2020-2023 年期间采用 HPLC 和 UPLC(或 UHPLC)分析植物大麻素的进展情况。

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-07-01 Epub Date: 2024-06-04 DOI:10.1002/pca.3374
Lutfun Nahar, Phanuphong Chaiwut, Sarita Sangthong, Tinnakorn Theansungnoen, Satyajit D Sarker
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引用次数: 0

摘要

简介:与大麻素受体结合的有机分子被称为大麻素。高效液相色谱法(HPLC)和超高效液相色谱法(UPLC,又称超高效液相色谱法,UHPLC)已成为检测和定量各种基质中植物大麻素的最广泛使用的分析工具。HPLC 和 UPLC(或 UHPLC)通常与紫外(UV)、光电二极管阵列(PDA)或质谱(MS)检测器联用:对 2020 年 1 月至 2023 年 12 月期间发表的有关应用 HPLC 和 UPLC(或 UHPLC)方法分析植物大麻素的文献进行批判性评估:利用 Web of Science、PubMed 和 Google Scholar 以及包括相关书籍在内的出版资料进行了广泛的文献检索。在文献检索中使用了大麻素、大麻、大麻、印度大麻、C. sativa、大麻、分析、高效液相色谱、超高效液相色谱、超高效液相色谱、定量、定性和质量控制等各种组合作为关键词:结果:报告了几种基于 HPLC 和 UPLC(或 UHPLC)的植物大麻素分析方法。虽然基于 HPLC-UV 或 HPLC-PDA 的简单方法很常见,但也有报道使用 HPLC-MS、HPLC-MS/MS、UPLC(或 UHPLC)-PDA、UPLC(或 UHPLC)-MS 和 UPLC(或 UHPLC)-MS/MS。报告还提到了数学和计算模型在优化方案中的应用。预分析包括各种环境友好型提取方案:在过去 4 年中,HPLC 和 UPLC(或 UHPLC)仍然是分析不同基质中植物大麻素的主要分析工具。
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Progress in the analysis of phytocannabinoids by HPLC and UPLC (or UHPLC) during 2020-2023.

Introduction: Organic molecules that bind to cannabinoid receptors are known as cannabinoids. These molecules possess pharmacological properties similar to those produced by Cannabis sativa L. High-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC, also known as ultra-high-performance liquid chromatography, UHPLC) have become the most widely used analytical tools for detection and quantification of phytocannabinoids in various matrices. HPLC and UPLC (or UHPLC) are usually coupled to an ultraviolet (UV), photodiode array (PDA), or mass spectrometric (MS) detector.

Objective: To critically appraise the literature on the application of HPLC and UPLC (or UHPLC) methods for the analysis of phytocannabinoids published from January 2020 to December 2023.

Methodology: An extensive literature search was conducted using Web of Science, PubMed, and Google Scholar and published materials including relevant books. In various combinations, using cannabinoid in all combinations, cannabis, hemp, hashish, C. sativa, marijuana, analysis, HPLC, UHPLC, UPLC, and quantitative, qualitative, and quality control were used as the keywords for the literature search.

Results: Several HPLC- and UPLC (or UHPLC)-based methods for the analysis of phytocannabinoids were reported. While simple HPLC-UV or HPLC-PDA-based methods were common, the use of HPLC-MS, HPLC-MS/MS, UPLC (or UHPLC)-PDA, UPLC (or UHPLC)-MS, and UPLC (or UHPLC)-MS/MS was also reported. Applications of mathematical and computational models for optimization of protocols were noted. Pre-analyses included various environmentally friendly extraction protocols.

Conclusion: During the last 4 years, HPLC and UPLC (or UHPLC) remained the main analytical tools for phytocannabinoid analysis in different matrices.

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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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