Mass spectra separation for explosives detection by using probabilistic latent component analysis

We propose a new method to separate mass spectra into components of each chemical compound for explosives detection. In mass spectra, all components have no negative values. However, conventional factor analyses for basis decomposition use no constraints of non-negativity, and we can not apply these methods to mass spectra. The proposed method is based on probabilistic latent component analysis (PLCA). The constraints of non-negativity always hold in PLCA, so that the method is effective for mass spectra. In addition, PLCA is defined in a statistical framework, thus PLCA makes it possible to utilize additional a priori information. Therefore, we introduce sparseness assumptions in the domain of mass spectrometry to PLCA in order to estimate the components more accurately. Experimental results indicate that the proposed method outperforms existing methods.