Recent Advances in Analytical Methods for Aconitine Alkaloid in Natural Products: A Systematic Review.

IF 2.6 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Phytochemical Analysis Pub Date : 2025-07-01 DOI:10.1002/pca.70003

Latif Ahmad, Minxia Fan, Juhar Zemede, Andrew J Semotiuk, Guilin Chen, Guangwan Hu

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引用次数: 0

Abstract

Introduction: Aconitine, the main toxic and active compound in Aconitum species, is a key component of Traditional Chinese Medicine (TCM), valued for its anti-inflammatory, cardiotonic, and analgesic properties. However, its potent neurotoxicity and cardiotoxicity, potentially leading to fatal outcomes, necessitate careful evaluation and management.

Objectives: Accurately identifying aconitine in natural products is one of the hot issues of international concern regarding the safety of TCM. This review systematically evaluates the current scientific literature to assess advancements in analytical methodologies for identifying and quantifying aconitine.

Methods: This review systematically evaluates literature from 2000 to 2025 regarding analytical methods for aconitine determination, utilizing databases such as PubMed, ScienceDirect, and Google Scholar.

Results: From 2237 identified articles, 90 were evaluated. This review highlights the advantages of LC-MS, DESI-MSI, and ambient MS techniques for identifying and quantifying aconitine, emphasizing their rapid analysis, minimal sample preparation, and cost-effectiveness. These advanced methods offer significant potential for reliable applications in various fields. DESI-MSI and DART-MS enable solvent-free, rapid analysis in native conditions, whereas oEESI-MS offers high throughput, precise quantification, and reduced solvent/sample use. The modified methods, H-oEESI-MS, allow direct analysis of Aconitum herbal materials without pretreatment, demonstrating high efficiency.

Conclusions: This review identifies research gaps and future directions using advanced analytical methods for aconitine determination, aiming to enhance quality control, regulatory compliance, and product safety. The integration of artificial intelligence into aconitine detection and analysis in TCM presents transformative potential, promising enhanced accuracy, efficiency, and safety while stimulating innovation and guiding future research directions in analytical science.

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天然产物中乌头碱生物碱的分析方法研究进展

简介：乌头碱是乌头属植物的主要毒性和活性化合物，是中药的重要成分，具有抗炎、强心和镇痛的作用。然而，其强大的神经毒性和心脏毒性，可能导致致命的结果，需要仔细的评估和管理。目的：准确鉴定天然产物中的乌头碱是国际上关注的中药安全性热点问题之一。这篇综述系统地评估了目前的科学文献，以评估分析方法的进展，以识别和定量乌头碱。方法：利用PubMed、ScienceDirect、谷歌Scholar等数据库，对2000 ~ 2025年有关乌头碱分析方法的文献进行系统评价。结果：在2237篇鉴定文章中，90篇被评价。本文重点介绍了LC-MS、DESI-MSI和环境质谱技术在鉴定和定量乌头碱方面的优势，强调了它们的快速分析、最少的样品制备和成本效益。这些先进的方法为各个领域的可靠应用提供了巨大的潜力。DESI-MSI和DART-MS能够在自然条件下无溶剂快速分析，而oEESI-MS提供高通量，精确定量，减少溶剂/样品使用。改进后的H-oEESI-MS方法无需预处理即可直接分析乌头药材，效率高。结论：本综述明确了乌头碱先进分析方法的研究空白和未来发展方向，旨在加强乌头碱的质量控制、法规遵从性和产品安全性。人工智能在中药乌头碱检测和分析中的集成具有变革潜力，有望提高准确性、效率和安全性，同时刺激创新并指导未来分析科学的研究方向。

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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来源期刊

Phytochemical Analysis 生物-分析化学

CiteScore

6.00

自引率

6.10%

发文量

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.