UHPLC-QqQ-MS/MS method development and validation with statistical analysis: Determination of raspberry ketone metabolites in mice plasma and brain

Bo Yuan, Danyue Zhao, Dushyant Kshatriya, Nicholas T. Bello, James E. Simon, Qingli Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Raspberry ketone (RK) (4-(4-hydroxyphenyl)-2-butanone) is the major compound responsible for the characteristic aroma of red raspberries, and has long been used commercially as a flavoring agent and recently as a weight loss supplement. A targeted UHPLC-QqQ-MS/MS method was developed and validated for analysis of RK and 25 associated metabolites in mouse plasma and brain. Dispersion and projection analysis and central composite design were used for method optimization. Random effect analysis of variance was applied for validation inference and variation partition. Within this framework, repeatability, a broader sense of precision, was calculated as fraction of accuracy variance, reflecting instrumental imprecision, compound degradation and carry-over effects. Multivariate correlation analysis and principle component analysis were conducted, revealing underlying association among the manifold of method traits. R programming was engaged in streamlined statistical analysis and data visualization. Two particular phenomena, the analytes’ background existence in the enzyme solution used for phase II metabolites deconjugation, and the noted lability of analytes in pure solvent at 4 ℃ vs. elevated stability in biomatrices, were found critical to method development and validation. The approach for the method development and validation provided a foundation for experiments that examine RK metabolism and bioavailability.

Original languageEnglish
Article number122146
JournalJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
Volume1149
DOIs
Publication statusPublished - 15 Jul 2020
Externally publishedYes

Keywords

  • Design of experiment
  • Metabolomics
  • Multivariate analysis
  • Random effects ANOVA
  • Surface response modelling

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Clinical Biochemistry
  • Cell Biology

Cite this