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Geographic Authentication of Eucommia ulmoides Leaves Using Multivariate Analysis and Preliminary Study on the Compositional Response to Environment

机译:<斜视> eucommia ulmoides 叶的地理认证使用多变量分析和对环境的组成应答的初步研究

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To explore the influences of different cultivated areas on the chemical profiles of Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies (low, mid, and high level) combined with two discrimination model algorithms (partial least squares discrimination analysis; random forest, RF). RF models constructed by high-level data fusion with different modes of different spectral data (Fourier transform near-infrared spectrum and attenuated total reflection Fourier transform mid-infrared spectrum) were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (&1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.
机译:为了探讨不同栽培区域对杜仲叶(EUL)的化学分布的影响,并迅速验证其地理起源,通过三个数据融合策略(低,中期和高水平)系统地研究了13个省份的187个样本。结合两个识别模型算法(部分最小二乘鉴别分析;随机森林,RF)。由具有不同光谱数据(傅里叶变换近红外频谱和衰减的全反射傅里叶变换中红外光谱)的高级数据融合构成的RF模型最适合识别来自不同地理起源的eULS。校准和验证集的精度分别为92.86%和93.44%。此外,系统地研究了气候参数的研究中的集群差异。可以从分层集群分析的聚类树图中找到一些有趣和新颖的信息。位于高纬度地区的新疆自治区(区域5)是所有样品收集区域的中间温度区中唯一的区域,其中样品无论在树图中的距离都属于单个班级。样品来自湖北省神农鸡林区(& 1200米)的相对较高的海拔,这是襄阳市样本的主要区别(78米)。因此,来自区域9的样品簇与来自其他区域的样品簇不同。结果将为来自特殊群集的这些样本进行进一步研究的参考。

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