Simulation of mass spectra of noncyclic alkanes and alkenes using artificial neural network Original Research Article

A 37-10-44 artificial neural network (ANN) was successfully developed for the simulation of mass spectra of noncyclic alkanes and alkenes. A total of 37 topological descriptors was selected using multiple linear regression (MLR) technique and was employed as inputs for the ANN. Forty-four outputs of the ANN represent the percent of total ion current (TIC%) in 44 positions. A collection of 117 noncyclic alkanes and 145 noncyclic alkenes was chosen as data set which were in the range of C5–10 H8–22. The data set was randomly divided into a training set consisting of 236 molecules and a prediction set consisting of 26 compounds. The results obtained indicate that except for the molecular ion peak, the predicted values of the m/z positions as well as their intensities are in good agreement with the experiment. Comparison of the SEC and SEP values of the ANN with those of the MLR models reveals the superiority of the ANN over that of the regression model for simulation of the mass spectra of organic compounds.