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- W3138026050 abstract "Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is currently the state-of-the-art instrument in terms of resolving power and accuracy for mass spectrometry and is able to resolve an unprecedented number of components in complex chemical mixtures, such as petroleum. The data analysis tools necessary struggle to keep pace with advancing instrument capabilities and the ever-increasing quantities of data generated. The existing workflows rely on combining different tools, not necessarily compatible between them and often generate a significant amount of manual repetitive tasks. A first issue is that the current standard practice does not utilise replicates to improve the reliability of an analysis. A second issue is that spectral stitching methods to combine data from multiple experiments performed for a single sample are not automated, and hence generate substantial manual work that precludes the routine applications of these experiments. Hyphenated ultra-high resolution, can provide structural information but the data analysis tools are lacking leading to loss retention time precision and labour-intensive workflows. A final issue explored in this thesis is that molecular assignments are performed using commercial software or in-house algorithms but currently no evaluation of the false positive assignments has been performed. During this PhD, algorithms were developed to address those needs and implemented using the R language. The tools needed to be accessible to a wide audience, not necessarily comfortable using scripted languages so interactive interfaces were created using the Shiny framework. Overall, the work presented in the thesis brings improved reliability when analysing complex mixture using Fourier transform mass spectrometry thanks to combining replicates or stitching multiple experiments, and assessing reproducibility. Further, it helps accelerate analyse hyphenated ultra-high-resolution mass spectrometry decreasing the time necessary from days to hours while bringing a deeper and more accurate insight into the data also capable to analyse and compare molecular assignments for petroleum related samples." @default.
- W3138026050 created "2021-03-29" @default.
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- W3138026050 date "2019-11-01" @default.
- W3138026050 modified "2023-09-27" @default.
- W3138026050 title "Addressing the challenge of petroleomics data" @default.
- W3138026050 hasPublicationYear "2019" @default.
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