摘要
目的 采用近红外光谱技术(NIR)结合TQ软件建立一种能快速测定天麻中6种有效成分含量的方法。方法 运用近红外漫反射光谱技术采集不同产地不同等级的天麻的NIR光谱,同时采用高效液相色谱(HPLC)法测定天麻中天麻素、对羟基苯甲醇、巴利森苷A、巴利森苷B、巴利森苷C和巴利森苷E 6种有效成分的含量,利用TQ软件中偏最小二乘法(PLS),结合收集的NIR光谱和有效成分含量对天麻中6种有效成分进行定量预测,同时分别建立6种有效成分定量模型,并对模型进行了验证。结果 天麻素、对羟基苯甲醇、巴利森苷A、巴利森苷B、巴利森苷C和巴利森苷E 6种有效性成分定量预测模型的校正集相关系数(R)分别为0.9841,0.9063,0.9858,0.9857,0.9852,0.9506;校正均方差(RMSEC)分别为0.0427%,0.0170%,0.0749%,0.0269%,0.0080%,0.136%;预测均方差(RMSEP)依次为0.0837%,0.0116%,0.138%,0.0699%,0.0145%,0.178%。结论 该研究建立天麻中6种指标成分的定量校正模型,模型稳定可靠,可用于对未知样品的预测。
Objective To establish an analytical method based on near-infrared spectroscopy(NIR)and TQ software to analyze 6 effective components of Gastrodiae rhizome quickly.Methods The NIR spectra of Gastrodiae rhizome samples with different grade levels and origins were collected by near infrared diffuse reflectance spectroscopy.Meanwhile,the contents of gastrodin,p-hydroxybe,parishin A,B,C,and E were determined through HPLC methods.Then the quantitative prediction and quantitative model of 6 effective components were proceeded successively based on the above NIR spectra and content values using Partial Least Squares(PLS)from TQ software,and the quantitative model was verified in the end.Results The correction set correlation coefficient(R)of the quantitative prediction models of Gastrodin,p-Hydroxybe,Parishin A,B,C and E were 0.9841,0.9063,0.9858,0.9857,0.9852,0.9506,respectively;The corrected mean variance(RMSEC)of them were 0.0427%,0.0170%,0.0749%,0.0269%,0.0080%,0.136%,respectively;The predicted mean variance(RMSEP)of them were 0.0837%,0.0116%,0.1380%,0.0699%,0.0145%,0.178%,respectively.Conclusion The quantitative correction model of 6 index components of Gastrodiaelata was established.The model was stable and reliable,and it can be used for the prediction of unknown samples.
作者
彭璐
钟淑梅
廖鹏程
范荫荫
胡昌强
覃冬玖
PENG Lu;ZHONG Shumei;LIAO Pengcheng;FAN Yinyin;HU Changqiang;QIN Dongjiu(Joinative Traditional Chinese Medicine Industry Co.Ltd,Wuhan 430050,China)
出处
《医药导报》
CAS
北大核心
2022年第6期858-862,共5页
Herald of Medicine
基金
国家重点研发计划中医药现代化研究专项(2018YFC1707004)
国家重点研发计划中医药现代化专项(2019YFC1711500)
湖北省科技重大专项(2020ACA019)。