Bayesian Integration Analysis of Spectral Data
In recent years, advances in analysis software have made it possible to automatically perform data analysis for simple, single-spectrum analyses. This is crucial for ensuring the reproducibility and objectivity of data analysis. However, there is a growing concern that this automation may turn data...
Saved in:
| Published in | Journal of Surface Analysis Vol. 31; no. 2; p. 137 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Japanese |
| Published |
Tokyo
The Surface Analysis Society of Japan
2024
Surface Analysis Society of Japan |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1341-1756 1347-8400 1347-8400 |
| DOI | 10.1384/jsa.31.137 |
Cover
| Abstract | In recent years, advances in analysis software have made it possible to automatically perform data analysis for simple, single-spectrum analyses. This is crucial for ensuring the reproducibility and objectivity of data analysis. However, there is a growing concern that this automation may turn data analysis into a black box, leading to situations where analysis is conducted through unnatural processes without users even realizing it. This issue is also prevalent in the analysis of spectral data. In this article, we introduce a small part of the mathematical structure of spectral analysis within the framework of Bayesian statistics, with a particular focus on the design of the error function. Additionally, we also show that the Bayesian statistical framework naturally leads to integrated analysis of multiple spectra, as would be achieved by a skilled analyst. |
|---|---|
| AbstractList | In recent years, advances in analysis software have made it possible to automatically perform data analysis for simple, single-spectrum analyses. This is crucial for ensuring the reproducibility and objectivity of data analysis. However, there is a growing concern that this automation may turn data analysis into a black box, leading to situations where analysis is conducted through unnatural processes without users even realizing it. This issue is also prevalent in the analysis of spectral data. In this article, we introduce a small part of the mathematical structure of spectral analysis within the framework of Bayesian statistics, with a particular focus on the design of the error function. Additionally, we also show that the Bayesian statistical framework naturally leads to integrated analysis of multiple spectra, as would be achieved by a skilled analyst. |
| Author | Murakami, Ryo |
| Author_xml | – sequence: 1 fullname: Murakami, Ryo organization: National Institute for Materials Science |
| BookMark | eNp9zz1PwzAQBmALFYlSWPgFkdhAKXZsx87AUMpXpUoMwGxd3UtJFJxgp0L596SkdGS6V6dHp3tPycjVDgm5YHTKuBY3ZYApZ31WR2TMuFCxFpSOfjOLmZLpCTkNoaQ0TZUUYzK9gw5DAS5auBY3HtqidtHMQdWFIkR1Hr02aFsPVXQPLZyR4xyqgOf7OSHvjw9v8-d4-fK0mM-WsWWSqxioBJ4iXYlEYgqZoND_stIss5RBJpGjXSsFiInlICGzqwxoDlJovta55hNyPdzduga6b6gq0_jiE3xnGDW7qqavajjrs-r15aAbX39tMbSmrLe-7xAMTxiTWgu5U1eDsr4OwWP-_8nbAZehhQ0eKPi2sBX-0WTvD3v7Ad6g4z_e1Hhg |
