| 光谱学与光谱分析 |
|
|
|
|
|
| Study on Rapid Identification of Medicinal Plants of Paris Polyphylla from Different Origin Areas by NIR spectroscopy |
| ZHAO Yan-li1, ZHANG Ji1, YUAN Tian-jun2, SHEN Tao3, HOU Ying2, YANG Shi-hua2, LI Wei2, WANG Yuan-zhong1*, JIN Hang1* |
| 1. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China2. Yunnan Reascend Tobacco Technology (Group) Co. Ltd., Kunming 650106, China3. College of Resources and Environment, Yuxi Normal University, Yuxi 653100, China |
|
|
|
|
Abstract Based on near infrared spectroscopy, seventy samples of wild medicinal plants of paris polyphylla from Guizhou, Guangxi and Yunnan Provinces were collected to identify their geographical origins. Multiplication signal correction (MSC), standard normal variate (SNV), first derivative (FD), second derivative (SD), savitzky-Golay filter (SG), and Norris derivative filter (ND) were conducted to optimize the original spectra of fifty samples of training set. The results showed that the method MSC combined with SD and ND presented the best results of spectra pretreatment. According to spectrum standard deviation, spectrum range (7 450～4 050 cm-1) was chosen and principal component analysis-mahalanobis distance (PCA-MD) method was used to build the model. Its first three principal components, i.e. cumulative contribution, determination coefficient (R2), root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) were 89.44%, 97.58%, 0.179 6 and 0.266 4, respectively, and the prediction accuracy is 90%. Furthermore, according to variable importance plot (VIP), spectrum range (7 135.33～4 007.35 cm-1) was chosen and partial least square discrimination analysis (PLS-DA) was applied to establish the model. Its first three principal components cumulative contribution, R2, RMSEC and RMSEP were 89.28%, 95.88%, 0.234 8 and 0.348 2, respectively, and the prediction accuracy is 100%. Comparing the two methods, we found that spectrum range chosen by VIP and model built by PLS-DA could provide greater accuracy in identifying paris polyphylla from different origin areas. The method supplied foundation for authenticity and quality evaluation of traditional Chinese medicine.
|
|
Received: 2013-08-17
Accepted: 2013-11-15
|
|
|
|
Corresponding Authors:
WANG Yuan-zhong, JIN Hang
E-mail: boletus@126.com;jinhang2009@126.com
|
|
[1] Man S L, Gao W Y, Zhang Y J, et al. Journal of Chromatography B. 2010, 878(29): 2913.
[2] LI Heng(李 恒). The Genus Paris (Trilliaceae)(重楼属植物). Beijing: Science Press(北京：科学出版社), 1998. 14.
[3] China Pharmacopoeia Commission(国家药典委员会). Chinese Pharmacopoeia of the People’s Republic of China (Part One)(中华人民共和国药典，Ⅰ部). Beijing: China Medical Science Press(北京：中国医药科技出版社), 2010. 243.
[4] Man S L, Gao W Y, Zhang Y J, et al. Analytical and Bioanalytical Chemistry, 2009, 395(2): 495.
[5] Zhao J L, Mou Y, Shan T J, et al. Molecules, 2010, 15: 7961.
[6] Zhang J Y, Wang Y Z, Zhao Y L, et al. Journal of Asian Natural Products Research, 2011, 13(7): 670.
[7] Cheng Z X, Liu B R, Qian X P, et al. Journal of Ethnopharmacology, 2008, 120(2): 129.
[8] Xiao X, Bai P, Nguyen T M B, et al. Molecular Cancer Therapeutics, 2009, 8(5): 1179.
[9] Man S L, Gao W Y, Zhang Y J, et al. Steroids, 2009, 74(13-14): 1051.
[10] Man S L, Gao W Y, Zhang Y J, et al. Archives of Pharmacal Research, 2011, 34(1): 43.
[11] Kong M J, Fan J Q, Dong A Q, et al. Acta Biochimica et Biophysica Sinica, 2010, 42(11): 827.
