Quality Assessment of Medicinal Plants via Chemometric Exploration of Quantitative NMR Data: A Review

Compounds Pub Date : 2022-06-13 DOI:10.3390/compounds2020012
A. Rebiai, B. B. Seghir, H. Hemmami, S. Zeghoud, I. B. Amor, I. Kouadri, M. Messaoudi, Ardalan Pasdaran, G. Caruso, Somesh Sharma, M. Atanassova, P. Pohl
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引用次数: 2

Abstract

Since ancient times, herbal medicines (HM) have played a vital role in worldwide healthcare systems. It is therefore critical that a thorough evaluation of the quality and control of its complicated chemical makeup be conducted, in order to ensure its efficacy and safety. The notion of HM chemical prints, which aim to acquire a full characterization of compound chemical matrices, has become one of the most persuasive techniques for HM quality evaluation during the last few decades. The link between NMR and chemometrics is discussed in this article. The chemometric latent variable technique has been shown to be extremely valuable in inductive studies of biological systems as well as in solving industrial challenges. The results of unsupervised data exploration utilizing main component analysis as well as the multivariate curve resolution, were various. On the other hand, many contemporary NMR applications in metabolomics and quality control are based on supervised regression or classification analyses.
基于核磁共振定量数据化学计量学的药用植物质量评价综述
自古以来,草药(HM)在全球医疗保健系统中发挥着至关重要的作用。因此,至关重要的是,必须对其复杂的化学组成进行彻底的质量评价和控制,以确保其有效性和安全性。在过去的几十年里,HM化学打印的概念,旨在获得化合物化学矩阵的完整表征,已成为HM质量评估中最有说服力的技术之一。本文讨论了核磁共振与化学计量学之间的联系。化学计量潜变量技术在生物系统的归纳研究和解决工业难题方面具有极其重要的价值。利用主成分分析和多变量曲线分辨率的无监督数据探索的结果是不同的。另一方面,许多当代核磁共振在代谢组学和质量控制方面的应用都是基于监督回归或分类分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.30
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0.00%
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