Rapid mitragynine quantification and fingerprinting of products from Mitragyna speciosa Korth. leaf (Kratom) using high-performance thin-layer chromatography.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-08-28 DOI:10.1002/pca.3442
Toveelah Hayeema, Juraithip Wungsintaweekul
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Abstract

Introduction: Kratom (leaves from Mitragyna speciosa Korth.; Rubiaceae) is a herbal medicine known for its analgesic properties and psychoactive effects. Kratom in Thailand is currently legal; however, it is prohibited in some countries and considered a narcotic plant.

Objective: Our aim was to establish a reliable, simple, and rapid method for quantifying mitragynine in Kratom leaves and related products through a combination of high-performance thin-layer chromatography (HPTLC) and densitometry.

Methodology: A densitometric HPTLC method was developed and validated in terms of specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, precision, and robustness. The fingerprints of kratom leaves, Mitragyna spp., and related products were constructed.

Results: For HPTLC, samples were applied to silica gel 60 F254 plates, and the mobile phase comprised n-hexane, ethyl acetate, and triethylamine (1:1:0.15, v/v/v). Densitometric detection was carried out under ultraviolet light at a wavelength of 226 nm. The validated method exhibited a range of 14.31-143.10 μg/mL, yielding a correlation coefficient of 0.9993. Spiked recovery rates were within a range of 98.3%-100.9%, and the LOD and LOQ were 3.80 and 11.53 μg/mL, respectively. Kratom samples were analyzed with the developed method, and the correlation coefficient was 0.9641, compared to the high-performance liquid chromatography-diode-array detection (HPLC-DAD) method. The HPTLC fingerprints displayed a distinctive pattern, facilitating discrimination among different plant parts and Mitragyna spp.

Conclusion: The established method offers the advantages of simplicity, ease of use, and speed of analysis, serving as a practical alternative for mitragynine quantification in kratom leaf and its related products.

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利用高效薄层色谱法对桔梗叶(Mitragyna speciosa Korth.
简介:桔梗(Mitragyna speciosa Korth.的叶子;茜草科)是一种草药,因其镇痛特性和精神作用而闻名。桔梗目前在泰国是合法的,但在一些国家被禁止使用,并被视为一种麻醉植物:我们的目的是建立一种可靠、简单、快速的方法,通过结合使用高效薄层色谱法(HPTLC)和密度测量法,对桔梗叶和相关产品中的米曲宁进行定量分析:开发了一种密度计 HPTLC 方法,并在特异性、线性、检出限 (LOD)、定量限 (LOQ)、准确度、精密度和稳健性方面进行了验证。构建了桔梗叶、桔梗属植物和相关产品的指纹图谱:HPTLC 采用硅胶 60 F254 薄层板,流动相为正己烷、乙酸乙酯和三乙胺(1:1:0.15, v/v/v)。在波长为 226 nm 的紫外光下进行密度检测。验证方法的检测范围为 14.31-143.10 μg/mL,相关系数为 0.9993。加标回收率为98.3%-100.9%,最低检出限(LOD)和最低定量限(LOQ)分别为3.80和11.53 μg/mL。采用所开发的方法对桔梗样品进行分析,与高效液相色谱-二极管阵列检测法(HPLC-DAD)相比,相关系数为0.9641。HPTLC指纹图谱显示出独特的模式,有助于区分不同的植物部位和密陀僧属植物:所建立的方法具有简单、易用和分析速度快等优点,是对桔梗叶及其相关产品中的丝氨酸进行定量分析的实用替代方法。
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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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