Academic literature on the topic 'Mass spectrometry (GC-MS)'

ASTM D3241 (Standard Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels) measures the thermal oxidative stability of jet fuels under elevated temperature and pressure conditions. When jet fuels fail ASTM D3241, either at the refinery or in the distribution system, there can be supply disruptions and financial losses. Understanding the causes of poor thermal oxidative stability in jet fuels could help prevent or mitigate issues. In order to develop a deeper understanding of the molecular precursors that lead to ASTM D3241 failures, a number of analytical methodologies and data treatment techniques have been developed, applied, and reported here. Statistical analysis of LC/MS ESI data from jet fuels with varying thermal oxidative stabilities allows for the identification of molecules that are significant to ASTM D3241 failures. Differential statistical analysis of LC/MS ESI data from jet fuels before and after thermal oxidative stressing in a QCM reactor elucidates which significant molecules are being consumed during oxidation and which molecules are increasing in abundance. The analysis of thermal oxidative deposits that form during thermal oxidative stressing in the QCM reactor allows for the insight into the molecular components of the deposits. Attapulgus clay removes the polar molecules that lead to thermal oxidative stability issues in the refinery. Extraction of Attapulgus clay that has been used in a refinery to filter jet fuel with a series of solvents removes the polar molecules into a series of fractions. The subsequent analysis of the fractions by comprehensive GCxGC/MS leads to the identification of the different homologous series of molecules that are removed by the clay. The analyses developed and employed here are shown to be particularly useful for the analysis of trace polar nitrogen and oxygen containing molecules. Similar homologous series of molecules are identified across all of the different analyses. It is also clear from some of the analyses, along with previously reported data in the literature, that reactive sulfur-containing molecules are significant to poor thermal oxidative stability as measured by ASTM D3241 and to the formation of thermal oxidative deposits. There is still an opportunity to find methodologies to better characterize the sulfur species present and correlate them to the data that is reported here.

This diploma thesis is based on the current issue of the presence of pharmaceuticals in various components of the environment. Concerning the contamination by residues of pharmaceuticals the most affected environment is the aquatic environment where these substances leaks especially from wastewater treatment plants, which eliminate them during the cleaning process only partially. This work is focused on the selected group of pharmaceuticals, non-steroidal anti-inflammatory drugs (salicylic acid, ibuprofen, caffeine, naproxen, ketoprofen, diclofenac) in waste water. For analysis purposes two types of sampling were used and compared: the conventional spot sampling of wastewater and the sampling using passive samplers POCIS. The sampling took place at the inflow and outflow of the wastewater treatment plant in Brno Modřice. The solid phase extraction (SPE) using Oasis HLB columns was used as the extraction method. Extracted sample was derivatized then. Derivatization agents were: MSTFA (N-methyl-N-(trimethylsilyl)trifluoroacetamid) and BSTFA (N, O-bis(trimethylsilyl) trifluoroacetamid). The final analysis was performed using gas chromatography with mass spectrometric detection Time-of-Flight (GC/TOF-MS).

The methyl ester of an unsaturated fatty acid, methyllinoleate was reacted with oxygen in a pressurized system at a controlled temperature. A natural autoxidation of methyl linoleate was observed without the addition of an initiating reagent. This autoxidation process could be used to mimic the course of lipid peroxidation, which is the major cause ofradical damage to living cells. The technology of GC-MS was employed to monitor the autoxidation of methyllinoleate. Eight of the autoxidation products separated by GC column were identified by interpreting the corresponding EI ion mass spectra. The products from 9-alkoxy methyl linoleate radical were methyl octanate, 2,4-decadienal, nonanoic acid, 9-oxo-methyl ester, and its further oxidation product, nonanedioic acid, monomethyl ester. All of them formed through a pathway of beta-cleavage. The products from 13-alkoxy methyl linoleate radical were tridecanoic acid, 9, 11-diene-13-oxo-methyl ester, hex anal, and its further oxidation product, hexanoic acid. They were also formed through a mechanism of beta-cleavage. The fourth product from 13-alkoxy methyllinoleate radical was 13-keto-9, 11-octadecadienoic acid, methyl ester, which was obtained through a pathway of keto formation. Observation of their concentrations in the samples at different autoxidation periods revealed the time-course of formation of these products.

We introduce new methods for preprocessing and normalization of data acquired by gas/liquid chromatography coupled with mass spectrometry (GC/LC-MS). Normalization is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences. There are different sources of experimental bias including variabilities in sample collection, sample storage, poor experimental design, noise, etc. Also, instrument variability in experiments involving a large number of runs leads to a significant drift in intensity measurements. We propose new normalization methods based on bootstrapping, Gaussian process regression, non-negative matrix factorization (NMF), and Bayesian hierarchical models. These methods model the bias by borrowing information across runs and features. Another novel aspect is utilizing scan-level data to improve the accuracy of quantification. We evaluated the performance of our method using simulated and experimental data. In comparison with several existing methods, the proposed methods yielded significant improvement. Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software tools specifically designed for analysis of GS-SIM-MS data. We introduce SIMAT, a new R package for quantitative analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping peaks based on a pre-specified library of background analytes. The tool also allows visualization of the total ion chromatogram (TIC) of runs and extracted ion chromatogram (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS data can be imported in netCDF or NIST mass spectral library (MSL) formats. We evaluated the performance of SIMAT using several experimental data sets. Our results demonstrate that SIMAT performs better than AMDIS and MetaboliteDetector in terms of finding the correct targets in the acquired GC-SIM-MS data and estimating their relative levels.<br>Ph. D.