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Rapid and simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy and chemometrics

机译:近红外光谱和化学测定学枢纽Rhizoma和PhellodendriChinensis皮质中快速和同时测定水分和小檗碱含量

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摘要

Coptidis Rhizoma (Chinese: Huanglian) and Phellodendri Chinensis Cortex (Chinese: Huangbo) are widely used Traditional Chinese Medicine, and often used in combination because of their similar pharmacological effects in clinical practice. However, the quality control methods of the two drugs are different and complicated, which is time consuming and laborious in practical application. In this paper, rapid and simultaneous determination of moisture and berberine in Coptidis Rhizoma (CR) and Phellodendri Chinensis Cortex (PC) was realized by near-infrared spectroscopy (NIRs) combined with global models. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) method were applied for variable selection. Principal component analysis (PCA) and partial least squares regression method (PLSR) were applied for qualitative and quantitative analysis, respectively. The characteristic variables of berberine showed similarity and consistency in distribution, providing basis for the global models. For moisture content, the global model had relative standard error of prediction set (RSEP) value of 3.04% and 2.53% for CR and PC, respectively. For berberine content, the global model had RSEP value of 5.41% and 3.97% for CR and PC, respectively. These results indicated the global models based on CARS-PLS method achieved satisfactory prediction for moisture and berberine content, improving the determination efficiency. Furthermore, the greater range and larger number of samples enhanced the reliance of the global model. The NIRs combined with global models could be a powerful tool for quality control of CR and PC.
机译:Coptidis Rhizoma(中文:黄连)和Phellodendri Chinensis Cortex(中文:黄博士)广泛使用中药,常用于其在临床实践中类似的药理学作用。然而,两种药物的质量控制方法是不同的和复杂的,在实际应用中是耗时和艰苦的。本文通过近红外光谱(NIRS)与全球模型结合实现了COPTIDIS Rhizoma(CR)和PhellodendriChinensis皮质(PC)的快速和同时测定水分和小檗碱。竞争自适应重新重量采样(CARS)和连续投影算法(SPA)方法应用于可变选择。主要成分分析(PCA)和局部最小二乘回归方法(PLSR)分别用于定性和定量分析。小檗碱的特征变量显示了分配的相似性和一致性,为全球模型提供依据。对于湿度含量,全局模型分别具有3.04%的预测集(RSEP)值的相对标准误差,分别为CR和PC的2.53%。对于小檗碱内容,分别为CR和PC具有5.41%和3.97%的RSEP值。这些结果表明了基于汽车的全球模型 - PLS方法对水分和小檗碱含量的令人满意的预测,提高了确定效率。此外,更大的范围和更大数量的样本增强了全局模型的依赖。 NIRS与全球模型相结合可能是CR和PC质量控制的强大工具。

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