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High-Sensitivity Determination of Nutrient Elements in Panax notoginseng by Laser-induced Breakdown Spectroscopy and Chemometric Methods

机译:激光诱导击穿光谱和化学计量学方法高灵敏度测定三七中的营养元素

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摘要

High-accuracy and fast detection of nutritive elements in traditional Chinese medicine Panax notoginseng (PN) is beneficial for providing useful assessment of the healthy alimentation and pharmaceutical value of PN herbs. Laser-induced breakdown spectroscopy (LIBS) was applied for high-accuracy and fast quantitative detection of six nutritive elements in PN samples from eight producing areas. More than 20,000 LIBS spectral variables were obtained to show elemental differences in PN samples. Univariate and multivariate calibrations were used to analyze the quantitative relationship between spectral variables and elements. Multivariate calibration based on full spectra and selected variables by the least absolute shrinkage and selection operator (Lasso) weights was used to compare the prediction ability of the partial least-squares regression (PLS), least-squares support vector machines (LS-SVM), and Lasso models. More than 90 emission lines for elements in PN were found and located. Univariate analysis was negatively interfered by matrix effects. For potassium, calcium, magnesium, zinc, and boron, LS-SVM models based on the selected variables obtained the best prediction performance with Rp values of 0.9546, 0.9176, 0.9412, 0.9665, and 0.9569 and root mean squared error of prediction (RMSEP) of 0.7704 mg/g, 0.0712 mg/g, 0.1000 mg/g, 0.0012 mg/g, and 0.0008 mg/g, respectively. For iron, the Lasso model based on full spectra obtained the best result with an Rp value of 0.9348 and RMSEP of 0.0726 mg/g. The results indicated that the LIBS technique coupled with proper multivariate chemometrics could be an accurate and fast method in the determination of PN nutritive elements for traditional Chinese medicine management and pharmaceutical analysis.
机译:三七(PN)的高精度和快速检测营养元素有助于为PN草药的健康营养和药用价值提供有用的评估。激光诱导击穿光谱法(LIBS)用于来自八个生产区域的PN样品中的六种营养元素的高精度和快速定量检测。获得了超过20,000个LIBS光谱变量,以显示PN样品中的元素差异。使用单变量和多变量校准来分析光谱变量和元素之间的定量关系。基于全光谱和通过最小绝对收缩和选择算子(Lasso)权重选择的变量的多元校准用于比较偏最小二乘回归(PLS),最小二乘支持向量机(LS-SVM)的预测能力和套索模型。找到并找到了PN中90多个元素的发射线。单变量分析受到基质效应的不利影响。对于钾,钙,镁,锌和硼,基于所选变量的LS-SVM模型获得了最佳预测性能,Rp值为0.9546、0.9176、0.9412、0.9665和0.9569,预测均方根误差(RMSEP)分别为0.7704 mg / g,0.0712 mg / g,0.1000 mg / g,0.0012 mg / g和0.0008 mg / g。对于铁,基于全光谱的拉索模型获得了最佳结果,Rp值为0.9348,RMSEP为0.0726 mg / g。结果表明,LIBS技术与适当的多元化学计量学结合可以为中药管理和药物分析中的PN营养元素的测定提供一种准确而快速的方法。

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