| Cites_doi | 10.7566/JPSJ.93.034003 10.1103/PhysRevB.5.4709 10.1016/j.elspec.2019.146903 10.1038/s41592-019-0686-2 10.1016/0039-6028(89)90380-4 10.1063/1.1699114 10.1080/27660400.2021.1957304 10.1080/27660400.2021.1943172 10.1016/j.elspec.2020.147003 10.1016/0370-2693(87)91197-X 10.1016/j.neunet.2011.12.001 10.1016/j.elspec.2023.147298 |
| ContentType | Journal Article |
| Copyright | 2024 by The Surface Analysis Society of Japan Copyright Surface Analysis Society of Japan 2024 |
| Copyright_xml | – notice: 2024 by The Surface Analysis Society of Japan – notice: Copyright Surface Analysis Society of Japan 2024 |
| DBID | AAYXX CITATION ADTOC UNPAY |
| DOI | 10.1384/jsa.31.137 |
| DatabaseName | CrossRef Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 1347-8400 |
| ExternalDocumentID | 10.1384/jsa.31.137 10_1384_jsa_31_137 article_jsa_31_2_31_137_article_char_en |
| GroupedDBID | 2WC 5GY ACIWK ALMA_UNASSIGNED_HOLDINGS CS3 E3Z HH5 JSF JSH KQ8 RJT RZJ TKC AAYXX CITATION ADTOC UNPAY |
| ID | FETCH-LOGICAL-c1537-a05a36e0b425e6a940a347b819c01a95e3ecd77aee2c3a5a9cb9a0fa5483d8f83 |
| IEDL.DBID | UNPAY |
| ISSN | 1341-1756 1347-8400 |
| IngestDate | Tue Aug 19 23:38:22 EDT 2025 Mon Jun 30 07:44:15 EDT 2025 Wed Oct 01 06:05:57 EDT 2025 Thu Jul 17 14:00:47 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 2 |
| Language | Japanese |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1537-a05a36e0b425e6a940a347b819c01a95e3ecd77aee2c3a5a9cb9a0fa5483d8f83 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://www.jstage.jst.go.jp/article/jsa/31/2/31_137/_pdf |
| PQID | 3211588457 |
| PQPubID | 1976362 |
| ParticipantIDs | unpaywall_primary_10_1384_jsa_31_137 proquest_journals_3211588457 crossref_primary_10_1384_jsa_31_137 jstage_primary_article_jsa_31_2_31_137_article_char_en |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – year: 2024 text: 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Tokyo |
| PublicationPlace_xml | – name: Tokyo |
| PublicationTitle | Journal of Surface Analysis |
| PublicationYear | 2024 |
| Publisher | The Surface Analysis Society of Japan Surface Analysis Society of Japan |
| Publisher_xml | – name: The Surface Analysis Society of Japan – name: Surface Analysis Society of Japan |
| References | [5]A. Machida, K. Nagata, R. Murakami, H. Shinotsuka, H. Shouno, H. Yoshikawa, and M. Okada, Sci. Technol. Adv. Mater.: Methods 1, 123 (2021).https://doi.org/10.1080/27660400.2021.1943172. [11]N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, J. Chem. Phys. 21, 1087 (1953).https://doi.org/10.1063/1.1699114. [3]R. Nishimura, S. Katakami, K. Nagata, M. Mizumaki, and M. Okada, J. Phys. Soc. Jpn. 93, 034003 (2024). https://doi.org/10.7566/JPSJ.93.034003. [1]K. Nagata, S. Sugita, and M. Okada, Neural Networks 28, 82 (2012).https://doi.org/10.1016/j.neunet.2011.12.001. [9]P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, et al., Nature Methods 17, 261 (2020). https://doi.org/10.1038/s41592-019-0686-2. [4]R. Murakami, K. Nagata, H. Shouno, and H. Yoshikawa, Sci. Technol. Adv. Mater.: Methods 1, 182 (2021).https://doi.org/10.1080/27660400.2021.1957304. [13]S. Watanabe, J. Machine Learning Research 14, 867 (2013). [10]R. Murakami, H. Tanaka, H. Shinotsuka, N. Kenji, H. Shouno, and H. Yoshikawa, J. Electron Spectrosc. Relat. Phenom. 