The results and R package are available at http://stage.louisville.edu/faculty/x0zhan17/software/ software-development.

We report a compound identification method (SimMR), which simultaneously evaluates the mass spectrum similarity and the retention index distance using an empirical mixture score function, for the analysis of GC-MS data. The performance of the developed SimMR method was compared to that of two existing compound identification strategies. One is mass spectrum matching method without incorporation of retention index information (SM). The other is the method that sequentially evaluates the mass spectrum similarity and retention index distance (SeqMR). For the comparison purpose, we used the NIST/EPA/NIH Mass Spectral Library 2005. Our study demonstrates that SimMR performs the best among the three compound identification methods, by improving the overall identification accuracy up to 1.53% and 4.81% compared to SeqMR and SM, respectively.