近红外光谱结合遗传算法优化的极限学习机实现银杏叶纯化过程有效成分快速测定

近红外光谱技术(near infrared spectroscopy,NIRS)结合波段筛选方法及建模算法可以实现中药生产过程分析的快速、无损检测。该文针对银参通络胶囊关键工艺银杏叶大孔树脂纯化过程,实现对洗脱液中槲皮素、山柰酚和异鼠李素3种成分含量的快速测定。通过马氏距离算法剔除异常光谱,联合X-Y距离样本集划分(sample set partitioning based on joint X-Y distances,SPXY)方法划分数据集,基于协同区间偏最小二乘法(synergy interval partial least squares,siPLS)筛选的关键信息波段,在此基础上实施竞争自适应加权重采样方法(competitive adaptive reweighted sampling,CARS)、连续投影算法(successive projections algorithm,SPA)和蒙特卡洛无信息变量消除法(Monte Carlo uninformation variable elimination,MC-UVE)筛选波长以得到更少但更关键的变量数据,将其作为输入变量建立遗传算法优化的极限学习机(genetic algorithm joint extreme learning machine,GA-ELM)定量分析模型,并将模型性能与偏最小二乘回归(partial least squares regression,PLSR)方法建立的模型进行比较,结果表明siPLS-CARS-GA-ELM算法联用可实现以最少变量数达到最优的模型性能。槲皮素、山柰酚、异鼠李素的校正集相关系数Rc和验证集相关系数Rp均达到0.98以上,校正集误差均方根(root mean square error of calibration,RMSEC)、验证集误差均方根(root mean square error of prediction,RMSEP)和验证集相对偏差(relative standard errors of prediction,RSEP)分别为0.0300,0.0292,8.88%;0.0414,0.0348,8.46%;0.0293,0.0271,10.10%,相较于传统PLSR方法,所建立GA-ELM模型性能有较大提升,证明NIRS结合GA-ELM方法实现中药有效成分快速测定具有很大潜力。

Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine

Near-infrared spectroscopy(NIRS)combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM)production process.This paper focused on the ginkgo leaf macroporous resin purification process,which is the key technology of Yinshen Tongluo Capsules,in order to achieve the rapid determination of quercetin,kaempferol and isorhamnetin in effluent.The abnormal spectrum was eliminated by Mahalanobis distance algorithm,and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY).The key information bands were selected by synergy interval partial least squares(siPLS);based on that,competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA)and Monte Carlo uninformative variable(MC-UVE)were used to select wavelengths to obtain less but more critical variable data.With selected key variables as input,the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM)algorithm.The performance of the model was compared with that of partial least squares regression(PLSR).The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables.The calibration set correlation coefficient Rc and the validation set correlation coefficient Rp of quercetin,kaempferol and isorhamnetin were all above 0.98.The root mean square error of calibration(RMSEC),the root mean square error of prediction(RMSEP)and the relative standard errors of prediction(RSEP)were 0.0300,0.0292 and 8.88%,0.0414,0.0348 and 8.46%,0.0293,0.0271 and 10.10%,respectively.Compared with the PLSR me-thod,the performance of the GA-ELM model was greatly improved,which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.