Bayesian modelling and quantification of Raman spectroscopy Article Swipe
Matthew T. Moores
,
Kirsten Gracie
,
Jake Carson
,
Karen Faulds
,
Duncan Graham
,
Mark Girolami
·
YOU?
·
· 2018
· Open Access
·
YOU?
·
· 2018
· Open Access
·
Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurately quantify the amount of a given molecule in a sample. The presence of a large, nonuniform background presents a major challenge to analysis of these spectra. To overcome this challenge, we introduce a sequential Monte Carlo (SMC) algorithm to separate each observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. The peaks are modelled as Lorentzian, Gaussian, or pseudo-Voigt functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. The posterior distribution can be incrementally updated as more data becomes available, resulting in a scalable algorithm that is robust to local maxima. By incorporating this representation in a Bayesian hierarchical regression model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection, which is of major importance to analytical chemistry.
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- Type
- article
- Language
- en
- Landing Page
- https://strathprints.strath.ac.uk/view/author/524445.html>
- OA Status
- green
- Cited By
- 2
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2963960216
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https://openalex.org/W2963960216Canonical identifier for this work in OpenAlex
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Bayesian modelling and quantification of Raman spectroscopyWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
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2018-01-24Full publication date if available
- Authors
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Matthew T. Moores, Kirsten Gracie, Jake Carson, Karen Faulds, Duncan Graham, Mark GirolamiList of authors in order
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https://strathprints.strath.ac.uk/view/author/524445.html>Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://hdl.handle.net/10044/1/72571Direct OA link when available
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Bayesian probability, Monte Carlo method, Statistical physics, Gaussian, Algorithm, Computer science, Biological system, Mathematics, Physics, Chemistry, Artificial intelligence, Statistics, Computational chemistry, BiologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2022: 1, 2019: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://hdl.handle.net/10044/1/72571 |
| primary_location.id | pmh:oai:strathprints.strath.ac.uk:63329 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4306402226 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
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| primary_location.source.display_name | Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) |
| primary_location.source.host_organization | https://openalex.org/I181647926 |
| primary_location.source.host_organization_name | University of Strathclyde |
| primary_location.source.host_organization_lineage | https://openalex.org/I181647926 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | PeerReviewed |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://strathprints.strath.ac.uk/view/author/524445.html> |
| publication_date | 2018-01-24 |
| publication_year | 2018 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 18, 34, 38, 43, 48, 62, 74, 79, 103, 132, 146 |
| abstract_inverted_index.By | 141 |
| abstract_inverted_index.It | 20 |
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| abstract_inverted_index.an | 164 |
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| citation_normalized_percentile.is_in_top_10_percent | False |