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Discrimination and quantification analysis of Acorus calamus L. and Acorus tatarinowii Schott with near-infrared reflection spectroscopy

机译:近红外反射光谱法对cal蒲和t蒲的鉴别和定量分析

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A rapid near-infrared reflection (NIR) spectroscopy analysis method was developed for discrimination of the dried rhizome part of Acorus calamus L. and Acorus tatarinowii Schott, two kinds of traditional Chinese herbs that are sometimes mixed or used interchangeably, and the simultaneous determination of their main components ?2-asarone and ?±-asarone. The NIR spectra of 25 Acorus calamus L. samples and 25 Acorus tatarinowii Schott samples were collected in integrating-sphere diffused reflection mode and pre-processed with different methods. Principal component analysis (PCA) and discriminant partial least squares (DPLS) were applied to discriminate Acorus calamus L. from Acorus tatarinowii Schott, and the latter method proved better, more visual and effective. The quantitative models of ?2-asarone and ?±-asarone were developed using partial least squares regression (PLSR) as multivariate regression method with optimum spectral pre-processing method, wavenumber range and latent variables (LV) numbers, and the results from ultra performance liquid chromatography (UPLC) analysis were taken as reference values. The correlation coefficients of the quantitative models of ?2-asarone and ?±-asarone are all above 0.98 while the root mean square errors of prediction (RMSEP) are all below 0.6%, indicating that the models we established have good predictive ability. The results demonstrate that NIR spectroscopy could be used to solve analogous problems for the safety of clinical medication, and can also be applied in the medical industry for the quality control of Acorus calamus L. and Acorus tatarinowii Schott.
机译:建立了快速近红外反射(NIR)光谱分析方法,用于鉴别有时混合或互换使用的两种中草药herbs蒲和A蒲的干燥根茎部分,并同时测定它们的主要成分是α2-小茴香醚和α±小茴香醚。以积分球扩散反射模式收集了25个A蒲和25个A蒲的近红外光谱,并用不同的方法进行了预处理。应用主成分分析(PCA)和判别偏最小二乘(DPLS)来区分cal蒲(Acorus calamus L.)和A蒲(Acorus tatarinowii Schott),后一种方法证明是更好,更直观和有效的。利用偏最小二乘回归(PLSR)作为多元回归方法,采用最佳光谱预处理方法,波数范围和潜变量(LV)数,开发了α2-花生素和γ±花生素的定量模型,高效液相色谱(UPLC)分析作为参考值。 γ2-花生糖和γ±花生糖的定量模型的相关系数均在0.98以上,而预测均方根误差(RMSEP)均在0.6%以下,表明我们建立的模型具有良好的预测能力。结果表明,近红外光谱技术可用于解决临床药物安全性方面的类似问题,也可用于医疗行业中对cor蒲和t蒲的质量控制。

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