Neutral losses: A type of important variables in prediction of branching degree for acyclic alkenes from mass spectra

Neutral losses are a type of important variables in mass spectral interpretation. Since it is hard to calculate or extract neutral losses from mass spectra, they are usually discarded. In this study, dissimilarity analysis was employed to extract mass spectral characteristics for predicting branching degree of acyclic alkenes. The relationships between branching degree and neutral loss were constructed under direction of experimental observation and mass spectral fragmentations. A branching degree predictor of acyclic alkenes was subsequently built based on the above relationships. After tested by the experimental data in previous studies, the predictor could correctly provide the branching degree from abundant ions of mass spectra. More importantly, this predictor was able to point out which acyclic alkenes could be predicted correctly or not.

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► The neutral loss was extracted and creatively employed to predict branching degree.
► Dissimilarity analysis was used as tool to extract information from unknown spectra.
► An effective branching degree predictor was built by using only ONE variable.