|
|
|
|
|
|
Data Fusion of ATR-FTIR and UV-Vis Spectra to Identify the Origin of Polygonatum Kingianum |
ZHANG Jiao1, 2, WANG Yuan-zhong1, YANG Wei-ze1, ZHANG Jin-yu1* |
1. Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
2. College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China |
|
|
Abstract The quality of Polygonati Rhizoma medicinal materials is closely related to the original plants’ origin environment. It is necessary to ensure their quality control and drug safety by establishing a simple, rapid and accurate origin identification method for the medicinal materials.In this study, the attenuated total Reflection-Fourier transform infrared (ATR-FTIR) spectra and ultraviolet visible (UV-Vis) spectra of 133 Polygonatum kingianum rhizomes from 9 geographic origins in Yunnan, Sichuan and Guangxi Provinces were collected to establish random forest (RF) modelafter data pretreatment, respectively. ATR-FTIR and UV-Vis spectra data were directly connected in series to complete the RF model of low-level data fusion. Principal components (PCs) and latent variables (LVs) of the two spectra were extracted to achieve RF model ofmid-level (mid-PCs and mid-LVs) and high-level (high-PCs and high-LVs) data fusion. The accuracy (ACC), sensitivity (SEN) and specificity (SPE) of different models were compared to select the best model for origin identification. The results showed that the peaks of ATR-FTIR and UV-Visspectrain P. kingianum were similar, and their absorbance were different. There were 14 common peaks in ATR-FTIR spectra of P. kingianum, which were related to carbohydrate, steroidal saponins, flavonoids and alkaloids. The common peaks of UV-Visspectra in P. kingianum were mainly at 272 and 327 nm, which were related to flavonoids. For the RF models of ATR-FTIR, UV-Vis and low-level fusion, the ACC of the training set and prediction set were respectively (76.34%, 95.00%), (80.65%, 95.00%) and (83.87%, 100.00%), however, the SEN and SPE values were so low that they were not suitable to use. The SEN and SPE of mid-PCs and mid-LVs RF models were greater than 0.91 and 0.98, respectively. The ACC of the training set was 91.40% and 97.85%, respectively, and that of the prediction set both were 97.50%. The ACC of RF training set with high-PCs and high-LVs was 77.42% and 97.85%, respectively, and the prediction set ACC both were 95.00%. The RF model with high-PCs has poor identification effect, and the RF model with high-LVs was over-fitted. In summary, the identification of model from high to low was: mid-LVs>mid-PCs>low fusion>UV-Vis>ATR-FTIR>high-PCs. LVs extraction method is better than PCs for origin identification. RF model of mid-LVs established has the highest ACC with the best model performance, and the SEN and SPE greater than 0.98, and, which can provide a theoretical basis for the scientific evaluation of medicinal resources of Polygonati Rhizoma.
|
Received: 2020-05-03
Accepted: 2020-08-12
|
|
Corresponding Authors:
ZHANG Jin-yu
E-mail: jyzhang2008@126.com
|
|
[1] Zhao P, Zhao C, Li X, et al. Journal of Ethnopharmacology, 2018, 214: 274.
[2] Chinese Pharmacopoeia Commission(国家药典委员会). Pharmacopoeia of the People’s Republic of China(中华人民共和国药典), Part One(第一部). Beijing: China Medical Science Press(北京: 中国医药科学出版社), 2015. 306.
[3] ZHANG Jiao, WANG Yuan-zhong, YANG Wei-ze, et al(张 娇, 王元忠, 杨维泽, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2019, 44(10): 1989.
[4] LI Jing, WANG Ying-zhe, LIU Yu-cui, et al(李 婧, 王英哲, 刘玉翠, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2019, 44(24): 5368.
[5] JIAO Ji, CHEN Li-ming, SUN Rui-ze, et al(焦 劼,陈黎明, 孙瑞泽,等). Journal of Chinese Medicinal Materials(中药材), 2016, 39(3): 519.
[6] CHEN Long-sheng, DU Li-ji, CHEN Shi-jin, et al(陈龙胜, 杜李继, 陈世金, 等). Journal of Chinese Medicinal Materials(中药材), 2018, 41(4): 894.
[7] Zhou Y H, Zuo Z T, Xu F R, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 226: 117619.
[8] Zhao Y L, Yuan T J, Zhang J, et al. Journal of Chemometrics, 2019, 33(4): e3115.
[9] Yao S, Li T, Li J Q, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 198: 257.
[10] Wu X M, Zhang Q Z, Wang Y Z. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 205: 479.
[11] Qiu S, Wang J. Food Chemistry, 2017, 230: 208.
[12] Wang Y, Zuo Z T, Huang H Y, et al. Royal Society Open Science, 2019, 6(5): 190399.
[13] Hou L, Liu Y, Wei A. Industrial Crops and Products, 2019, 134: 146.
[14] Rodríguez S D, Rolandelli G, Buera M P. Food Chemistry, 2019, 274: 392.
