1. DropMS: Petroleomics Data Treatment Based in Web Server for High-Resolution Mass Spectrometry
- Author
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Thalles R. Rosa, Wagner Ls Pacheco, Gabrielly S. Folli, Paulo R. Filgueiras, Wanderson Romão, and Marcela P Castro
- Subjects
Multivariate statistics ,business.industry ,Chemistry ,010401 analytical chemistry ,Petroleomics ,Pattern recognition ,Latent variable ,010402 general chemistry ,Mass spectrometry ,01 natural sciences ,0104 chemical sciences ,Chemometrics ,Data visualization ,Structural Biology ,Mass spectrum ,Range (statistics) ,Artificial intelligence ,business ,Spectroscopy - Abstract
We have built an online tool with a user-friendly and browser-based interface to facilitate the processing of high resolution and precision oil mass spectrometry data. DropMS does not require software installations. Mass spectra are sent and processed by the server using various algorithms reported in the literature, such as S/N ratio filters, recalibrations, chemical formula assimilations, and data visualization using graphs and diagrams popularly known in mass spectrometry as Van Krevelen and Kendrick diagrams and DBE vs C#. To validate the algorithms used and the processing results, the same mass spectrum of a typical Brazilian oil sample was analyzed by ESI(+)-FT-ICR/MS and processed using Sierra Analytics DropMS and Composer to obtain good agreement between the heteroatomic classes found and the number of compounds assigned. The MS has chemical information spread over the entire spectrum. The PLS multivariate regression has the main objective of decomposing the most important information into latent variables in order to quantify the most evaluated properties. Finally, 12 processed petroleum FT-ICR MS spectra were used for a partial least-squares regression with seven latent variables for R2 = 0.971 and RMSEC of 0.997 for API density property with a reference value range of 21-42.
- Published
- 2020