Conference paper
Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Department of Micro- and Nanotechnology, Technical University of Denmark3
Surface Engineering, Department of Micro- and Nanotechnology, Technical University of Denmark4
Nanoprobes, Department of Micro- and Nanotechnology, Technical University of Denmark5
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2014 |
| Pages: | 1-6 |
| Proceedings: | 2014 IEEE International Workshop on Machine Learning for Signal Processing |
| ISBN: | 1479936944 , 1479936952 , 9781479936946 and 9781479936953 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2014.6958925 |
| ORCIDs: | Alstrøm, Tommy Sonne , Larsen, Jan , Schmidt, Mikkel Nørgaard , Bache, Michael , Jakobsen, Mogens Havsteen and Boisen, Anja |
17β-Estradiol Abstracts Bioengineering Biosensing Communication, Networking and Broadcast Technologies Computing and Processing Engineering Profession Non-negative Matrix Factorization (NMF) Signal Processing and Analysis Spectroscopy Surface Enhanced Raman Spectroscopy (SERS) Unsupervised Learning
Bayes methods Bayesian nonnegative matrix factorization SERS biosensors diagnostics electromagnetic hot spots environmental safety localized areas low-concentrations hot spots matrix decomposition molecular biophysics molecule concentration detection optical sensors proteins real world detection setting signal-to-noise ratio surface enhanced Raman scattering surface enhanced Raman spectroscopy based sensors uncontaminated sensor readings