[12] Wang G X, Han J, Zhao L W, et al. Phytomedicine, 2010, 17(14): 1120.
[13] Guo L, Su J, Deng B W, et al. Human Reproduction, 2008, 23(4): 964.
[14] ZHANG Jin-yu, WANG Yuan-zhong, ZHAO Yan-li, et al(张金渝，王元忠，赵艳丽，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(8): 2176.
[15] Galtier O, Dupuy N, Le Dreau Y, et al. Analytica Chimica Acta, 2007, 595(1-2): 136.
[16] Eriksson L, Johansson E, Kettaneh Wold N, et al. Multi-and Megavariate Data Analysis. Sweden：Umetrics AB (PartⅠ,Ⅱ), 2006.
|
| [1] |
WANG Wen-xiu, PENG Yan-kun*, FANG Xiao-qian, BU Xiao-pu. Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2094-2100. |
| [2] |
LE Ba Tuan1, 3, XIAO Dong1*, MAO Ya-chun2, SONG Liang2, HE Da-kuo1, LIU Shan-jun2. Coal Classification Based on Visible, Near-Infrared Spectroscopy and CNN-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2107-2112. |
| [3] |
LIU Jin, LUAN Xiao-li*, LIU Fei. Near Infrared Spectroscopic Modelling of Sodium Content in Oil Sands Based on Lasso Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2274-2278. |
| [4] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
| [5] |
XU Wei-jie1, WU Zhong-chen1, 2*, ZHU Xiang-ping2, ZHANG Jiang1, LING Zong-cheng1, NI Yu-heng1, GUO Kai-chen1. Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1926-1932. |
| [6] |
LI Ying1, LI Yao-xiang1*, LI Wen-bin2, JIANG Li-chun3. Model Optimization of Wood Property and Quality Tracing Based on Wavelet Transform and NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1384-1392. |
| [7] |
DU Jian1, 2, HU Bing-liang1*, LIU Yong-zheng1, WEI Cui-yu1, ZHANG Geng1, TANG Xing-jia1. Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1514-1519. |
| [8] |
HAN Guang, LIU Rong*, XU Ke-xin. Extraction of Effective Signal in Non-Invasive Blood Glucose Sensing with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1599-1604. |
| [9] |
WANG Li-shuang, ZHANG Wen-bo*, TONG Li. Studies on Dimensional Stability of Wood under Different Moisture Conditions by Near Infrared Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1066-1069. |
| [10] |
HUANG Hua1, WU Xi-yu2, ZHU Shi-ping1*. Feature Wavelength Selection and Efficiency Analysis for Paddy Moisture Content Prediction by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1070-1075. |
| [11] |
LI Hao-guang1,2, YU Yun-hua1,2, PANG Yan1, SHEN Xue-feng1,2. Study of Maize Haploid Identification Based on Oil Content Detection with Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1089-1094. |
| [12] |
PENG Cheng1, FENG Xu-ping2*, HE Yong2, ZHANG Chu2, ZHAO Yi-ying2, XU Jun-feng1. Discrimination of Transgenic Maize Containing the Cry1Ab/Cry2Aj and G10evo Genes Using Near Infrared Spectroscopy (NIR)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1095-1100. |
| [13] |
WU Zhe1, 2, 3, ZHANG Ji1, 2, ZUO Zhi-tian1, 2, XU Fu-rong3, WANG Yuan-zhong1, 2*, ZHANG Jin-yu1, 2*. Rapid Discrimination of the Different Processed Paris polyphylla var. yunnanensis with Infrared Spectroscopy Combined with Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1101-1106. |
| [14] |
XIA Ji-an1, YANG Yu-wang1*, CAO Hong-xin2, HAN Chen1, GE Dao-kuo2, ZHANG Wen-yu2. Classification of Broad Bean Pest of Visible-Near Infrared Spectroscopy Based on Cloud Computing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 756-760. |
| [15] |
MAO Ya-chun, WANG Dong, WANG Yue, LIU Shan-jun*. A FeO/TFe Determination Method of BIF Based on the Visible and Near-Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 765-770. |
|
|
|
|
