傅里叶变换红外光谱技术对金银花中有效成分定量模型建立及含量测定

doi:10.3964/j.issn.1000-0593(2024)02-0467-07

摘要
参考文献
相关文章 (15)

全文: PDF (2556 KB)
输出: BibTeX | EndNote (RIS)

摘要：中药材“安全、稳定、有效、可靠”是中医药现代化发展的现实需求，针对中药材质量良莠不齐的实际情况探索快速、精准、无损新型含量测定方法是目前中药材质量控制领域亟待解决问题之一。金银花有效成分复杂、质量波动较大，利用傅里叶变换红外光谱技术对金银花有效成分进行高效、无损快速测定可能是推动其质量控制的有效措施。该研究将HPLC法与傅里叶变换红外光谱技术相结合，运用化学计量学方法，建立金银花有效成分的含量测定模型，以期为金银花的快速、精准含量测定提供新方法。利用傅里叶变换光谱仪采集了河南、河北、山东三个产地共64份金银花的红外光谱，使用高效液相色谱(HPLC)法同时测定金银花中绿原酸、当药苷、断氧化马钱子苷、木犀草苷、异绿原酸A、异绿原酸C六种有效成分的含量。使用TQ分析软件中多元散射校正（MSC)、标准正态变换（SNV）、偏最小二乘法（PLS）、逐步多元线性回归（SMLR）、一阶导数（1st）、二阶导数（2nd）及SG卷曲平滑和诺里斯导数滤波（ND）进行光谱处理建立模型。经过对河南、河北、山东共64份金银花样品的化学计量学分析，结果表明PLS+MSC+2nd Der+NS对绿原酸含量测定效果最好，相关系数为0.754 9，平均绝对偏差为0.24%；PLS+MSC+1st Der+ND对当药苷的含量测定效果最好，相关系数为0.936 9，平均绝对偏差为0.00%；PLS+MSC+1st Der+SG对断氧化马钱子苷含量测定效果最好，相关系数为0.967 8，平均绝对偏差为-0.06%；PLS+MSC+1st Der+NS、PLS+MSC+1st Der+SG以及PLS+SNV+1st Der+SG对木犀草苷的含量测定效果最好，相关系数为0.859 0，平均绝对偏差为0.01%；PLS+MSC+2nd Der+ND对异绿原酸A的含量测定效果最好，相关系数为0.933 9，平均绝对偏差为0.11%；PLS+MSC+2nd Der+SG对异绿原酸C的含量测定效果最好，相关系数为0.866 1，平均绝对偏差为0.01%。通过傅里叶变换光谱技术建立对金银花有效成分的定量模型可以实现对未知金银花有效成分的含量测定，该研究为金银花有效成分的快速、无损测定提供了新的方法依据，有助于推动中药材质量的稳定、可控。

关键词：金银花；定量模型；傅里叶变换红外图谱；含量测定

Abstract：Chinese medicinal materials are “safe, stable, effective and reliable”, which is the real demand for the modernization and development of Chinese medicine. To explore new rapid, accurate and non-destructive content determination methods for Chinese medicinal materials with varying quality is one of the urgent problems in the field of quality control of Chinese medicinal materials. The active ingredients of Lonicerae Japonicae Flos are complex and fluctuate in quality. The efficient, non-destructive and rapid determination of the active ingredients of Lonicerae Japonicae Flos using Fourier transform infrared spectroscopy may be an effective measure to promote its quality control. In this study, the HPLC method was combined with Fourier transform infrared spectroscopy to establish a model for the determination of the active ingredients of Lonicerae Japonicae Flos using chemometric methods, in order to provide a new method for the rapid and accurate content determination of Lonicerae Japonicae Flos. In this study, the infrared spectra of 64 Lonicera Japonica Flos samples from Henan, Hebei and Shandong provinces were collected using a Fourier transform spectrometer. Simultaneous determination of the contents of six active ingredients in Lonicera Japonica Flos including chlorogenic acid, sweroside, secoxyloganin, cynaroside, isochlorogenic acid A and isochlorogenic acid C by HPLC. Multiple scattering correction (MSC), stand ard normal transformation (SNV), partial least squares (PLS), stepwise multiple linear regression (SMLR), first derivative (1st), second derivative (2nd), SG curl smoothing and Norris derivative filtering (ND) in TQ analysis software are used for spectral processing to establish the model after the chemometric analysis of 64 Lonicera Japonica Flos samples from Henan, Hebei and Shand ong. The result shows that the model of “PLS+MSC+2nd Der+NS” has the best prediction effect on chlorogenic acid content, with a correlation coefficient of 0.754 9 and an average absolute deviation of 0.24%. The “PLS+MSC+1st Der+ND” model has the best prediction effect on sweroside content, with a correlation coefficient of 0.936 9 and an average absolute deviation of 0.00%. The “PLS+MSC+1st Der+SG” model has the best prediction effect on secoxyloganin content, with a correlation coefficient of 0.967 8 and an average absolute deviation of -0.06%, the model of “PLS+MSC+1st Der+NS, PLS+MSC+1st Der+SG and PLS+SNV+1st Der+SG” has the best prediction effect on cynaroside content, with a correlation coefficient of 0.859 0 and an average absolute deviation of 0.01%. The model of “PLS+MSC+2nd Der+ND” has the best prediction effect on isochlorogenic acid A content, with a correlation coefficient of 0.933 9 and an average absolute deviation of 0.11%. The model of “PLS+MSC+2nd Der+SG” has the best prediction effect on isochlorogenic acid C content, with a correlation coefficient of 0.866 1 and an average absolute deviation of 0. 01%. It can be seen that the quantitative model of the active ingredients in Lonicerae Japonicae Flos established by Fourier transform spectral data can realize the content prediction of the active ingredients in unknown Lonicerae Japonicae Flos samples. This study provides a new method for the rapid and non-destructive testing of the active ingredients of Lonicerae Japonicae Flos, and also helps to achieve stable and controllable quality of traditional Chinese medicinal materials.

