Computers have significantly improved scientific research since their invention by making data collection and analysis easy, and increased efficiency of research instruments through high-speed processing, automation, auto sampling and detection of errors. But there are still several research instruments and methods, for example, Isotope Ratios Mass Spectrometry (IRMS) that rely on sample preparation methods and analysis that consume a lot of time, require large sample size and well-trained personnel to operate. On the other hand, the information obtained through IRMS can also be acquired via tandem mass spectrometry (MS) techniques that are faster, require small sample size, and are easier to operate. However, tandem MS techniques lack data processing mechanism and analysis tools that can produce similar information obtained from an IRMS. Thus, a nonlinear computer model was developed for the analysis of tandem MS data. It calculates isotope ratios (IRs) through a modeling and simulation optimization software that minimizes single objective function derived from the sum of differences between experimentally observed and theoretically predicted fragment abundances in tandem MS. The theoretical fragments are produced from a structural model of organic compounds that uses probability and stack data structure analysis while the objective function is described by nonlinear functions and optimized by nonlinear Genetic Algorithm (GA) and Tabu search algorithm. Isotope ratios (IRs) obtained through nonlinear modeling have shown better precisions between 0.01‰ and 0.1‰, and 0.37% and 8.14% accuracy compared to IRMS. Computer modeling has several advantages because it is efficient and takes less time for data analysis.<br/>

Nonlinear modeling of organic compounds structures, simulation, and optimization of tandem mass spectrometer (MS) data

255th National Meeting (2018)

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