利用 UPLC-Q-TOF-MS/MS 和 GC-MS 结合色度测量法研究生姜汁和姜汁加工厚朴皮的质量差异。

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Yufang Qi, Kewei Zhang, Yingtong Ren, Xingchen Fan, Jing Wang, Tulin Lu, Chunqin Mao
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引用次数: 0

摘要

简介厚朴作为传统中药材已有数千年的历史。在《中国药典》(2020 年版)中,厚朴有两种煎煮剂:生厚朴煎煮剂(RMOC)和姜汁厚朴煎煮剂(GMOC)。目前还没有系统地探讨RMOC和GMOC的质量差异:本研究旨在发现RMOC与GMOC的质量差异,并全面阐明姜汁在加工过程中的影响:方法:采用超高效液相色谱-四极杆飞行时间串联质谱法(UPLC-Q-TOF-MS/MS)和气相色谱-质谱法(GC-MS)研究RMOC和GMOC的非挥发性成分和挥发性成分;采用电子眼测量色度。同时,以木兰花皮水(WMOC)作为空白样品进行研究:结果:共鉴定出 155 种非挥发性物质和 72 种挥发性物质。在 RMOC 和 GMOC 之间,检测到 29 种独特的非挥发性化合物和 34 种独特的挥发性化合物,其中 23 种新化合物出现,5 种化合物因加工过程中添加姜汁而消失。12 种常见非挥发性化合物的强度和 4 种常见挥发性化合物的相对百分比含量在 RMOC 和 GMOC 之间存在显著差异。在对 RMOC、GMOC 和 WMOC 的色度测量中,发现了 14 种常见化合物的色值存在显著差异,并建立了其数学预测函数:结论:RMOC 和 GMOC 之间存在明显差异;GMOC 的加工机制将根据差异化合物进行进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on the quality difference between raw and ginger juice processed Magnoliae officinalis cortex by UPLC-Q-TOF-MS/MS and GC-MS coupled with color measurement.

Introduction: Magnoliae officinalis cortex (MOC) has been used for thousands of years as a traditional Chinese herb. In Chinese Pharmacopoeia (2020 edition), it has two types of decoction pieces, raw Magnoliae officinalis cortex (RMOC) and ginger juice processed Magnoliae officinalis cortex (GMOC). The quality difference between RMOC and GMOC has not been explored systemically.

Objective: This study aimed to discover the quality difference between RMOC and GMOC, and clarify the effect of ginger juice during processing comprehensively.

Methods: Ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS) and gas chromatography-mass spectrometry (GC-MS) were applied to study the non-volatile and volatile components of RMOC and GMOC; electronic eye was applied for color measurement. Meanwhile, water processed Magnoliae officinalis cortex (WMOC) was studied as the blank sample.

Results: There were 155 non-volatile and 72 volatile substances identified. Between RMOC and GMOC, 29 distinctive non-volatile and 34 distinctive volatile compounds were detected, among which 23 new compounds appeared and five compounds disappeared due to the addition of ginger juice during processing. The intensities of 12 common non-volatile compounds and the relative percentage contents of four common volatile compounds showed significant differences between RMOC and GMOC. In color measurement of RMOC, GMOC, and WMOC, 14 common compounds with significant differences were discovered related to their color values, and their mathematical prediction functions were built.

Conclusion: There were significant differences between RMOC and GMOC; the processing mechanism of GMOC would be carried out based on the differential compounds in further investigation.

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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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