuracy of the validation set reaches 96.9%, and the area under the ROC curve (AUC) can reach 99.3%. The results demonstrate that the proposed method can eliminate redundant and collinear variables, thereby validating the effectiveness of distinguishing wool and cashmere fibers.https://www.tandfonline.com/doi/10.1080/15440478.2024.2409877NIR spectroscopywool and cashmereclassificationspectral optimizationBiPLSWOA |
| spellingShingle | Xin Chen Qingle Lan Yaolin Zhu Jinni Chen Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy Journal of Natural Fibers NIR spectroscopy wool and cashmere classification spectral optimization BiPLS WOA |
| title | Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy |
| title_full | Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy |
| title_fullStr | Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy |
| title_full_unstemmed | Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy |
| title_short | Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy |
| title_sort | non destructive identification of wool and cashmere fibers based on cascade optimizations of interval wavelength selection using nir spectroscopy |
| topic | NIR spectroscopy wool and cashmere classification spectral optimization BiPLS WOA |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2024.2409877 |
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