Characterization of Chemical Information and Content Prediction of Dendrobium officinale Based on ATR-FTIR

文献类型： 外文期刊

第一作者： Li, Peiyuan

作者： Li, Peiyuan;Yang, Shaobing;Zuo, Zhitian;Wang, Yuanzhong;Hu, Qiang;Shen, Tao

作者机构：

关键词： ATR-FTIR; chemical composition; content prediction; Dendrobium officinale; machine learning

期刊名称：JOURNAL OF CHEMOMETRICS （ 影响因子：2.1； 五年影响因子：2.3 ）

ISSN： 0886-9383

年卷期： 2024 年 38 卷 12 期

页码：

收录情况： SCI

摘要： Dendrobium officinale is a medicinal and food plant with high commercial and medicinal value. Yunnan is known as China's "plant kingdom," and although the climatic conditions are favorable, the large vertical climatic differences have led to a large difference in the quality of dendrobium from different origins. The analysis of quality differences between several origins with large ecological advantages has not been reported yet. Therefore, the aim of this study is to compare these regions in terms of both morphology and chemical composition and to analyze the variation of their chemical composition in spectral information. The PLS-DA, SVM, and PLSR models were developed to qualitatively and quantitatively evaluate Dendrobium from different production areas. The results show that the Menghai production area was superior to other production areas in terms of phenotypic morphology, quality, and yield. Within the appropriate range, the higher the specific absorbance, the higher the quercetin content.

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