IEICE TRANSACTIONS on Fundamentals
Separation of Mass Spectra Based on Probabilistic Latent Component Analysis for Explosives Detection
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.9 pp.1888-1897
- Publication Date
- 2015/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.1888
- Type of Manuscript
- Special Section PAPER (Special Section on Signal Processing for Sensing and Diagnosis)
- Category
Authors
Yohei KAWAGUCHI
Hitachi, Ltd.
Masahito TOGAMI
Hitachi, Ltd.
Hisashi NAGANO
Hitachi, Ltd.
Yuichiro HASHIMOTO
Hitachi, Ltd.
Masuyuki SUGIYAMA
Hitachi, Ltd.
Yasuaki TAKADA
Hitachi, Ltd.
Keyword
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Yohei KAWAGUCHI, Masahito TOGAMI, Hisashi NAGANO, Yuichiro HASHIMOTO, Masuyuki SUGIYAMA, Yasuaki TAKADA, "Separation of Mass Spectra Based on Probabilistic Latent Component Analysis for Explosives Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 9, pp. 1888-1897, September 2015, doi: 10.1587/transfun.E98.A.1888.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.1888/_p
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@ARTICLE{e98-a_9_1888,
author={Yohei KAWAGUCHI, Masahito TOGAMI, Hisashi NAGANO, Yuichiro HASHIMOTO, Masuyuki SUGIYAMA, Yasuaki TAKADA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Separation of Mass Spectra Based on Probabilistic Latent Component Analysis for Explosives Detection},
year={2015},
volume={E98-A},
number={9},
pages={1888-1897},
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.},
keywords={},
doi={10.1587/transfun.E98.A.1888},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Separation of Mass Spectra Based on Probabilistic Latent Component Analysis for Explosives Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1888
EP - 1897
AU - Yohei KAWAGUCHI
AU - Masahito TOGAMI
AU - Hisashi NAGANO
AU - Yuichiro HASHIMOTO
AU - Masuyuki SUGIYAMA
AU - Yasuaki TAKADA
PY - 2015
DO - 10.1587/transfun.E98.A.1888
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2015
AB - 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.
ER -
English
