| 光谱学与光谱分析 |
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| Injection by Near Infrared Diffuse Reflectance Spectroscopy |
| SONG Yan1, 2, XIE Yun-fei1, ZHANG Yong3, 4, LI Zhi-shi1, CONG Qian3, ZHAO Bing1* |
1. State Key Laboratory of Supramolecular Structure and Materials, Jilin University, Changchun 130012, China
2. Center for New Drugs Research, Changchun University of Traditional Chinese Medicine, Changchun 130117, China
3. Key Laboratory for Terrain-Machine Bionics Engineering, Ministry of Education, Jilin University, Changchun 130022, China
4. College of Information Engineering, Jilin Teachers’ Institute of Engineering and Technology, Changchun 130052, China |
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Abstract In the present study, a total of 47 levofloxacin hydrochloride injection samples were detected by near infrared (NIR) spectroscopy, and 37 samples were randomly selected to establish the quantitative models by partial least squares (PLS) and artificial neural network (ANN) technology, while other 12 samples were used for prediction. On the one hand, the model was established by PLS, the coefficient of determination (R2) of the prediction is 0.964, and the root mean squared error of prediction (RMSEP) is 0.242 8. On the other hand, after the spectrum variables were highly effectively compressed using the wavelet transformation technology, the quantitative analysis model of levofloxacin hydrochloride was established through the ANN technology. The R2 and RMSEP of the model is 0.944 and 0.572 2，respectively. In this work, we have a detailed comparison between the two technologies in the progress of two quantitative models and optimizing correlative parameter, and finally we got a satisfied result. The simulation experiment indicated that the above PLS model is more steady and precise than ANN model, which can get hold of a rapid and nondestructive quantitative analysis result of the injection. Thus, the research can provide powerful scientific basis and technical support for further analysis of levofloxacin hydrochloride injection.
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Received: 2008-10-22
Accepted: 2009-01-26
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Corresponding Authors:
ZHAO Bing
E-mail: zhaobing@jlu.edu.cn
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[1] Bailac S, Ballesteros O, Jimenez-Lozano E, et al. J. Chromatography A, 2004, 1029: 145.
[2] Pharmacopoeia Committee of the People’s Republic of China(中华人民共和国药典编委会编). Pharmacopoeia of the People’s Republic of China(中华人民共和国药典). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2000. 185.
[3] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄，赵龙莲，韩东海，等). Principle and Application of Near Infrared Spectroscopy(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2005. 1.
[4] SUN Jun-ming, HAN Fen-xia, YAN Shu-rong, et al(孙君明, 韩粉霞, 闫淑荣, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(6): 1290.
[5] BAI Ling, NI Yong-nian (白 玲, 倪永年). Chinese Journal of Analytical Chemistry(分析化学), 2002, 30: 39.
[6] Feisi Technical Product Research and Development Center(飞思科技产品研发中心). The Theory of the Artificial Neural Networks and Its Realizations in MATLAB 7(神经网络理论与MATLAB 7实现). Beijing: Publishing House of Electronics Industry(北京: 电子工业出版社), 2005. 114.
[7] WANG Guo-qing, SHAO Xue-guang(王国庆, 邵学广). Chinese Journal of Analytical Chemistry(分析化学), 2005, 33: 191.
[8] Feisi Technical Product Research and Development Center(飞思科技产品研发中心). The Theory of Wavelet Transform and Its Realizations in MATLAB 7(小波分析理论与MATLAB 7 实现). Beijing: Publishing House of Electronics Industry(北京: 电子工业出版社), 2005. 315.
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