A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis
Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2016/5416506 |
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| _version_ | 1832548568152408064 |
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| author | Haitao Chang Lianqing Zhu Xiaoping Lou Xiaochen Meng Yangkuan Guo Zhongyu Wang |
| author_facet | Haitao Chang Lianqing Zhu Xiaoping Lou Xiaochen Meng Yangkuan Guo Zhongyu Wang |
| author_sort | Haitao Chang |
| collection | DOAJ |
| description | Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression method is presented in order to build an accurate calibration model in this paper, where a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors. After the selection of calibration subset, the partial least squares regression is applied to build calibration model. The performance of the proposed method is demonstrated through a near-infrared spectroscopy dataset of pharmaceutical tablets. Compared with other local strategies with different similarity criterions, it has been shown that the proposed local errors regression can result in a significant improvement in terms of both prediction ability and calculation speed. |
| format | Article |
| id | doaj-art-c1f16fd855f740bcb125d248c9f62bb8 |
| institution | Kabale University |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-c1f16fd855f740bcb125d248c9f62bb82025-02-03T06:13:40ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732016-01-01201610.1155/2016/54165065416506A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral AnalysisHaitao Chang0Lianqing Zhu1Xiaoping Lou2Xiaochen Meng3Yangkuan Guo4Zhongyu Wang5School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaBeijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, ChinaBeijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, ChinaBeijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, ChinaBeijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, ChinaSchool of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaOver the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression method is presented in order to build an accurate calibration model in this paper, where a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors. After the selection of calibration subset, the partial least squares regression is applied to build calibration model. The performance of the proposed method is demonstrated through a near-infrared spectroscopy dataset of pharmaceutical tablets. Compared with other local strategies with different similarity criterions, it has been shown that the proposed local errors regression can result in a significant improvement in terms of both prediction ability and calculation speed.http://dx.doi.org/10.1155/2016/5416506 |
| spellingShingle | Haitao Chang Lianqing Zhu Xiaoping Lou Xiaochen Meng Yangkuan Guo Zhongyu Wang A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis Journal of Analytical Methods in Chemistry |
| title | A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis |
| title_full | A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis |
| title_fullStr | A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis |
| title_full_unstemmed | A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis |
| title_short | A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis |
| title_sort | new local modelling approach based on predicted errors for near infrared spectral analysis |
| url | http://dx.doi.org/10.1155/2016/5416506 |
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