Multi-information based on ATR-FTIR and FT-NIR for identification and evaluation for different parts and harvest time of Dendrobium officinale with chemometrics

化学计量学 傅里叶变换红外光谱 线性判别分析 偏最小二乘回归 数学 衰减全反射 收获时间 傅里叶变换 生物系统 模式识别(心理学) 环境科学 计算机科学 化学 人工智能 园艺 生物 统计 色谱法 光学 物理 数学分析
作者
Lian Li,Yanli Zhao,Zhimin Li,Yuanzhong Wang
出处
期刊:Microchemical Journal [Elsevier BV]
卷期号:178: 107430-107430 被引量:45
标识
DOI:10.1016/j.microc.2022.107430
摘要

• A fast method of ATR-FTIR had superiority than FT-NIR to discriminate the D. officinale . • The separate effect of different parts is better than harvest times with exploratory analysis. • 2240 2DCOS images were collected and identified different parts and harvest time. • The relationship between DMA in different parts and harvest times was investigated. 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 (FT-NIR), 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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