Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
| Editors | Mamadou Mboup, Tülay Adali , Éric Moreau, Jan Larsen |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2014 |
| ISBN (Print) | 978-1-4799-3694-6 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 24th IEEE International Workshop on Machine Learning for Signal Processing - Reims Centre des Congrès, Reims, France Duration: 21 Sep 2014 → 24 Sep 2014 Conference number: 24 http://mlsp2014.conwiz.dk/home.htm http://mlsp2014.conwiz.dk/home.htm |
Conference
| Conference | 24th IEEE International Workshop on Machine Learning for Signal Processing |
|---|---|
| Number | 24 |
| Location | Reims Centre des Congrès |
| Country | France |
| City | Reims |
| Period | 21/09/2014 → 24/09/2014 |
| Internet address |
Keywords
- Bioengineering
- Communication, Networking and Broadcast Technologies
- Computing and Processing
- Engineering Profession
- Signal Processing and Analysis
- 17β-Estradiol
- Abstracts
- Biosensing
- Non-negative Matrix Factorization (NMF)
- Spectroscopy
- Surface Enhanced Raman Spectroscopy (SERS)
- Unsupervised Learning
Cite this
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Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. / Alstrøm, Tommy Sonne; Frøhling, Kasper Bayer; Larsen, Jan; Schmidt, Mikkel Nørgaard; Bache, Michael; Schmidt, Michael Stenbæk; Jakobsen, Mogens Havsteen; Boisen, Anja.
Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) . ed. / Mamadou Mboup; Tülay Adali ; Éric Moreau; Jan Larsen. IEEE, 2014.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization
AU - Alstrøm, Tommy Sonne
AU - Frøhling, Kasper Bayer
AU - Larsen, Jan
AU - Schmidt, Mikkel Nørgaard
AU - Bache, Michael
AU - Schmidt, Michael Stenbæk
AU - Jakobsen, Mogens Havsteen
AU - Boisen, Anja
PY - 2014
Y1 - 2014
N2 - Due to applications in areas such as diagnostics and environmental safety, detection of molecules at very low concentrations has attracted recent attention. A powerful tool for this is Surface Enhanced Raman Spectroscopy (SERS) where substrates form localized areas of electromagnetic “hot spots” where the signal-to-noise (SNR) ratio is greatly amplified. However, at low concentrations hot spots with target molecules bound are rare. Furthermore, traditional detection relies on having uncontaminated sensor readings which is unrealistic in a real world detection setting. In this paper, we propose a Bayesian Non-negative Matrix Factorization (NMF) approach to identify locations of target molecules. The proposed method is able to successfully analyze the spectra and extract the target spectrum. A visualization of the loadings of the basis vector is created and the results show a clear SNR enhancement. Compared to traditional data processing, the NMF approach enables a more reproducible and sensitive sensor.
AB - Due to applications in areas such as diagnostics and environmental safety, detection of molecules at very low concentrations has attracted recent attention. A powerful tool for this is Surface Enhanced Raman Spectroscopy (SERS) where substrates form localized areas of electromagnetic “hot spots” where the signal-to-noise (SNR) ratio is greatly amplified. However, at low concentrations hot spots with target molecules bound are rare. Furthermore, traditional detection relies on having uncontaminated sensor readings which is unrealistic in a real world detection setting. In this paper, we propose a Bayesian Non-negative Matrix Factorization (NMF) approach to identify locations of target molecules. The proposed method is able to successfully analyze the spectra and extract the target spectrum. A visualization of the loadings of the basis vector is created and the results show a clear SNR enhancement. Compared to traditional data processing, the NMF approach enables a more reproducible and sensitive sensor.
KW - Bioengineering
KW - Communication, Networking and Broadcast Technologies
KW - Computing and Processing
KW - Engineering Profession
KW - Signal Processing and Analysis
KW - 17β-Estradiol
KW - Abstracts
KW - Biosensing
KW - Non-negative Matrix Factorization (NMF)
KW - Spectroscopy
KW - Surface Enhanced Raman Spectroscopy (SERS)
KW - Unsupervised Learning
U2 - 10.1109/MLSP.2014.6958925
DO - 10.1109/MLSP.2014.6958925
M3 - Article in proceedings
SN - 978-1-4799-3694-6
BT - Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
A2 - Mboup, Mamadou
A2 - Adali , Tülay
A2 - Moreau, Éric
A2 - Larsen, Jan
PB - IEEE
ER -