Unbiased and biased chemometric analysis of LC-MS data from human urine following coffee intake.

We carried out a human volunteer study with 14 participants, eight of whom were asked to consume one cup of coffee at four different time points. Urine samples were collected at eight time points and analyzed by HPLC-MS analysis. The LC-MS data were subjected to unsupervised multivariate statistical analysis (principal component analysis) followed by supervised multivariate analysis (linear discriminant analysis).

LC-MS based metabolomic approach for the efficient identification and relative quantification of bioavailable cocoa phenolics in human urine.

This study was designed to investigate the rate and extent of urinary excretion of cocoa phenolic metabolites after human intake using metabolomics approach. In this context, a feeding trial was conducted where urine samples were collected at different time points over 48-h period. Several biomarkers were highlighted in LC-MS based chemometrics using principal component (PCA) and partial least squares discriminant analysis (PLS-DA), which revealed the presence of both epicatechin and gut microbial phenyl-γ-valerolactones (PVLs) conjugated analogues.

LC-MS/MS based molecular networking approach for the identification of cocoa phenolic metabolites in human urine.

Dietary phenolic compounds are often transformed by gut microbiota prior to absorption. This transformation may modify their structures, producing novel gut flora metabolites associated with numerous health benefits. Traditional mass spectrometry (MS) based approaches for assessing dietary exposure of cocotea (cocoa, coffee and tea) products provided very little information about the modification and fate of dietary phenolics after ingestion, mainly due to limitation of complex sample nature and their data analyses.