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Original plant traceability of Dendrobium species using multi-spectroscopy fusion and mathematical models

机译:利用多光谱融合和数学模型对铁皮石species种的原始植物溯源性

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摘要

Dendrobium is the largest genus of orchids most of which have excellent medicinal properties. Fresh stems of some species have been consumed in daily life by Asians for thousands of years. However, there are differences in flavour and clinical efficacy among different species. Therefore, it is necessary for a detector to establish an effective and rapid method controlling botanical origins of these crude materials. In our study, three spectroscopies including mid-infrared (MIR) (transmission and reflection mode) and near-infrared (NIR) spectra were investigated for authentication of 12 Dendrobium species. Generally, two fusion strategies, reflection MIR and NIR spectra, were combined with three mathematical models (random forest, support vector machine with grid search (SVM-GS) and partial least-squares discrimination analysis (PLS-DA)) for discrimination analysis. In conclusion, a low-level fusion strategy comprising two spectra after pretreated by the second derivative and multiplicative scatter correction was recommended for discrimination analysis because of its excellent performance in three models. Compared with MIR spectra, NIR spectra were more responsible for the discrimination according to a bi-plot analysis of PLS-DA. Moreover, SVM-GS and PLS-DA were suitable for accurate discrimination (100% accuracy rates) of calibration and validation sets. The protocol combined with low-level fusion strategy and chemometrics provides a rapid and effective reference for control of botanical origins in crude Dendrobium materials.
机译:石end兰是兰花的最大属,其中大多数具有优良的药用特性。亚洲人在日常生活中已经食用了某些物种的新鲜茎数千年来。但是,不同物种之间的风味和临床功效存在差异。因此,对于检测器而言,有必要建立一种有效且快速的方法来控制这些原油原料的植物来源。在我们的研究中,研究了三种光谱学,包括中红外(MIR)(透射和反射模式)和近红外(NIR)光谱,以鉴定12种石end。通常,将两种融合策略(反射MIR和NIR光谱)与三种数学模型(随机森林,带网格搜索的支持向量机(SVM-GS)和偏最小二乘判别分析(PLS-DA))相结合进行判别分析。总之,由于其在三个模型中的出色性能,建议将包含两个光谱的低水平融合策略进行二阶导数预处理和乘性散射校正用于鉴别分析。与MIR光谱相比,根据PLS-DA的双图分析,NIR光谱更能区分。此外,SVM-GS和PLS-DA适用于校准和验证集的准确区分(100%准确率)。该协议与低级融合策略和化学计量学相结合,为控制粗铁皮石materials材料中的植物来源提供了快速有效的参考。

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