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Main components determination and rapid geographical origins identification in Gentiana rigescens Franch. based on HPLC, 2DCOS images combined to ResNet

文献类型： 外文期刊

作者： Liu, Chunlu 1 ; Shen, Tao 3 ; Xu, Furong 2 ; Wang, Yuanzhong 1 ;

作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China

2.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China

3.Yuxi Normal Univ, Coll Chem Biol & Environm, Yuxi 653100, Yunnan, Peoples R China

关键词： Gentiana rigescens Franch.; Iridoid; Geographical origins identification; HPLC; FT-IR; 2DCOS images combined to ResNet

期刊名称：INDUSTRIAL CROPS AND PRODUCTS （ 影响因子：6.449； 五年影响因子：6.508 ）

ISSN： 0926-6690

年卷期： 2022 年 187 卷

页码：

收录情况： SCI

摘要： As an important resource in many prescriptions, the geographical origins of Gentiana rigescens Franch. influences its chemical characteristics, quality and price greatly. Hence, a simple and rapid method for the correct classification and identification of the geographical origins of G. rigescens is of significance. In this work, marker components of iridoids were measured by high performance liquid chromatography (HPLC) and were applied as a reference to characterize chemical profiles of samples from different geographical origins. The effects of climate factors on the content differences of G. rigescens were examined by correlation analysis. Afterward, a novel two-dimensional correlation spectroscopy (2DCOS) images acquired based on Fourier transform infrared (FT-IR) spectroscopy was proposed combined to deep learning to identify geographical origins of G. rigescens. Through analyzing the iridoid components of G. rigescens, which discovered that there were significant differences in its five marker components. In addition, the marker components of gentiopicroside based on Northwestern Yunnan (DXB) were higher, and the climate environment of low temperature, temperate, and high precipitation was more suitable for the cultivation and growth of G. rigescens. In the residual convolutional neural network (ResNet), the train set and test set accuracy of synchronous 2DCOS images for the feature bands (1800-400 cm-1) was 100%, and the external validation set of all samples was correctly identified. The results indicated the synchronous 2DCOS images of feature bands were suitable for the correct identification of the geographic origin of G. rigescens, and it reduced the amount of computation and time, and saved computing resources. This study provided a powerful and useful tool for the cultivation and geographical origins identification of G. rigescens.

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