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Gas chromatograph, Gas detector, Trace gas analyzer, Gas analyzer, TOF mass spectrometer
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| A tool for comprehensive statistical evaluation of LC-MS data | |||||||||||||||||||||||||||||||||||
| Different metabolomics and profiling applications using LC-MS in drug discovery and development, clinical research, andfood science all share the common need to quickly analyze a large number ofhighly complex samples. Pattern recognition techniques are used tofilter the relevant informationfrom different sample groups and thus to detect compounds that differentiate samples. | |||||||||||||||||||||||||||||||||||
| Evaluation of LC-MS based Profiling Experiments with ProfileAnalysis | |||||||||||||||||||||||||||||||||||
| , m/z | |||||||||||||||||||||||||||||||||||
| "XT" HO**^ NH2 C,H11N02 Data Interpretation | |||||||||||||||||||||||||||||||||||
| ~i—I I—I I ■ I ■ I <—I ■ I | |||||||||||||||||||||||||||||||||||
| t 0000 nano ODDO ODDO □ DOD BucketTable | |||||||||||||||||||||||||||||||||||
| LC-MS chromatogram Acquisition: A LC-MS analysis consists ofatimecourse of individual mass spectra at each time point. Multiple LC-MS analyses are acquiredinasequence of measurements. | |||||||||||||||||||||||||||||||||||
| Multiple LC-MS chromatograms | |||||||||||||||||||||||||||||||||||
| Principle Components Analysis (PCA) | |||||||||||||||||||||||||||||||||||
| Interpretation: PCA provides information about distribution of varianceindatasets and | Feedback: Based on relevant masses, compounds are readily identified | ||||||||||||||||||||||||||||||||||
| Evaluation: LC-MS analyses are loaded to the sample table in ProfileAnalysis. LC-MS data are prepared for statistical analysis in a bucket table. The bucket table consists of retention time (RT)-m/z-pairs with corresponding intensities for each sample. The data set in the bucket table is analyzed using principle component analysis (PCA). | |||||||||||||||||||||||||||||||||||
| simultaneouslyhighlights byaccuratemass and RT-m/z-pairs responsible SigmaFit™ using "Gene- | |||||||||||||||||||||||||||||||||||
| forvariation. | rateMolecular Formula" or by MS/MS spectra with library search. | ||||||||||||||||||||||||||||||||||
| Reduce Dimensionality while Retaining Information The ProfileAnalysis™ software is the ideal tool to analyze large numbers of complex LC-MS profiles and extract the relevant information about the distribution of samples and the group characteristics with statistical means like PCA. The goal of PCA is to reduce the dimensionality in a data set while retaining all relevant information. PCA visualizes the distribution of samples in the scores plot and ranks the variables, here RT-m/z-buckets, according to the variance they explain. The preparation of LC-MS analyses for PCA is performed in a simple bucketing step: RT-m/z-buckets are defined and the corresponding intensities are entered for each sample. As an unsupervised pattern recognition technique, PCA does not require any additional information about samples or tentative groups for calculation. All possible principle components (PCs) are calculated at once, making re-calculations for visualization of higher PCs obsolete. Many more tools for model building like cross validation complete the statistical analysis. ProfileAnalysis provides the direct interaction of statistical results with the original LC-MS data. This permits the fast identification of statistically significant compounds on-the-fly by unique accurate mass and True Isotopic Pattern Analysis (TIP™) e.g. from micrOTOF™ ESI-TOF, or by MS/MS spectra, e.g. from HCTultra™ quadrupole ion trap or micrOTOF-Q™ ESI-Qq-TOF mass spectrometer. | |||||||||||||||||||||||||||||||||||