DOSY-Assisted Multicomponent Characterization Enables Classification of Precious Chinese Materia Medica and Identification of Adulteration: A Case Study of Calculus Bovis.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Phytochemical Analysis Pub Date : 2025-07-01 Epub Date: 2025-03-04 DOI:10.1002/pca.3522
Shanghong Li, Cuifen Fang, Sinan Wang, Hsiuting Chang, Cheng Zheng, Yi Wang, Yu Tang
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引用次数: 0

Abstract

Introduction: Despite significant advances in analytical methods, the quality evaluation and adulteration detection of chemically complex traditional Chinese medicines continues to be a major challenge.

Objectives: We developed a custom diffusion-ordered spectroscopy (DOSY) approach for the qualitative analysis of precious Calculus Bovis (CB) and detection of its adulterants.

Methods: Using Artificial Calculus Bovis as an example, we focused on the effects of NMR spectrometers, solvent viscosity, matrices, matrix concentration, and temperature on separation efficiency of DOSY. The optimal conditions also successfully applied for the identification of Natural Calculus Bovis, Calculus Bovis Sativus, and adulterated samples. The accuracy of DOSY was verified by 13C NMR.

Results: Via inspection of NMR spectrometers, solvents, matrices, matrix concentration, and temperature, the optimal conditions for DOSY experiments of CB were found to be using methanol-d4 as the solvent, adding 4 mg of polyvinyl pyrrolidone (PVP) as the matrix, and a temperature of 298 K. Under these conditions, five types of compounds, cholic acid (CA), deoxycholic acid (DCA), hyodeoxycholic acid (HDCA), taurine-conjugated bile acids (tBAs), and glycine-conjugated bile acids (gBAs), were unequivocally identified in three types of CB. 13C NMR validated the accuracy of the DOSY results. As for the sugar-adulterated CB, the signals of sugar could be clearly identified on the DOSY spectrum.

Conclusions: This study verified the feasibility of DOSY for the quality evaluation of CB and provided a reference point for future qualitative and quantitative analysis of other complex traditional Chinese medicines, as well as the characterization of adulterated samples.

DOSY 辅助多组分表征技术实现了珍贵中药材的分类和掺假鉴定:牛黄的案例研究。
导语:尽管分析方法取得了重大进展,但化学复杂中药的质量评价和掺假检测仍然是一个重大挑战。目的:建立了一种定制的扩散有序光谱(DOSY)方法,用于珍贵的牛微积分(CB)的定性分析和掺假检测。方法:以人工牛微积分为例,研究了核磁共振谱仪、溶剂粘度、基质、基质浓度、温度等因素对DOSY分离效率的影响。该最佳条件也可用于天然牛演子、牛演子及掺假样品的鉴别。用13C NMR验证了DOSY的准确性。结果:通过对核磁共振谱仪、溶剂、基质、基质浓度、温度等因素的考察,确定以甲醇-d4为溶剂,聚乙烯吡罗烷酮(PVP) 4 mg为基质,温度298 K的条件下,炭黑DOSY实验的最佳条件。在这些条件下,五种类型的化合物,胆酸(CA),去氧胆酸(DCA),羟基去氧胆酸(HDCA),牛磺酸偶联胆汁酸(tBAs),甘氨酸偶联胆汁酸(gBAs),明确地鉴定出三种类型的胆酸。13C核磁共振验证了DOSY结果的准确性。对于掺杂糖的CB,在DOSY谱上可以清晰地识别出糖的信号。结论:本研究验证了DOSY用于CB质量评价的可行性,为今后其他复方中药的定性、定量分析及掺假样品的表征提供参考点。
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
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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