Mass spectral search method using the neural network approach

This paper investigates the use of neural networks as a novel approach in the implementation of spectral library search for gas chromatography mass spectrometry. A total of 28 drugs currently under control in Hong Kong were chosen for the study. Real forensic data, which represents mass spectra obtained under various conditions ranging from good to poor, were used for training and testing. A total of 355 spectra were used for training the neural networks, and a further set of 163 spectra was used for evaluation. All the neural networks considered performed better than the conventional benchmark, with recognition rates above 97.5%.