Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection
In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2018/5237308 |
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| _version_ | 1849308710096076800 |
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| author | Lianqing Zhu Haitao Chang Qun Zhou Zhongyu Wang |
| author_facet | Lianqing Zhu Haitao Chang Qun Zhou Zhongyu Wang |
| author_sort | Lianqing Zhu |
| collection | DOAJ |
| description | In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection. |
| format | Article |
| id | doaj-art-1a0fda323f6740b6bd8e6fdd4ed8daab |
| institution | Kabale University |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-1a0fda323f6740b6bd8e6fdd4ed8daab2025-08-20T03:54:21ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732018-01-01201810.1155/2018/52373085237308Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable SelectionLianqing Zhu0Haitao Chang1Qun Zhou2Zhongyu Wang3Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, ChinaSchool of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaDepartment of Chemistry, Tsinghua University, Beijing 100084, ChinaSchool of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaIn order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection.http://dx.doi.org/10.1155/2018/5237308 |
| spellingShingle | Lianqing Zhu Haitao Chang Qun Zhou Zhongyu Wang Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection Journal of Analytical Methods in Chemistry |
| title | Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection |
| title_full | Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection |
| title_fullStr | Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection |
| title_full_unstemmed | Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection |
| title_short | Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection |
| title_sort | improving the classification accuracy for near infrared spectroscopy of chinese salvia miltiorrhiza using local variable selection |
| url | http://dx.doi.org/10.1155/2018/5237308 |
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