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食品和天然健康产品(NHPs)中植物成分的核磁共振(NMR)和液相色谱-质谱联用(LC-MS)代谢物指纹图谱分析提取方法的优化

Optimization of Extraction Methods for NMR and LC-MS Metabolite Fingerprint Profiling of Botanical Ingredients in Food and Natural Health Products (NHPs).

作者信息

Vinayagam Varathan, Thirugnanasambandam Arunachalam, Ragupathy Subramanyam, Sneha Ragupathy, Newmaster Steven G

机构信息

College of Biological Science, University of Guelph, Guelph, ON N1G 2W1, Canada.

Biological and Life Sciences, Canadian Light Source, Saskatoon, SK S7N 2V3, Canada.

出版信息

Molecules. 2025 Aug 14;30(16):3379. doi: 10.3390/molecules30163379.

Abstract

Metabolite fingerprint profiling is a robust tool for verifying suppliers of authentic botanical ingredients. While comparative studies exist, few apply identical conditions across multiple species; this study utilized a cross-species comparison to identify versatile solvents despite biochemical variability. Multiple solvents were used for sample extraction prior to analysis by proton NMR and liquid chromatography-mass spectrometry (LC-MS) for multiple botanicals including , , , , , , , , and . Comparisons were normalized by total intensity; deuterated methanol aids NMR lock but is not required for LC-MS. Hierarchical clustering analysis (HCA) evaluated solvent efficacy. Methanol-deuterium oxide (1:1) was the most effective extraction method, yielding 155 NMR spectral metabolite variables for , while methanol (90% CHOH + 10% CDOD) produced 198 for and 167 for , with 11, 9, and 28 assigned metabolites, respectively. LC-MS detected 121 metabolites in in methanol as the most effective extraction method. Methanol (10% deuterated) is the most effective extraction method for comprehensive metabolite fingerprinting using NMR and LC-MS protocols because it provides the broadest metabolite coverage. This study advances fit-for-purpose methods to qualify suppliers of botanical ingredients in food and NHP quality control programs.

摘要

代谢物指纹图谱分析是验证正宗植物成分供应商的有力工具。虽然存在比较研究,但很少有研究在多个物种中应用相同的条件;本研究采用跨物种比较来确定通用溶剂,尽管存在生化变异性。在通过质子核磁共振(NMR)和液相色谱 - 质谱联用(LC - MS)分析包括[具体植物名称1]、[具体植物名称2]、[具体植物名称3]、[具体植物名称4]、[具体植物名称5]、[具体植物名称6]、[具体植物名称7]、[具体植物名称8]和[具体植物名称9]在内的多种植物之前,使用了多种溶剂进行样品提取。比较通过总强度进行归一化;氘代甲醇有助于NMR锁定,但LC - MS分析不需要。层次聚类分析(HCA)评估了溶剂效果。甲醇 - 氧化氘(1:1)是最有效的提取方法,对于[具体植物名称1]产生了155个NMR光谱代谢物变量,而甲醇(90% CH₃OH + 10% CD₃OD)对于[具体植物名称2]产生了198个,对于[具体植物名称3]产生了167个,分别有11、9和28个已鉴定代谢物。以甲醇作为最有效的提取方法时,LC - MS在[具体植物名称4]中检测到121种代谢物。甲醇(10%氘代)是使用NMR和LC - MS方案进行全面代谢物指纹图谱分析的最有效提取方法,因为它提供了最广泛的代谢物覆盖范围。本研究推进了适用于食品和天然健康产品(NHP)质量控制计划中植物成分供应商资质鉴定的针对性方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5800/12388471/d95f95c8980f/molecules-30-03379-g001.jpg

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