基于LLE-RF的中药三维荧光光谱分类识别

doi:10.3969/j.issn.1000-1158.2020.02.23

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Abstract To research the non-linearity of the three dimensional fluorescence spectrum signal of traditional Chinese medicine, locally linear embedding (LLE) algorithm was used to extract the feature of three dimensional fluorescence spectrum signals for tonifying herbs. On the basis of the three dimensional fluorescence spectrum and contour line spectrum of 24 traditional Chinese medicines of different concentrations measured by fluorescence spectrometer,and ensemble empirical mode decomposition (EEMD) algorithm for noise reduction, the LLE algorithm was used to extract the characteristics of the three dimensional fluorescence spectrum data after noisereduction.Then, the extracted features were classified and identified by using random forest (RF) algorithm.In order to verify the effectiveness of LLE algorithm, the principal component analysis (PCA) algorithm was compared. Experimental results showed that the classification accuracy of the LLE-RF combination algorithm was 95%, which was higher than PCA-RF algorithm which proved the effectiveness of the algorithm.

Key words： metrology traditional Chinese medicine fingerprint three dimensional fluorescence spectroscopy LLE-RF classification recognition feature extraction

Received: 17 September 2018 Published: 17 February 2020

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TB99

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	Articles by authors
	FAN Feng-jie
	XUAN Feng-lai
	BAI Yang
	JI Hui-fang

Cite this article:

FAN Feng-jie,XUAN Feng-lai,BAI Yang, et al. Classification Recognition of Three Dimensional Fluorescence Spectrum Traditional Chinese Medicine Based on LLE-RF[J]. Acta Metrologica Sinica, 2020, 41(2): 263-268.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.02.23 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I2/263

［1］李强, 杜思邈, 张忠亮, 等. 中药指纹图谱技术进展及未来发展方向展望［J］. 中草药, 2013, 44(22): 3095-3104.
Li Q, Du S M, Zhang Z L, et al. Progress in fingerprint technology on Chinese materia medica and prospect of its future development［J］.Chinese Traditional and Herbal Drugs, 2013, 44(22): 3095-3104.
［2］盖云, 鲍成满, 叶树明, 等. 化学计量学方法在三维荧光光谱分析中的应用［J］. 光谱学与光谱分析, 2011, 31(7): 1828-1833.
Gai Y, Bao C M, Ye S M, et al. Application of Chemometric Methods in Three-dimensional Fluorescence Spectral Analysis［J］. Spectroscopy and Spectral Analysis, 2011, 31(7): 1828-1833.
［3］胡泽建, 王克闵, 冉绍春. 三维荧光谱参量化方法及其在油种鉴别中的应用［J］. 黄渤海海洋学报, 1998, 16(4): 35-41.
Hu Z J, Wang K M, Ran S C.Paraneterization of Three Dimensional Fluorescence Spectra and Its Application to Oil Identification［J］. Journal of Oceanography of Huanghai & Bohai seas, 1998, 16(4): 35-41.
［4］吴桂芳, 何勇. 应用可见/近红外光谱进行纺织纤维鉴别的研究［J］. 光谱学与光谱分析, 2010, 30(2): 331-335.
Wu G F, He Y. Identification of Varieties of Textile Fibers by Using Vis/NIR Infrared Spectroscopy Technique［J］. Spectroscopy and Spectral Analysis, 2010, 30(2): 331-335.
［5］Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding［J］. Science, 2000, 290(5500): 2323-2326.
［6］朱江淼, 孙盼盼, 高源, 等. 原子钟频差数据去噪算法的研究［J］. 计量学报, 2017, 38(4): 499-503.
Zhu J M,Sun P P, Gao Y, et al. The Denoise of the Atomic Clock Frequency Differences［J］. Acta Metrologica Sinica, 2017, 38(4): 499-503.
［7］段宇飞, 王巧华, 马美湖, 等. 基于LLE-SVR的鸡蛋新鲜度可见/近红外光谱无损检测方法［J］. 光谱学与光谱分析, 2016, 36(4): 981-985.
Duan Y F, Wang Q H, Ma M H, et al. Study on Non-Destructive Detection Method for Egg Freshness Basedon LLE-SVR and Visible/Near-Infrared Spectrum［J］. Spectroscopy and Spectral Analysis, 2016, 36(4): 981-985.
［8］Zhuo L, Cheng B, Zhang J. A comparative study of dimensionality reduction methods for large-scale image retrieval［J］. Neurocomputing, 2014, 141: 202-210.
［9］Demetgul M, Yildiz K, Taskin S, et al. Fault diagnosis on material handling system using feature selection and data mining techniques［J］. Measurement, 2014, 55: 15-24.
［10］Breiman L. Random Forests［J］. Machine Learning, 2001, 45(1): 5-32.
［11］王鹏,龚盼,冯定,等. 基于随机森林算法的井下原油含水率软测量方法［J］. 计量学报, 2019, 40(5): 835-841.
Wang P, Gong P, Feng D, et al. Soft Sensing Method for Water Cut of Crude Oil Based on Random Forest Algorithm［J］. Acta Metrologica Sinica, 2019, 40(5): 835-841.
［12］Ouallouche F, Lazri M, Ameur S. Improvement of rainfall estimation from MSG data using Random Forests classification and regression［J］. Atmospheric Research, 2018, 211: 62-72.
［13］Tomiyama S, Sakata-Yanagimoto M, Chiba S, et al. Development of automatic classification system for leukocyte images using Random Forest［J］. Electronics and Communications in Japan, 2018, 101(11): 13-19.