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Updated: May 12, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
Published on: May 20, 2013
HPLC-MS/MS-based detection of Pseudomonas aeruginosa secondary metabolites and their application in metabolomics
1Changsha Institute for Food and Drug Control& National Alcohol Products Quality Supervision and Inspection Center, Changsha 410013, China.
Abstract:
An HPLC-MS/MS method was developed for determining Pseudomonas aeruginosa metabolites in culture media and cosmetics. The samples were dispersed in saturated sodium chloride, purified by C18-SPE after acetonitrile extraction, filtered through a PTFE membrane, and then analyzed. The mobile phase system used was 0.1 % formic acid-methanol. Positive and negative ions are scanned simultaneously, and multiple reaction monitoring (MRM) mode was used for detection. The results revealed that the linear correlation coefficients (r) of the 17 metabolic substances were all greater than 0.99 within the linear range of 1-100 μg/L. Most of the substances had matrix inhibition effect. The recoveries ranged from 85.1 to 114.7 %, and the relative standard deviations (RSD) ranged from 1.3 to 13.3 %. The LOD and LOQ were set as 0.05 mg/kg and 0.1mg/kg respectively. The culture metabolism of Pseudomonas aeruginosa, Pseudomonas putida and Pseudomonas fluorescens were analyzed. Pyocyanin, 2-heptyl-4-quinolone (HHQ), phenazine-1-carboxylic acid (PCA) and 1-hydroxyphenazine were found to be unique metabolites of Pseudomonas aeruginosa. The actual sample determination results shows that the metabolic residual risk of Pseudomonas aeruginosa in cosmetics is low. The simulation sample analysis results indicate that cosmetics can inhibit the metabolism of Pseudomonas aeruginosa. The HPLC-MS/MS method demonstrated high sensitivity, strong specificity, excellent selectivity, good accuracy and convenient operation. This study can provide technical support for the applied research on Pseudomonas aeruginosa metabolism and risk screening of residual metabolites in cosmetics. This analytical approach has significant implications for biometabolomics studies, microbial identification, and clinical medicine research.
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