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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2014Researchpeer-review

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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 languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
EditorsMamadou Mboup, Tülay Adali , Éric Moreau, Jan Larsen
Number of pages6
Publication date2014
ISBN (Print)978-1-4799-3694-6
Publication statusPublished - 2014
Event24th IEEE International Workshop on Machine Learning for Signal Processing - Reims Centre des Congrès, Reims, France
Duration: 21 Sep 201424 Sep 2014
Conference number: 24


Conference24th IEEE International Workshop on Machine Learning for Signal Processing
LocationReims Centre des Congrès
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • 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

ID: 103005094