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- Jonsson, Pär, et al.
(författare)
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Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets
- 2005
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Ingår i: The Analyst. - : Royal Society of Chemistry (RSC). - 0003-2654 .- 1364-5528. ; 130:5, s. 701-707
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable ( transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e. g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu ( USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.
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- Mullin, Lauren Gayle, 1984-, et al.
(författare)
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Analysis of hexafluoropropylene oxide-dimer acid (HFPO-DA) by liquid chromatography-mass spectrometry (LC-MS) : Review of current approaches and environmental levels
- 2019
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Ingår i: TrAC. Trends in analytical chemistry. - : Elsevier. - 0165-9936 .- 1879-3142. ; 118, s. 828-839
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Forskningsöversikt (refereegranskat)abstract
- Emerging per- and polyfluorinated compounds (PFAS) compounds are of increasing interest for environmental monitoring, one being hexafluoropropylene oxide-dimer acid (HFPO-DA), commonly referred to as GenX. The following review describes existing liquid chromatography-mass spectrometry (LC-MS) methods used to analyse HFPO-DA, including sample preparation and method sensitivity relative to other PFAS. Analytical challenges are also described, in particular the significant formation of in-source fragmentation, dimer and dimer adducts which detract from [M-H](-) signal. Lastly, detected levels of HFPO-DA in environmental and biological samples are compared across the limited number of available field exposure studies, which found several mu g/L concentrations in water samples taken near fluorochemical plant discharges.
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