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Validation of a Generic Quantitative 1 H NMR Method for Natural Products Analysis
Author(s) -
Gödecke Tanja,
Napolitano José G.,
RodríguezBrasco María F.,
Chen Shaog,
Jaki Birgit U.,
Lankin David C.,
Pauli Guido F.
Publication year - 2013
Publication title -
phytochemical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 72
eISSN - 1099-1565
pISSN - 0958-0344
DOI - 10.1002/pca.2436
Subject(s) - calibration , chemistry , quantitative analysis (chemistry) , workflow , software , detection limit , biological system , range (aeronautics) , accuracy and precision , chromatography , analytical chemistry (journal) , computer science , mathematics , statistics , materials science , database , composite material , biology , programming language
Nuclear magnetic resonance (NMR) spectroscopy is increasingly employed in the quantitative analysis and quality control (QC) of natural products (NP) including botanical dietary supplements (BDS). The establishment of QC protocols based on quantitative 1 H NMR (qHNMR) requires method validation. Objective Develop and validate a generic qHNMR method. Optimize acquisition and processing parameters, with specific attention to the requirements for the analysis of complex NP samples, including botanicals and purity assessment of NP isolates. Methods In order to establish the validated qHNMR method, samples containing two highly pure reference materials were used. The influence of acquisition and processing parameters on the method validation was examined, and general aspects of method validation of qHNMR methods discussed. Subsequently, the method established was applied to the analysis of two NP samples: a purified reference compound and a crude mixture. Results The accuracy and precision of qHNMR using internal or external calibration were compared, using a validated method suitable for complex samples. The impact of post‐acquisition processing on method validation was examined using three software packages: TopSpin, Mnova and NUTS. The dynamic range of the qHNMR method developed was 5000:1 with a limit of detection (LOD) of better than 10 µ m . The limit of quantification (LOQ) depends on the desired level of accuracy and experiment time spent. Conclusion This study revealed that acquisition parameters, processing parameters and processing software all contribute to qHNMR method validation. A validated method with a high dynamic range and general workflow for qHNMR analysis of NP is proposed. Copyright © 2013 John Wiley & Sons, Ltd.