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1.天津中医药大学 中医药研究院,天津 301617
2.天士力医药集团股份有限公司研究院 现代中药开发中心,天津 300410
3.天士力医药集团股份有限公司 现代中药创制全国重点实验室,天津 300410
宋兆辉,正高级工程师,研究方向:中药新药研发与质量控制,E-mail:songzh@tasly.com
张依倩,博士,高级工程师,研究方向:中药活性物质基础,E-mail:zhangyiqian@tasly.com
收稿:2024-12-25,
修回:2025-02-08,
录用:2025-03-17,
纸质出版:2025-10-15
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张文楠,石星,葛诗雅,高玥皓,王雁雯,何毅,宋兆辉,张依倩.脊痛宁片醇沉上清液多指标成分近红外光谱快速定量模型建立[J].分析测试学报,2025,44(10):2054-2062.
ZHANG Wen-nan,SHI Xing,GE Shi-ya,GAO Yue-hao,WANG Yan-wen,HE Yi,SONG Zhao-hui,ZHANG Yi-qian.Building of a Fast Near-infrared Spectroscopy Quantitative Model for Jitong Ning Tablet Alcohol Precipitation Supernatant Multi-component Ingredients[J].Journal of Instrumental Analysis,2025,44(10):2054-2062.
张文楠,石星,葛诗雅,高玥皓,王雁雯,何毅,宋兆辉,张依倩.脊痛宁片醇沉上清液多指标成分近红外光谱快速定量模型建立[J].分析测试学报,2025,44(10):2054-2062. DOI: 10.12452/j.fxcsxb.24122539.
ZHANG Wen-nan,SHI Xing,GE Shi-ya,GAO Yue-hao,WANG Yan-wen,HE Yi,SONG Zhao-hui,ZHANG Yi-qian.Building of a Fast Near-infrared Spectroscopy Quantitative Model for Jitong Ning Tablet Alcohol Precipitation Supernatant Multi-component Ingredients[J].Journal of Instrumental Analysis,2025,44(10):2054-2062. DOI: 10.12452/j.fxcsxb.24122539.
基于近红外光谱(NIRS)技术结合偏最小二乘法(PLS)建立了脊痛宁片醇沉上清液多指标成分快速定量分析模型。采用超高效液相色谱测定98批脊痛宁片醇沉上清液中的葛根素、芍药苷、甘草酸含量;使用烘干法测定可溶性固体含量;比色法测定总糖含量,并运用PLS法建立 NIRS 与各项指标定量参考值之间的多元校正模型。以预测决定系数(
<math id="M1"><msubsup><mrow><mi>R</mi></mrow><mrow><mi mathvariant="normal">P</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msubsup></math>
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3.47133350
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3.13266683
)和预测均方根误差(RMSEP)对定量预测模型进行评价。结果显示,上清液中葛根素、芍药苷、甘草酸、可溶性固体、总糖NIRS定量模型的预测
R
2
P
分别为0.980 1、0.989 0、0.981 5、0.996 1、0.978 1;RMSEP
分别为0.011 2、0.029 9、0.014 1、0.062 7、0.809 0。12批外部独立验证集样品的平均相对偏差范围为2.52%~3.87%。所建立的近红外定量模型预测性能良好,简捷快速,可用于脊痛宁片醇沉上清液中间体的快速检测和质量控制。
The objective was to establish a rapid quantitative analysis model for multi-component ingredients in the alcohol precipitation supernatant of Jitong Ning Tablets(JTNT) using near-infrared spectroscopy(NIRS) combined with partial least squares(PLS) method. Ninety-eight batches of JTNT tablet alcohol precipitation supernatant were analyzed for puerarin,paeoniflorin,and glycyrrhizic acid content using ultra-high performance liquid chromatography,soluble solid content using drying method,and total sugar content using colorimetric method. Then,a multivariate calibration model was established between NIRS and quantitative reference values of each indicators using PLS method. The quantitative prediction models were evaluated by prediction determination coefficient(
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4.06400013
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3.64066648
) and root mean square error of prediction(RMSEP). The NIRS quantitative models for puerarin,paeoniflorin,glycyrrhizic acid,soluble solid,and total sugar in the supernatant had prediction
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values of 0.980 1,0.989 0,0.981 5,0.996 1 and 0.978 1,respectively;and RMSEP values of 0.011 2,0.029 9,0.014 1,0.062 7 and 0.809 0,respectively. The average relative deviations of 12 batches of external independent validation set samples ranged from 2.52% to 3.87%. The established near-infrared quantitative model exhibited good predictive performance,simplicity,and rapidity,and can be used for rapid detection and quality control of intermediates in the alcohol precipitation supernatant of JTNT tablets.
