Combine multiple mass spectral similarity measures for compound identification

Compound identification in gas chromatography-mass spectrometry GC-MS is usually achieved by comparing a query mass spectrum with reference spectral library. The rapid growing spectral library requires a more powerful spectral similarity measure to achieve the best identification performance. In this study, seven spectrum similarity measures were combined to improve the identification accuracy. To reduce the computation time, absolute value distance ABS_VD similarity measure was chosen to construct a sub-library to be searched by all similarity measures. Particle Swarm Optimisation PSO algorithm was used to first find the optimised weights for the similarity score of each similarity measure based on the training data, and then the optimised weights were applied to the test data. Simulation study using the NIST/EPA/NIH Mass Spectral Library 2005 indicates that the combination of multiple similarity measures achieves a better performance than any single similarity measure, with the identification accuracy improved by 2.2% and 1.7% for the training data and the test data, respectively.