Comparison of Gas Chromatography-Mass Spectrometry Data from Different Laboratories using Dynamic Programming

Abstract

The standard method for non-targeted profiling in plant metabolomics is the gas chromatography-mass spectrometry (GC-MS). It produces more robust output than other chromatographic techniques linked to MS, and metabolite identification requires both retention indices (i.e., adjusted retention times) and mass spectra, which are dependent on individual experimental platforms. Here we assess two approaches of dynamic programming on annotated reliable GC-MS data from two major institutions in plant metabolomics: (1) dynamic programming approach using absolute retention indices and (2) dynamic programming approach using relative retention indices. Our result suggested the advantage of the latter method.