基于光谱和色谱数据融合策略的青叶胆及近似种的鉴别研究

doi:10.3964/j.issn.1000-0593(2020)08-2440-07

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Abstract Swertia leducii is an annual herbaceous plant of the genus Swertia. It has a remarkable high effective in treating liver inflammation. The appearance of S. leducii and the species of the same genus is very similar, and the whole dry herb of Swertia plants is often used as medicine. It is very difficult to correctly identify different species from the morphology. Nevertheless, It is different in treating effective due to different species with different chemical components. In this study, based on data fusion of spectra and chromatography, an effective identification method of S. leducii and its closest relative species was established to provide the scientific basis for authenticity and security of S. leducii medication. Fourier transform infrared (FTIR) and ultra performance liquid chromatography (UPLC) of 102 samples of Swertia were collected from 5 species. Standard normal variate (SNV), multiplicative signal correction (MSC), Savitzky-Golay smoothing (SG), first derivative (1D) and second derivative (2D) were used to treat raw spectral data. Then, the optimal spectral data was utilized to process Hierarchical cluster analysis (HCA) for analyzing the similarity and dissimilarity of genus Swertia with different species. Kennard-Stone algorithm was applied to divide 102 samples into the calibration set and validation set in accordance with 2∶1 ratio. The calibration set was established the random forest (RF) discriminant model basing on FTIR, UPLC, low-level and mid-level data fusion, and the validation set was used to test the predictive ability of these models. In addition, the model performance was evaluated by sensitivity, specificity, precision and accuracy. The results indicated that: (1) SNV+SG+2D was the optimal pretreatment that all samples were correctly classified with the highest R²Y (91.2%) and Q² (84.1%). (2) HCA could reflect the classification and genetic relationship of S. leducii and its wild relatives. The other 4 species excepting S. punicea were correctly classified and its total accuracy rate reached 93.1%. S. punicea, S. cincta and S. davidii had closed relationship with S. leducii while S. angustifolia was relative far. (3) Comparing the FTIR, UPLC, low-level data fusion and mid-level data fusion, the number of error samples in the classification of RF analysis were 1, 5, 1 and 0, respectively. In the RF models, the best classification of mid-level data fusion with none error samples was better than other data matrices. Mid-level data fusion combined with RF methods can identify different species of genus Swertia and display the genetic relationship between S. leducii and its wild relatives. Besides, it could provide a theoretical basis for the development of plant resources and quality control of genus Swertia.

Key words：Data fusion; Species differentiation; Swertia leducii; Closely relative species; Fourier transform infrared spectroscopy; Ultra-performance liquid chromatography

Received: 2019-08-06 Accepted: 2019-12-20

ZTFLH:

R282.5

Corresponding Authors: LI Li, WANG Yuan-zhong E-mail: lilyjsu@126.com; boletus@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)08-2440-07 OR https://www.gpxygpfx.com/EN/Y2020/V40/I08/2440

[1] HE Ting-nong, LIU Shang-wu, WU Qing-ru(何廷农，刘尚武，吴庆如). Flora of China Vol.62(中国植物志，第62卷). Beijing: Science Press(北京：科学出版社), 1988.
[2] Li J, Zhao Y L, Huang H Y, et al. The American Journal of Chinese Medicine, 2017, 45(4): 667.
[3] Chinese Pharmacopoeia Commission(中华人民共和国药典委员会). Pharmacopoeia of the People’s Republic of China，Part One(中华人民共和国药典第一部). Beijing: China Medical Science Press(北京：中国医药科技出版社), 2015.
[4] Wang Y Z, Liu E H, Li P. Journal of Pharmaceutical and Biomedical Analysis, 2017, 140: 20.
[5] WU Zhe, WANG Yuan-zhong, ZHANG Ji, et al(吴喆，王元忠，张霁，等). Chinese Traditional and Herbal Drugs(中草药), 2017, 48(11): 2279.
[6] SHI Chong-jing, WANG Shan-shan, CHENG Zhong-qin, et al(施崇精，王姗姗，程中琴，等). China Journal of Chinese Materia Medica(中国中药杂志), 2018, 43(11): 2313.
[7] Fu L, Zheng Y H, Zhang P C, et al. Bioelectrochemistry, 2019, 129: 199.
[8] Borràs E, Ferré J, Boqué R, et al. Analytica Chimica Acta, 2015, 891: 1.
[9] Ríos-reina R, Callejónr R M, Savorani F, et al. Talanta, 2019, 198: 560.
[10] Wu X M, Zuo Z T, Zhang Q Z, et al. Microchemical Journal, 2018, 143: 367.
[11] Sun W J, Zhang X, Zhang Z Y, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2017, 171: 72.
[12] DI Zhun, ZHANG Ji, ZHAO Yan-li, et al(狄准，张霁，赵艳丽，等). Chinese Traditional and Herbal Drugs(中草药), 2017, 48(9): 1860.
[13] Li J, Zhang J, Jin H, et al. Pharmacognosy Magazine, 2017, 13(49): 13.
[14] Wang Q Q, Huang H Y, Wang Y Z. Molecules, 2019, 24(7): 16.
[15] Oliveri P, Downey G. Trends in Analytical Chemistry, 2012, 35: 74.
[16] Cebi N, Durak M, Toker O, et al. Food Chemistry, 2016, 190: 1109.
[17] Horn B, Esslinger S, Pfister M, et al. Food Chemistry, 2018, 257: 112.
[18] Dejong S A, O’Brien W L, Lu Z Y, et al. Applied Spectroscopy, 2015, 69(6): 733.
[19] Pei Y F, Zuo Z T, Zhang Q Z, et al. Journal of Molecular Structure, 2019, 1196: 478.