Tian D M , Zhao H D , Cao J , Zhang S H , Wang W Q , Tang X Y , Dai Y , Zhou W Y , Zhang L H , Han Y Y , Tang J S , Tian J F , Yao X S , Song Z H , Ma X H , He Y . J. Pharm. Biomed. Anal. , 2023 , 227 : 115271 .
Gao D , Wang B J , Pei Y , Bai X , He Y , Song Z H . Chin . Tradit. Herb. Drugs (高迪,王保军,裴玉,白雪,何毅,宋兆辉.中草药), 2017 , 48 ( 4 ): 668 - 672 .
Xiong L , Du X , Chen B Z , Wang Y Y , He X L . Chin . J. Exp. Tradit. Med. Formulae (熊丽,杜肖,陈宝忠,王炎焱,贺晓丽.中国实验方剂学杂志), 2017 , 23 ( 1 ): 140 - 145 .
Wang Y Y , Huang F , Hao A Z . Chin. J. Inf. Tradit. Chin. Med. (王炎焱 , 黄烽 , 郝爱真 . 中国中医药信息杂志) , 2006 ,( 4 ): 9 - 11 .
Yang M . China Food Drug Admin . (杨明. 中国食品药品监管) , 2019 ,( 7 ): 87 - 90 .
Shang Y , Jiang M L , Shao F , Yang M , Tang F R , Yu M Y , Wu J M . Lishizhen Med . Mater. Med. Res. (尚悦,蒋美林,邵峰,杨明,唐芳瑞,俞梦莹,吴继梅. 时珍国医国药), 2020 , 31 ( 5 ): 1206 - 1208 .
Jiang M L . The Critical Parammeters of Shuanghuanglian Preparation on the Effect of Ethanol Precipitation Based on Floc Micromorphology . Nanchang : Jiangxi University of Traditional Chinese Medicine (蒋美林. 基于絮体微观形态探讨双黄连制剂醇沉关键工艺参数对醇沉效果影响分析研究. 南昌:江西中医药大学), 2020 .
Xu B . Research on the Methodology for Optimizing the Entire Production Process of Traditional Chinese Medicine Preparations . Beijing : Beijing University of Traditional Chinese Medicine (徐冰. 中药制剂生产过程全程优化方法学研究.北京:北京中医药大学), 2013 .
Liu P , Zhao J C , Li R G . Guid . J. Tradit. Chin. Med. Pharm. (刘平,赵俊超,李日光.中医药导报), 2023 , 29 ( 8 ): 84 - 88 .
Wu G Q , Li Z G , Yang J Y , Wang Z D , Zhang Y , Lin Y H . Chin . J. Integr. Med. Cardio-Cerebrovasc. Dis. (吴国庆,李志刚,杨佳一,汪子栋,张洋,林禹宏.中西医结合心脑血管病杂志), 2023 , 21 ( 10 ): 1807 - 1810 .
Xiao X , Li C Y , Liu X L , Xue J T . J. Xinxiang Med. Univ. (肖先,李春燕,刘晓龙,薛金涛. 新乡医学院学报), 2023 , 40 ( 3 ): 280 - 285 .
Chen P , Yang J , Chu X L , Li J Y , Xu Y P , Liu D . Chin. J. Anal. Chem. (陈瀑,杨健,褚小立,李敬岩,许育鹏,刘丹. 分析化学), 2024 , 52 ( 9 ): 1213 - 1224 .
Beć K B , Grabska J , Huck C W . Molecules(basel,Switzerland) , 2020 , 25 ( 12 ): 2948 .
Luo X , Feng D , Zang L Y , Sun J . Chin. Tradit. Patent Med. (罗西,冯丹,臧利艳,孙菁.中成药), 2025 , 47 ( 1 ): 297 - 305 .
Long R L , Li D , Li P P , Hu N , Feng D , Sun J . Food Ferment. Ind. (龙若兰,李朵,李佩佩,胡娜,冯丹,孙菁. 食品与发酵工业), 2023 , 49 ( 20 ): 274 - 279 .
Lai C J S , Zhou R R , Yu Y , Zeng W , Hu M H , Fan L D , Chen L , Qiu Z D , Song C , Zhang S H , Guo L P , Huang L Q . China J. Chin. Mater. Med. (赖长江生,周融融,余意,曾雯,胡明华,范罗嫡,陈林,邱子栋,宋川,张水寒,郭兰萍,黄璐琦.中国中药杂志), 2018 , 43 ( 16 ): 3243 - 3248 .