[15] Chen H, Tan C, Lin Z, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 189: 183.
[16] SUN Yu-qing, HU Yu-zhu, DU Ying-xiang, et al(孙毓庆, 胡育筑, 杜迎翔, 等). Analytical Chemistry(分析化学). 3rd edition(第3版). Beijing: Science Press(北京: 科学出版社), 2011. 136.
[17] Pei Y F, Wu L H, Zhang Q Z, et al. Analytical Methods, 2019, 11(1): 113.
[18] Yang Y G, Zhao Y L, Zuo Z T, et al. Journal of AOAC International, 2019, 102(2): 457. |
[1] |
YANG Xin1, 2, XIA Min1, 2, YE Yin1, 2*, WANG Jing1, 2. Spatiotemporal Distribution Characteristics of Dissolved Organic Matter Spectrum in the Agricultural Watershed of Dianbu River[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2983-2988. |
[2] |
JIN Cheng-liang1, WANG Yong-jun2*, HUANG He2, LIU Jun-min3. Application of High-Dimensional Infrared Spectral Data Preprocessing in the Origin Identification of Traditional Chinese Medicinal Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2238-2245. |
[3] |
WU Chao1, QIU Bo1*, PAN Zhi-ren1, LI Xiao-tong1, WANG Lin-qian1, CAO Guan-long1, KONG Xiao2. Application of Spectral and Metering Data Fusion Algorithm in Variable Star Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1869-1874. |
[4] |
WU Mu-lan1, SONG Xiao-xiao1*, CUI Wu-wei1, 2, YIN Jun-yi1. The Identification of Peas (Pisum sativum L.) From Nanyang Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1095-1102. |
[5] |
WANG Zhi-xin, WANG Hui-hui, ZHANG Wen-bo, WANG Zhong, LI Yue-e*. Classification and Recognition of Lilies Based on Raman Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 183-189. |
[6] |
WANG Wen-jun1, SHA Yun-fei1, WANG Yang-zhong1, YU Jie1, LIU Tai-ang2, ZHANG Xu-feng3, MENG Xiang-zhou3, GE Jiong1*. Discriminating Flavor Styles via Data Fusion of NIR and EN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 133-137. |
[7] |
GENG Ying-rui1, SHEN Huan-chao1, NI Hong-fei2, CHEN Yong1, LIU Xue-song1*. Support Vector Machine Optimized by Near-Infrared Spectroscopic
Technique Combined With Grey Wolf Optimizer Algorithm to
Realize Rapid Identification of Tobacco Origin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2830-2835. |
[8] |
LI Qing1, 2, XU Li1, 2, PENG Shan-gui1, 2, LUO Xiao1, 2, ZHANG Rong-qin1, 2, YAN Zhu-yun3, WEN Yong-sheng1, 2*. Research on Identification of Danshen Origin Based on Micro-Focused
Raman Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1774-1780. |
[9] |
DAI Lu-lu1, YANG Ming-xing1, 2*, WEN Hui-lin1. Study on Chemical Compositions and Origin Discriminations of Hetian Yu From Maxianshan, Gansu Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1451-1458. |
[10] |
CHEN Feng-xia1, YANG Tian-wei2, LI Jie-qing1, LIU Hong-gao3, FAN Mao-pan1*, WANG Yuan-zhong4*. Identification of Boletus Species Based on Discriminant Analysis of Partial Least Squares and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 549-554. |
[11] |
WANG Dong1,2, HAN Ping1,2*, WU Jing-zhu3*, ZHAO Li-li4, XU Heng4. Non-Destructive Identification of the Heat-Damaged Kernels of Waxy Corn Seeds Based on Near-Ultraviolet-Visible-Shortwave and Near-Infrared Multi-Spectral Imaging Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2696-2702. |
[12] |
CHEN Qi1,3, PAN Tian-hong2,4*, LI Yu-qiang4, LIN Hong4. Geographical Origin Discrimination of Taiping Houkui Tea Using Convolutional Neural Network and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2776-2781. |
[13] |
LU Shi-yang1, 2, ZHANG Lei-lei1, 2, PAN Jia-rong1, 2, YANG De-hong1, 2, SUI Ya-nan1, 2, ZHU Cheng1, 2*. Study on the Indetification of the Geographical Origin of Cherries Using Raman Spectroscopy and LSTM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1177-1181. |
[14] |
SHA Yun-fei1, HUANG Wen1, WANG Liang1, LIU Tai-ang2,YUE Bao-hua2, LI Min-jie2, YOU Jing-lin2, GE Jiong1*, XIE Wen-yan1*. Merging MIR and NIR Spectral Data for Flavor Style Determination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 473-477. |
[15] |
CHEN Ying1,XU Yang-mei1, DI Yuan-jian1,CUI Xing-ning1,ZHANG Jie1,ZHOU Xin-de1,XIAO Chun-yan2, LI Shao-hua3. COD Concentration Prediction Model Based on Multi-Spectral Data Fusion and GANs Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 188-193. |
|
|
|
|