摘要
目的为实现绞股蓝总皂苷(Gynostemma pentaphyllum saponins,GPS)色谱洗脱过程实时监测,保障纯化过程绞股蓝总皂苷质量一致性。方法采集色谱洗脱过程7批共计237个样本的拉曼光谱,将其中5批用于建模,2批用于外部测试,以总皂苷质量浓度、总固体量和人参皂苷Rb_(3)(Rb_(3))质量浓度为指标,采用高斯过程回归(Gaussian process regression,GPR)法建立定量模型,并将GPR模型与偏最小二乘回归及支持向量机回归定量模型进行性能对比。结果基于拉曼光谱技术结合GPR,建立了其洗脱过程的多指标定量校正模型。总皂苷质量浓度、总固体量和Rb_(3)质量浓度3个指标的GPR模型均具有更高的决定系数(R2),训练集R2均为1.00,验证集R2分别为0.953、0.986、0.939,以及更低的误差均方根(root mean square error,RMSE),训练集RMSE分别为70.4、224.0、31.6μg/mL,验证集RMSE分别为3.02、2.03、1.19 mg/mL。GPR模型在外部测试集的结果为总皂苷质量浓度、总固体量和Rb_(3)质量浓度预测R2分别达到0.947、0.954、0.837,RMSE分别为3.28、4.37、2.44 mg/mL;GPR模型能较好地反映总皂苷质量浓度和总固体量含量和变化趋势,但对Rb_(3)质量浓度的预测能力较弱。结论以总皂苷质量浓度和总固体量为指标,提出的基于拉曼光谱结合GPR建模的方法可实现绞股蓝总皂苷色谱洗脱过程的实时监测。
Objective In order to realize the real-time monitoring of chromatographic process of Gynostemma pentaphyllum saponins(GPS)and ensure the quality uniformity and batch consistency.Methods The Raman spectra of 237 samples collected in seven batches during the chromatographic process,of which five batches were used as modeling sets and two batches were used as external test sets.With total saponin concentration,total solids and ginsenoside Rb_(3) concentration as indexes,Gaussian process regression(GPR)method was used to establish the model,and the performance was compared with partial least squares and support vector machine regression quantitative models,and the method was applied to external test sets for validation.Results Multi-index quantitative correction models were established based on Raman spectroscopy combined with GPR.The results showed that the GPR models of the three indexes had higher coefficient of determination(R2)and lower root mean square error(RMSE).The R2 of the training sets were all 1.00,and the R2 of the verification sets were 0.953,0.986,and 0.939,respectively.The RMSE of the training sets were 70.4,224.0,31.6μg/mL,and the RMSE of the verification sets were 3.02,2.03,1.19 mg/mL,respectively.The results of external test sets showed that the prediction R2 of total saponin concentration,total solid content and ginsenoside Rb_(3) concentration were 0.947,0.954 and 0.837,respectively,and RMSE were 3.28,4.37 and 2.44 mg/mL,respectively.GPR model can predict the content and trend of total saponin and total solid well,but it is weak in predicting ginsenoside Rb_(3) concentration.Conclusion With total saponins concentration and total solids as indexes,this method can realize the real-time monitoring of the chromatographic process of GPS.
作者
谢佳丽
张胜
姜新宇
王青青
张建兵
瞿海斌
XIE Jia-li;ZHANG Sheng;JIANG Xin-yu;WANG Qing-qing;ZHANG Jian-bing;QU Hai-bin(Pharmaceutical Informatics Institute,College of Pharmaceutical Sciences,Zhejiang University,Hangzhou 310058,China;Hunan Huabaotong Pharmaceutical Co.,Ltd.,Changsha 410331,China;Wanbangde Pharmaceutical Group Co.,Ltd.,Taizhou 317599,China)
出处
《中草药》
CAS
CSCD
北大核心
2023年第12期3824-3833,共10页
Chinese Traditional and Herbal Drugs
基金
国家中医药管理局“组分中药与智能制药多学科交叉创新团队”(ZYYCXTD-D-2020002)。
关键词
绞股蓝
色谱洗脱
拉曼光谱
高斯过程回归
在线监测
质量一致性
总皂苷
人参皂苷RB3
偏最小二乘回归
支持向量机
误差均方根
gynostemma pentaphyllum
chromatographic process
raman spectrum
gaussian process regression
on line monitoring
quality conformance
total saponins
ginsenoside Rb3
partial least squares
support vector machine regression