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论文摘要

紫外-可见漫反射光谱-支持向量回归法快速测定西咪替丁片剂含量

DeterminationofCimetidineTabletsbyUltraviolet-visibleDiffuseReflectionSpectrumandSupportVectorRegression

作者:梁冰(四川大学 化学工程学院);冯宇艳(四川大学 化学工程学院);宋航(四川大学 化学工程学院);姚舜(四川大学 化学工程学院);徐凯林(四川大学 化学工程学院);邹华煜(四川大学 化学工程学院)

Author:Liang Bing(CollegeofChemicalEng.,SichuanUniv.);Feng Yuyan(CollegeofChemicalEng.,SichuanUniv.);Song Hang(CollegeofChemicalEng.,SichuanUniv.);Sun Shun(CollegeofChemicalEng.,SichuanUniv.);Xu Kailin(CollegeofChemicalEng.,SichuanUniv.);Zou Huayu(CollegeofChemicalEng.,SichuanUniv.)

收稿日期:2014-01-15          年卷(期)页码:2014,46(3):182-186

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:紫外可见漫反射光谱;支持向量回归法;西咪替丁片剂

Key words:ultraviolet-visible diffuse reflection spectrum;support vector regression;cimetidine tablet

基金项目:

中文摘要

采用紫外-可见漫反射光谱-支持向量回归法(UV-Vis DRS-SVR)建立了快速、无损测定西咪替丁片剂的定量方法。人工配制58个西咪替丁片剂粉末样,分为校正集和预测集。通过光谱预处理、异常值剔除和调整RBF核函数参数g、正则化系数C和不敏感损失函数p来优化模型,最终采用原始光谱,在220.17~980.80 nm,参数g=0.02,C=20,p=0.03的条件下,以支持向量回归法(SVR)建立模型,校正集和预测集的决定系数(R2)分别为0.978 2,0.987 1,对5个盲样和15个批次市售西咪替丁片剂的预测均方根差(RMSEP)分别为0.036 0,0.044 8,比偏最小二乘法(PLS)所建模型的预测精度更高,效果更好。研究表明,UV-Vis DRS-SVR用于快速、非破坏性测定药物制剂中的活性成分的含量是可行的,有望用于制剂生产过程中的质量控制。

英文摘要

A method of rapid and nondestructive determination of cimetidine tablets was established by ultraviolet-visible diffuse reflection spectrum coupled with support vector regression (UV-Vis DRS-SVR).By spectral pretreatment,outlier detection and the optimization of parameter of RBF kernel functiong,regularization coefficientC,insensitive loss functionp,SVR model was established in range of 220.17~980.80 nm withg= 0.02,C= 20,p=0.03.The determination coefficients (R2) of calibration set and prediction set were 0.978 2 and 0.987 1,respectively. Compared with PLS model,the SVR model had better prediction performance for 5 blind samples and 15 commercial cimetidine tablet samples with a RMSEP of 0.036 0 and 0.044 8,respectively.The results showed that UV-Vis DRS-SVR is feasible to apply for quantitative analysis of active compound in pharmaceutical formulations, being expected to be used for the quality control in the process of production of pharmaceutical formulations.

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