245, 147003 (2020).https://doi.org/10.1016/j.elspec.2020.147003. [6]R. Murakami, H. Yoshitomo, Y. Sonobayashi, H. Oji, H. Makino, H. Tanaka, H. Taguchi, et al., J. Electron Spectrosc. Relat. Phenom. 264, 147298 (2023). https://doi.org/10.1016/j.elspec.2023.147298. [12]S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth. Phys. Lett. B 195, 216 (1987).https://doi.org/10.1016/0370-2693(87)91197-X. [8]S. Tougaard, Surf. Sci. 216, 343 (1989).https://doi.org/10.1016/0039-6028(89)90380-4. [2]H. Shinotsuka, H. Yoshikawa, R. Murakami, K. Nakamura, H. Tanaka, and K. Yoshihara, J. Electron Spectrosc. Relat. Phenom. 239, 146903 (2020).https://doi.org/10.1016/j.elspec.2019.146903. [7]S. A. Shirley, Phys. Rev. B 5, 4709 (1972).https://doi.org/10.1103/PhysRevB.5.4709. 11 12 13 1 2 3 4 5 6 7 8 9 10 |
| References_xml | – reference: [5]A. Machida, K. Nagata, R. Murakami, H. Shinotsuka, H. Shouno, H. Yoshikawa, and M. Okada, Sci. Technol. Adv. Mater.: Methods 1, 123 (2021).https://doi.org/10.1080/27660400.2021.1943172. – reference: [7]S. A. Shirley, Phys. Rev. B 5, 4709 (1972).https://doi.org/10.1103/PhysRevB.5.4709. – reference: [13]S. Watanabe, J. Machine Learning Research 14, 867 (2013). – reference: [10]R. Murakami, H. Tanaka, H. Shinotsuka, N. Kenji, H. Shouno, and H. Yoshikawa, J. Electron Spectrosc. Relat. Phenom. 245, 147003 (2020).https://doi.org/10.1016/j.elspec.2020.147003. – reference: [4]R. Murakami, K. Nagata, H. Shouno, and H. Yoshikawa, Sci. Technol. Adv. Mater.: Methods 1, 182 (2021).https://doi.org/10.1080/27660400.2021.1957304. – reference: [3]R. Nishimura, S. Katakami, K. Nagata, M. Mizumaki, and M. Okada, J. Phys. Soc. Jpn. 93, 034003 (2024). https://doi.org/10.7566/JPSJ.93.034003. – reference: [8]S. Tougaard, Surf. Sci. 216, 343 (1989).https://doi.org/10.1016/0039-6028(89)90380-4. – reference: [2]H. Shinotsuka, H. Yoshikawa, R. Murakami, K. Nakamura, H. Tanaka, and K. Yoshihara, J. Electron Spectrosc. Relat. Phenom. 239, 146903 (2020).https://doi.org/10.1016/j.elspec.2019.146903. – reference: [11]N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, J. Chem. Phys. 21, 1087 (1953).https://doi.org/10.1063/1.1699114. – reference: [6]R. Murakami, H. Yoshitomo, Y. Sonobayashi, H. Oji, H. Makino, H. Tanaka, H. Taguchi, et al., J. Electron Spectrosc. Relat. Phenom. 264, 147298 (2023). https://doi.org/10.1016/j.elspec.2023.147298. – reference: [9]P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, et al., Nature Methods 17, 261 (2020). https://doi.org/10.1038/s41592-019-0686-2. – reference: [12]S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth. Phys. Lett. B 195, 216 (1987).https://doi.org/10.1016/0370-2693(87)91197-X. – reference: [1]K. Nagata, S. Sugita, and M. Okada, Neural Networks 28, 82 (2012).https://doi.org/10.1016/j.neunet.2011.12.001. – ident: 3 doi: 10.7566/JPSJ.93.034003 – ident: 7 doi: 10.1103/PhysRevB.5.4709 – ident: 2 doi: 10.1016/j.elspec.2019.146903 – ident: 9 doi: 10.1038/s41592-019-0686-2 – ident: 8 doi: 10.1016/0039-6028(89)90380-4 – ident: 11 doi: 10.1063/1.1699114 – ident: 4 doi: 10.1080/27660400.2021.1957304 – ident: 5 doi: 10.1080/27660400.2021.1943172 – ident: 10 doi: 10.1016/j.elspec.2020.147003 – ident: 12 doi: 10.1016/0370-2693(87)91197-X – ident: 13 – ident: 1 doi: 10.1016/j.neunet.2011.12.001 – ident: 6 doi: 10.1016/j.elspec.2023.147298 |
