Abstract
A new algorithm for separating mass spectra into individual substances for explosives detection is proposed. In the field of mass spectrometry, separation methods, such as principal-component analysis (PCA) and independent-component analysis (ICA), are widely used. All components, however, have no negative values, and the orthogonality condition imposed on components also does not necessarily hold in the case of mass spectra. Because these methods allow negative values and PCA imposes an orthogonality condition, they are not suitable for separation of mass spectra. The proposed algorithm is based on probabilistic latent-component analysis (PLCA). PLCA is a statistical formulation of non-negative matrix factorization (NMF) using KL divergence. Because PLCA imposes the constraint of non-negativity but not orthogonality, the algorithm is effective for separating components of mass spectra. In addition, to estimate the components more accurately, a sparsity constraint is applied to PLCA for explosives detection. The main contribution is industrial application of the algorithm into an explosives-detection system. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms PCA and ICA. Also, results of calculation time demonstrate that the algorithm can work in real time.
