Academic Journal
Nonlinear dimensionality reduction methods for potentiometric multisensor systems data analysis.
| Τίτλος: | Nonlinear dimensionality reduction methods for potentiometric multisensor systems data analysis. |
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| Συγγραφείς: | Selivanovs, Zahars, Savosina, Julia, Agafonova‐Moroz, Marina, Kirsanov, Dmitry |
| Πηγή: | Electroanalysis; Jan2024, Vol. 36 Issue 1, p1-11, 11p |
| Θεματικοί όροι: | Self-organizing maps, Principal components analysis, Dimension reduction (Statistics), Data analysis, Chemical detectors, Electronic data processing |
| Περίληψη: | Electrochemical multisensor systems were proven to be a very perspective research direction in modern analytical chemistry. The multisensor approach assumes an employment of cross‐sensitive chemical sensors in combination with multivariate data processing methods. Dimensionality reduction of the data obtained from multisensor systems is a very important step and it is mostly based on the traditional tools of chemometrics, such as Principal Component Analysis (PCA). In case of chemically complex samples, the response of multisensor systems may have a complex nonlinear nature and the use of linear modelling methods does not seem optimal. However, the potential of nonlinear dimensionality reduction methods in the processing of multisensor data has not yet been systematically studied. In this report we aim to fill this gap and assess the performance of various nonlinear dimensionality reduction tools: Isomap, Self‐Organizing Kohonen Maps, and Autoencoder. These methods were explored using three datasets from potentiometric multisensor systems obtained in various real applications. It was shown that nonlinear dimensionality reduction methods give the possibility to obtain additional and more detailed information about the analyzed objects/processes compared to PCA. However, calculation time for nonlinear dimensionality reduction methods essentially exceeds that for PCA, and it can be a limiting factor for application of such algorithms. [ABSTRACT FROM AUTHOR] |
| Copyright of Electroanalysis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 174779648 RelevancyScore: 950 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 949.9453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nonlinear dimensionality reduction methods for potentiometric multisensor systems data analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Selivanovs%2C+Zahars%22">Selivanovs, Zahars</searchLink><br /><searchLink fieldCode="AR" term="%22Savosina%2C+Julia%22">Savosina, Julia</searchLink><br /><searchLink fieldCode="AR" term="%22Agafonova‐Moroz%2C+Marina%22">Agafonova‐Moroz, Marina</searchLink><br /><searchLink fieldCode="AR" term="%22Kirsanov%2C+Dmitry%22">Kirsanov, Dmitry</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electroanalysis; Jan2024, Vol. 36 Issue 1, p1-11, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dimension+reduction+%28Statistics%29%22">Dimension reduction (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+detectors%22">Chemical detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Electrochemical multisensor systems were proven to be a very perspective research direction in modern analytical chemistry. The multisensor approach assumes an employment of cross‐sensitive chemical sensors in combination with multivariate data processing methods. Dimensionality reduction of the data obtained from multisensor systems is a very important step and it is mostly based on the traditional tools of chemometrics, such as Principal Component Analysis (PCA). In case of chemically complex samples, the response of multisensor systems may have a complex nonlinear nature and the use of linear modelling methods does not seem optimal. However, the potential of nonlinear dimensionality reduction methods in the processing of multisensor data has not yet been systematically studied. In this report we aim to fill this gap and assess the performance of various nonlinear dimensionality reduction tools: Isomap, Self‐Organizing Kohonen Maps, and Autoencoder. These methods were explored using three datasets from potentiometric multisensor systems obtained in various real applications. It was shown that nonlinear dimensionality reduction methods give the possibility to obtain additional and more detailed information about the analyzed objects/processes compared to PCA. However, calculation time for nonlinear dimensionality reduction methods essentially exceeds that for PCA, and it can be a limiting factor for application of such algorithms. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electroanalysis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/elan.202300220 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Self-organizing maps Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Dimension reduction (Statistics) Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Chemical detectors Type: general – SubjectFull: Electronic data processing Type: general Titles: – TitleFull: Nonlinear dimensionality reduction methods for potentiometric multisensor systems data analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Selivanovs, Zahars – PersonEntity: Name: NameFull: Savosina, Julia – PersonEntity: Name: NameFull: Agafonova‐Moroz, Marina – PersonEntity: Name: NameFull: Kirsanov, Dmitry IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10400397 Numbering: – Type: volume Value: 36 – Type: issue Value: 1 Titles: – TitleFull: Electroanalysis Type: main |
| ResultId | 1 |