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Disclaimer: This site does not store any files on its server. All contents are provided by non-affiliated third parties.Statistical Model Fitting of the Results of Gas Chromatography-High-Performance Liquid Chromatography-Mass Spectrometry Data.
Herein, we propose a methodology for the fitting of gas chromatography-high-performance liquid chromatography-mass spectrometry (GC-HPLC-MS) data by using non-linear regression methods, which are among the most commonly used. We consider the performance of three statistical approaches to fit a wide range of data sets that constitute different portions of the whole process, which are method optimization, key method tuning, and peak matching, respectively. The results show that all three methods worked as expected. Surprisingly, the nonlinear regression method based on local approximation was not very accurate, and was not recommended. However, it performed much better than the others in optimizing the results. We also demonstrated the importance