Si Y T , Zhang X , Zhang Y C , Zhang J Y , Wang J , Chen Y , Liu X S , Wu Y J . Acta Pharm. Sin. (斯乐婷,张欣,张永超,张江艳,王钧,陈勇,刘雪松,吴永江. 药学学报), 2025 , 60 ( 2 ): 471 - 478 .
Wang X L , Niu L Q , Zhang B H . Chin . J. Pharm. Anal. (王小亮,牛龙青,张秉华. 药物分析杂志), 2024 , 44 ( 11 ): 1923 - 1931 .
Ma X R , Wang B X , Zhao W S , Cong D G , Sun W , Xiong H S , Zhang S N . China J. Chin. Mater. Med. (马欣荣,王鐾璇,赵万顺,丛德刚,孙巍,熊皓舒,章顺楠.中国中药杂志), 2023 , 48 ( 21 ): 5701 - 5706 .
Amirvaresi A , Nikounezhad N , Amirahmadi M , Daraei B , Parastar H . Food Chem. , 2021 , 344 : 128647 .
Chen J B , Wang Y , Liu A X , Rong L X , Wang J J . J. Mol. Struct. , 2018 , 1155 : 681 - 686 .
National Pharmacopoeia Commission . China Pharmacopoeia:Part Ⅳ . Beijing : China Medical Science and Technology Press (中华人民共和国国家药典委员会.中国药典:四部.北京:中国医学科技出版社), 2020 : 114 .
Chen B , Zou X Y , Zhu W J . J. Jiangsu Univ. : Nat . Sci. Ed. (陈斌,邹贤勇,朱文静.江苏大学学报 : 自然科学版) , 2008 ,( 4 ): 277 - 279,292 .
Song J H . Near Infrared Spectroscopy Combined with Chemometrics for Rice Quality Detection . Zhenjiang : Jiangsu University of Science and Technology (宋嘉慧. 基于近红外光谱技术结合化学计量学的稻米品质检测研究.镇江:江苏科技大学), 2023 .
Diwu P Y , Bian X H , Wang Z F , Liu W . Spectrosc. Spectral Anal. (第五鹏瑶,卞希慧,王姿方,刘巍.光谱学与光谱分析), 2019 , 39 ( 9 ): 2800 - 2806 .
Du Z J , Tian W F , Tilley M , Wang D H , Zhang G R , Li Y H . Compr. Rev. Food Sci. Food Saf. , 2022 , 21 ( 3 ): 2956 - 3009 .
An S Y , Zhang L , Shang X Z , Yue H S , Liu W Y , Ju A C . Spectrosc . Spectral Anal. (安思宇,张磊,尚献召,岳洪水,柳文媛,鞠爱春.光谱学与光谱分析), 2021 , 41 ( 1 ): 206 - 209 .
Chen B , Zheng E R , Guo T . Spectrosc . Spectral Anal. (陈蓓,郑恩让,郭拓.光谱学与光谱分析), 2021 , 41 ( 8 ): 2443 - 2449 .
Song J H , Yu Y , Wang R N , Chen M T , Li Z M , He X M , Ren Z Y , Dong H . Microchem. J. , 2024 , 199 : 110032 .
Xu Q L , Guo L Y , Du K , Shan B M , Zhang F K . J. Instrum. Anal. (徐啟蕾,郭鲁钰,杜康,单宝明,张方坤.分析测试学报), 2022 , 41 ( 8 ): 1229 - 1234,1241 .
Haruna S A , Li H H , Wei W Y , Geng W H , Adade S Y S S , Zareef M , Vane Ngouana M A , Chen Q S . Anal. Methods , 2022 , 14 ( 31 ): 2989 - 2999 .
Viviana C , Giacomo B , Fabio G , Roberto T , Davide B . Chemom. Intell. Lab. Syst. , 2021 , 213 : 104313 .
Han H F , Zhang L , Zhang Y , Li W L , Qu H B . Chin . Tradit. Herb. Drugs (韩海帆,张路,张淹,李文龙,瞿海斌.中草药), 2013 , 44 ( 17 ): 2397 - 2403 .
Chen Y J , Yang O , Sampat C , Bhalode P , Ramachandran R , Ierapetritou M . Processes , 2020 , 8 ( 9 ): 1088 .
Cai J R , Huang C J , Ma L X , Zhai L X , Guo Z M . Spectrosc. Spectral Anal. (蔡健荣,黄楚钧,马立鑫,翟利祥,郭志明.光谱学与光谱分析), 2023 , 43 ( 9 ): 2792 - 2798 .
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