| SSID | ssj0066754 |
| Score | 2.2461743 |
| Snippet | In recent years, advances in analysis software have made it possible to automatically perform data analysis for simple, single-spectrum analyses. This is... |
| SourceID | unpaywall proquest crossref jstage |
| SourceType | Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 137 |
| SubjectTerms | Bayesian analysis Data analysis Error functions Spectrum analysis |
| Title | Bayesian Integration Analysis of Spectral Data |
| URI | https://www.jstage.jst.go.jp/article/jsa/31/2/31_137/_article/-char/en https://www.proquest.com/docview/3211588457 https://www.jstage.jst.go.jp/article/jsa/31/2/31_137/_pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 31 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | Journal of Surface Analysis, 2024, Vol.31(2), pp.137 |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 1347-8400 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0066754 issn: 1347-8400 databaseCode: HH5 dateStart: 20020101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1347-8400 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0066754 issn: 1347-8400 databaseCode: KQ8 dateStart: 20020101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEF60CnrxgYrVWgJ6zXM3r2N9FPUgHixUL2GymRSqpEVTpP56Z7uboh5EvCRLMuxjZmfnm33MMnYWSwjoj28HRcxtGvB8G0RSkNdaeEnMU4k62udddD0Qt8Nw-OWqL7Wtcky4aITq5YwmznjqGia64zdwue8G9Mh8HrvZtChX2Vqk1pZabG1wd997XDhYwrfJLEY6TaMwdVQTmpQnQmVDriql42_GaF2X-w1qbsyqKczf4eXli9Xpb7Onpr56s8mzM6tzR378COX4rwbtsC2DRa2eJttlK1jtMecc5qhOVlo3JpIESc5qYpdYk9JSV9ar-RHrEmrYZ4P-1cPFtW0uVbAlDW6xDV4IPEIvJ2XFCFLhAbEoJ2AgPR_SEDnKIo4BMZAcQkhlnoJXAnk2vEjKhB-wVjWp8JBZGEXol8i5B0IEJfnZCZfcB0yRKKVos9OGu9lUx87IFgtoicio5ZludJtFmj9LGsOdhiYwhMvv6jwaKXWbdRpBZUbx3jJODq06extSvmdL4f1S_NHfyI7ZZkBwRk--dFirfp3hCcGROu8qYxB2Tff7BBQU36U |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3LSsQwMOiuoBcfqLi-KOi1z6Svo09WD4sHF9RLmaZTYV26xe0i-vVOTLqsHkS8tKEdkswkmUeSmWHsNJYQ0B_fDoqY28TwfBtEUpDVWnhJzFOJOtrnIOoPxe1D-LCQ6ktdqxyRXvSM6uU8T5xR7RoiuqMpuNx3A3pkPo_drC7KZdaN1NlSh3WHg7uzxy8DS_g2icVIl4kL00Q1oUl5IlQ1ZKpSOf4mjFZ0u99UzdVZVcP7G4zHC1LneoM9tf3Vl01enFmTO_LjRyjHfyG0ydaNLmqdabAttoTVNnPO4R2VZ6V1YyJJ0MhZbewSa1JaKmW92h-xLqGBHTa8vrq_6NsmqYItibnFNngh8Ai9nBYrRpAKD4hEOSkG0vMhDZGjLOIYEAPJIYRU5il4JZBlw4ukTPgu61STCveYhVGEfomceyBEUJKdnXDJfcAUCVKKHjtpqZvVOnZG9nWAloiMMM800j0WafrMYQx1WpjAAM6_K380WtQ9dtgOVGYW3jTjZNAq39uQ6j2dD94vze__DeyArQWkzujNl0PWaV5neETqSJMfm4n3CdvT3r8 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bayesian+Integration+Analysis+of+Spectral+Data&rft.jtitle=Journal+of+surface+analysis&rft.au=Murakami%2C+Ryo&rft.date=2024&rft.issn=1341-1756&rft.eissn=1347-8400&rft.volume=31&rft.issue=2&rft.spage=137&rft_id=info:doi/10.1384%2Fjsa.31.137&rft.externalDBID=n%2Fa&rft.externalDocID=10_1384_jsa_31_137 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1341-1756&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1341-1756&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1341-1756&client=summon |