Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization

Tommy Sonne Alstrøm; Kasper Bayer Frøhling; Jan Larsen; Mikkel Nørgaard Schmidt; Michael Bache; Michael Stenbæk Schmidt; Mogens Havsteen Jakobsen; Anja Boisen

doi:10.1109/MLSP.2014.6958925

Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization

Tommy Sonne Alstrøm, Kasper Bayer Frøhling, Jan Larsen, Mikkel Nørgaard Schmidt, Michael Bache, Michael Stenbæk Schmidt, Mogens Havsteen Jakobsen, Anja Boisen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

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.

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	https://doi.org/10.1109/MLSP.2014.6958925
Publication status	Published - 2014
Event	2014 IEEE International Workshop on Machine Learning for Signal Processing - Reims Centre des Congrès, Reims, France Duration: 21 Sept 2014 → 24 Sept 2014 Conference number: 24 https://ieeexplore.ieee.org/xpl/conhome/6945945/proceeding

Conference

Conference	2014 IEEE International Workshop on Machine Learning for Signal Processing
Number	24
Location	Reims Centre des Congrès
Country/Territory	France
City	Reims
Period	21/09/2014 → 24/09/2014
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6945945/proceeding

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

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10.1109/MLSP.2014.6958925

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Alstrøm, T. S., Frøhling, K. B., Larsen, J., Schmidt, M. N., Bache, M., Schmidt, M. S., Jakobsen, M. H., & Boisen, A. (2014). Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. In M. Mboup, T. Adali , É. Moreau, & J. Larsen (Eds.), Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) IEEE. https://doi.org/10.1109/MLSP.2014.6958925

Alstrøm, Tommy Sonne ; Frøhling, Kasper Bayer ; Larsen, Jan et al. / Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) . editor / Mamadou Mboup ; Tülay Adali ; Éric Moreau ; Jan Larsen. IEEE, 2014.

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title = "Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization",

abstract = "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.",

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",

author = "Alstr{\o}m, {Tommy Sonne} and Fr{\o}hling, {Kasper Bayer} and Jan Larsen and Schmidt, {Mikkel N{\o}rgaard} and Michael Bache and Schmidt, {Michael Stenb{\ae}k} and Jakobsen, {Mogens Havsteen} and Anja Boisen",

year = "2014",

doi = "10.1109/MLSP.2014.6958925",

language = "English",

isbn = "978-1-4799-3694-6",

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booktitle = "Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)",

publisher = "IEEE",

address = "United States",

note = "2014 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 ; Conference date: 21-09-2014 Through 24-09-2014",

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Alstrøm, TS, Frøhling, KB, Larsen, J, Schmidt, MN, Bache, M, Schmidt, MS, Jakobsen, MH & Boisen, A 2014, Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. in M Mboup, T Adali , É Moreau & J Larsen (eds), Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) . IEEE, 2014 IEEE International Workshop on Machine Learning for Signal Processing, Reims, France, 21/09/2014. https://doi.org/10.1109/MLSP.2014.6958925

Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. / Alstrøm, Tommy Sonne; Frøhling, Kasper Bayer; Larsen, Jan et al.
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

N1 - Conference code: 24

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

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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

T2 - 2014 IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 21 September 2014 through 24 September 2014

ER -

Alstrøm TS, Frøhling KB, Larsen J, Schmidt MN, Bache M, Schmidt MS et al. Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. In Mboup M, Adali T, Moreau É, Larsen J, editors, Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) . IEEE. 2014 doi: 10.1109/MLSP.2014.6958925

Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization

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