采用高效液相色谱法,以丁烯基苯酞为内参物,建立川芎中1种酚酸类成分和5种苯酞类成分的单标多组分HPLC定量分析方法,并考察和验证该方法在川芎质量评价中应用的可行性和准确性。实验所用流动相为乙腈-0.2%甲酸水溶液,流速为1.0 m L·...采用高效液相色谱法,以丁烯基苯酞为内参物,建立川芎中1种酚酸类成分和5种苯酞类成分的单标多组分HPLC定量分析方法,并考察和验证该方法在川芎质量评价中应用的可行性和准确性。实验所用流动相为乙腈-0.2%甲酸水溶液,流速为1.0 m L·min-1,柱温30℃,检测波长分别为252 nm(阿魏酸、藁本内酯、丁烯基苯酞)和266 nm(洋川芎内酯I、洋川芎内酯A、阿魏酸松柏酯),进样量20μL,梯度洗脱。结果显示,单标多组分HPLC定量分析法计算所得值与外标法实测值之间无显著性差异,RSD值均小于5%;相对校正因子的耐用性较好,色谱条件改变后,相对校正因子变化的RSD值均小于5%。所建立的单标多组分HPLC定量分析法可准确地用于中药川芎的多指标同步质量控制。展开更多
The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. The...The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.展开更多
文摘采用高效液相色谱法,以丁烯基苯酞为内参物,建立川芎中1种酚酸类成分和5种苯酞类成分的单标多组分HPLC定量分析方法,并考察和验证该方法在川芎质量评价中应用的可行性和准确性。实验所用流动相为乙腈-0.2%甲酸水溶液,流速为1.0 m L·min-1,柱温30℃,检测波长分别为252 nm(阿魏酸、藁本内酯、丁烯基苯酞)和266 nm(洋川芎内酯I、洋川芎内酯A、阿魏酸松柏酯),进样量20μL,梯度洗脱。结果显示,单标多组分HPLC定量分析法计算所得值与外标法实测值之间无显著性差异,RSD值均小于5%;相对校正因子的耐用性较好,色谱条件改变后,相对校正因子变化的RSD值均小于5%。所建立的单标多组分HPLC定量分析法可准确地用于中药川芎的多指标同步质量控制。
文摘The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.