Key words：Lonicera Japonica Flos; Quantitative model; FT-infrared spectrum; Content prediction

收稿日期: 2022-06-15 修订日期: 2022-10-28

中图分类号:

R284.1

基金资助: 国家自然科学基金项目(82104329)资助

通讯作者: 杨林林，董诚明 E-mail: yangll-hatcm@hactcm.edu.cn；dcm371@sohu.com

作者简介: 顾旭鹏，1998年生，河南中医药大学药学院硕士研究生 e-mail: 2321665159@qq.com

引用本文:

顾旭鹏，杨林林，齐大明，刘天亮，董诚明. 傅里叶变换红外光谱技术对金银花中有效成分定量模型建立及含量测定[J]. 光谱学与光谱分析, 2024, 44(02): 467-473.
GU Xu-peng, YANG Lin-lin, QI Da-ming, LIU Tian-liang, DONG Cheng-ming. Quantitative Modeling and Content Determination of Active Ingredients in Lonicera Japonica Flos by Fourier Transform Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 467-473.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2024)02-0467-07 或 https://www.gpxygpfx.com/CN/Y2024/V44/I02/467

[1] WU Jiao, WANG Cong, YU Hai-chuan, et al(吴娇，王聪，于海川，等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2019, 25(4): 225.
[2] ZHENG Yi-ling, OUYANG-Yong, MEI Quan-xi, et al(郑依玲，欧阳勇，梅全喜，等). Asia-Pacific Traditional Medicine(亚太传统医药), 2021, 17(7): 180.
[3] LIU Tian-liang, DONG Cheng-ming, GAO Qi-guo, et al(刘天亮，董诚明，高启国，等). Lishizhen Medicine and Materia Medica Research(时珍国医国药), 2022, 33(4): 951.
[4] WANG Xin-yuan, DONG Cheng-ming, YANG Lin-lin, et al(王鑫源，董诚明，杨林林，等). Chinese Archives of Traditional Chinese Medicine(中华中医药学刊), 2022, 40(8): 54，265.
[5] CHEN Qian-feng, HOU Peng, LIU Qiao, et al(陈前锋，侯鹏，刘巧，等). Journal of Southwest University(Natural Science Edition)[西南大学学报(自然科学版)], 2016, 38(6): 188.
[6] ZOU Shu-han, XU Wen-fen, HE Shun-zhi(邹淑涵，徐文芬，何顺志). Guizhou Science(贵州科学), 2015, 33(5): 65.
[7] YANG Ke, XUE Shu-juan, WANG Li-li, et al(杨珂，薛淑娟，王利丽，等). Chinese Archives of Traditional Chinese Medicine(中华中医药学刊), 2021, 39(5): 147.
[8] QI Da-ming, DONG Cheng-ming, LIU Tian-liang, et al(齐大明，董诚明，刘天亮，等). Chinese Traditional and Herbal Drugs(中草药), 2023, 54(6): 1946.
[9] YU Mei, LI Shang-ke, YANG Fei, et al(余梅，李尚科，杨菲，等). Journal of Instrumental Analysis(分析测试学报)，2021, 40(1): 65.
[10] BAI Yan, LI Wen-xia, WANG Xing, et al(白雁，李雯霞，王星，等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2010, 16(13): 45.
[11] FANG Bao-xia, GUN Dai-fen, LI Xiang, et al(方宝霞，滚代芬，李湘，等). China Pharmacist(中国药师), 2021, 24(10): 1894.
[12] BAI Yan, FAN Ke-feng, LI Jun, et al(白雁，樊克锋，李军，等). China Journal of Chinese Materia Medica(中国中药杂志), 2005，(7): 504.
[13] XUE Shu-juan, CHEN Sui-qing, WANG Li-li, et al(薛淑娟，陈随清，王利丽，等). China Journal of Traditional Chinese Medicine and Pharmacy(中华中医药杂志), 2017, 32(5): 2055.
[14] LU Yong, SUN Dong-mei, WANG Luo-lin, et al(卢泳，孙冬梅，王洛临，等). Journal of Guangdong Pharmaceutical University(广东药科大学学报), 2019, 35(4): 517.
[15] LEI Xiao-qing, WANG Xiu-li, LI Geng, et al(雷晓晴，王秀丽，李耿，等). Chinese Traditional and Herbal Drugs(中草药), 2019, 50(16): 3947.
[16] XIE Yu-jing, ZHANG Jia-nan, ZHU Dong-ning, et al(解育静，张家楠，朱冬宁，等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2020, 26(2): 119.
[17] ZHANG Jiang-shan, ZHANG Zhen-ling, YAN Meng-zhen, et al(张江山，张振凌，闫梦真，等). Chinese Archives of Traditional Chinese Medicine(中华中医药学刊), 2020, 38(8): 133.