Abstract:As three-dimensional fluorescence spectroscopy has many advantages,such as good selectivity,high sensitivity,fast analysis,it has been widely used in many fields.As one of the characteristics of traditional Chinese medicine(TCM),Chinese herbal medicine property (CHMP) is the core of TCM. Objective discrimination of the properties of TCM is the key issues of modernization of TCM. The identification of traditional Chinese medicine property is of great significance in the theoretical study of Chinese medicine. Most of the molecules in traditional Chinese medicine have the ability to generate fluorescence. According to the characteristics of the three-dimensional fluorescence spectrum of traditional Chinese medicines, the classification and recognition were studied from the perspective of the properties of traditional Chinese medicines. Firstly, the three-dimensional fluorescence spectral data of 5 different concentrations of 23 cold and warm Chinese medicinal solutions were acquired by FS920 fluorescence spectrometer. Then, the ensemble empirical mode decomposition (EEMD) algorithm is applied to denoise the spectrogram, based on the analysis of noise in different excitation and emission wavelength ranges of different samples. Based on the local linear embedding (LLE) algorithm, feature extraction of spectral data is carried out. The extracted eigenvectors are input into the random forest (RF) to construct LLE-RF classification model. The classification effect of LLE-RF classification model on fluorescence spectrum data of cold and warm Chinese medicines was analyzed under different parameters. The sample ratio of the training set and test set in RF classifier is set to 3∶1 and 2∶1. The correct rate of LLE classification is analyzed when the nearest neighbor points k is 7~18 and the eigenvalue dimension d is 6, 7, 8, 9 and 10. When the nearest neighbor points k is 12 and the eigenvalue dimension d is 7, the accuracy of LLE-RF model for classification of Chinese herbal medicines was 96.6%. Finally, the classification effect of SVM classifier constructed with different kernels on fluorescence spectrum data of cold and warm Chinese medicines was compared under the same ratio of r. When multi-layer perceptron is used as the kernel function, the classification effect is the worst. When r=3/4 and radial basis function is used as the kernel function, the classification accuracy is 82.1%. The results show that the method of combining fluorescence spectroscopy with LLE-RF can effectively recognize cold and warm Chinese medicines, and the classification effect is better than LLE-SVM.
Key words:Three-dimensional fluorescence spectrum; Feature extraction; Traditional Chinese medicine property; Local linear embedding; Random forest
樊凤杰,轩凤来,白 洋,纪会芳. 基于三维荧光光谱特征的中药药性模式识别研究[J]. 光谱学与光谱分析, 2020, 40(06): 1763-1768.
FAN Feng-jie, XUAN Feng-lai, BAI Yang, JI Hui-fang. Pattern Recognition of Traditional Chinese Medicine Property Based on Three-Dimensional Fluorescence Spectrum Characteristics. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1763-1768.
[1] ZHANG Xin-xin, LI Yu, JI Yu-jia, et al(张新新, 李 雨, 纪玉佳, 等). Journal of Shandong University·Health Science(山东大学学报·医学版), 2012, 50(1): 143.
[2] LI Jia-hui, CHEN Ren-shou, LI Lu-jie(李加慧, 陈仁寿, 李陆杰). Journal of Traditional Chinese Medicine(中医杂志), 2019, 60(1): 67.
[3] LIU Min, WU Dong-xue, LI Jing, et al(刘 敏, 吴东雪, 李 晶, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2019, 44(2): 218.
[4] WANG Xiao-yan, LI Feng(王晓燕, 李 峰). Liaoning Journal of Traditional Chinese Medicine(辽宁中医杂志), 2015, 42(6): 1303.
[5] WU Si-yuan, HU You-fen, LIU Xiao-wei, et al(吴思媛, 胡幼芬, 刘晓伟, 等). Software Guide(软件导刊), 2014,13(10): 71.
[6] CHEN Zhao, CAO Yan-feng, HE Shuai-bing, et al(陈 昭, 曹燕凤, 何帅兵, 等). China Journal of Traditional Chinese Medicine and Pharmacy(中华中医药杂志), 2017, 32(5): 2107.
[7] Henrique Z P, Alonso J B, Ferrer M A,et al. IEEE Transactions on Audio Speech and Language Processing, 2009, 17(6): 1186.
[8] QIN Xi-wen, LÜ Si-qi, LI Qiao-ling(秦喜文, 吕思奇, 李巧玲). Chinese Journal of Biomedical Engineering(中国生物医学工程学报), 2018, 37(6): 665.
[9] Shinnosuke Tomiyama, Mamiko Sakata-yanagimoto, Shigeru Chiba, et al. Electronics and Communications in Japan, 2018, 101(11): 13.
[10] LIU Peng, WU Rui-mei, YANG Pu-xiang, et al(刘 鹏, 吴瑞梅, 杨普香, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(1): 193.