Multi-information based on ATR-FTIR and FT-NIR for identification and evaluation for different parts and harvest time of Dendrobium officinale with chemometrics
文献类型： 外文期刊
作者： Li, Lian 1 ; Zhao, YanLi 2 ; Li, ZhiMin 2 ; Wang, YuanZhong 2 ;
作者机构： 1.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China
关键词： Dendrobium officinale; Spectrum analysis; Two-dimensional correlation spectrum; (2DCOS); Chemometrics; Analysis of dry matter accumulation; Quality evaluation
期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：5.304； 五年影响因子：4.723 ）
ISSN： 0026-265X
年卷期： 2022 年 178 卷
页码：
收录情况： SCI
摘要： Dendrobium officinale Kimura et Migo, plays an important role in foods, medicinal and health products and its leaves have a high-quality value for raw industrial material. Different parts and harvest time are the main factors causing to differences for its accumulation of active ingredients. This study attempts to evaluate and identify different parts and harvests time of D. officinale multi-platform information combined with chemometrics as a practical strategy. From all the results: (1) Compared with Fourier transform-near infrared spectroscopy (FTNIR), the models of partial least squares discriminant analysis and support vector machine had absolute advantages to discriminate this plant based on ATR-FTIR; (2) The results of exploratory analysis showed that the samples were gathered well according to different categories, and the recognition effect of different parts is better than that of different harvest time; (3) The synchronous two-dimensional correlation spectrum based on ATR-FTIR can well identify different parts; (4) Compared with the original spectral data, all models were superiority based on Savitzky-Golay, which is more suitable to identify for different parts of D. officinale; (5) The investigation resulted that the best harvest time is from November this year to January next year for stems. The characteristics of this method is a fast, nondestructive, and green method with widely applicability that can not only solve the problem of identification and lay the foundation for further research of medicinal and edible homologous plants, but also provides a theoretical basis for the harvesting time and quality evaluation.
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