Nonlinear multivariate curve resolution alternating least squares (NL-MCR-ALS)
Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data tha...
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| Veröffentlicht in: | Journal of chemometrics 2014-10, Vol.28 (10), p.740-748 |
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| container_title | Journal of chemometrics |
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| creator | Pomerantsev, Alexey L. Zontov, Yuri V. Rodionova, Oxana Ye |
| description | Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods. Copyright © 2014 John Wiley & Sons, Ltd.
Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods. |
| doi_str_mv | 10.1002/cem.2666 |
| format | Article |
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Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods.</description><identifier>ISSN: 0886-9383</identifier><identifier>EISSN: 1099-128X</identifier><identifier>DOI: 10.1002/cem.2666</identifier><language>eng</language><publisher>Chichester: Blackwell Publishing Ltd</publisher><subject>Absorbance ; Accuracy ; ALS ; Calibration ; Chemometrics ; Flattening ; Lambert-Beer law violations ; Law ; Least squares method ; MCR ; Multivariate analysis ; nitric acid ; Nonlinearity ; nonlinearity, peak saturation ; peak saturation ; Spectra</subject><ispartof>Journal of chemometrics, 2014-10, Vol.28 (10), p.740-748</ispartof><rights>Copyright © 2014 John Wiley & Sons, Ltd.</rights><rights>Copyright Wiley Subscription Services, Inc. Oct 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4346-a184411b1fd05f97a0414bc2d210e2af139436faa2c20924ee3d0cdb2a74067e3</citedby><cites>FETCH-LOGICAL-c4346-a184411b1fd05f97a0414bc2d210e2af139436faa2c20924ee3d0cdb2a74067e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcem.2666$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcem.2666$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27929,27930,45579,45580</link.rule.ids></links><search><creatorcontrib>Pomerantsev, Alexey L.</creatorcontrib><creatorcontrib>Zontov, Yuri V.</creatorcontrib><creatorcontrib>Rodionova, Oxana Ye</creatorcontrib><title>Nonlinear multivariate curve resolution alternating least squares (NL-MCR-ALS)</title><title>Journal of chemometrics</title><addtitle>J. Chemometrics</addtitle><description>Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods. Copyright © 2014 John Wiley & Sons, Ltd.
Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods.</description><subject>Absorbance</subject><subject>Accuracy</subject><subject>ALS</subject><subject>Calibration</subject><subject>Chemometrics</subject><subject>Flattening</subject><subject>Lambert-Beer law violations</subject><subject>Law</subject><subject>Least squares method</subject><subject>MCR</subject><subject>Multivariate analysis</subject><subject>nitric acid</subject><subject>Nonlinearity</subject><subject>nonlinearity, peak saturation</subject><subject>peak saturation</subject><subject>Spectra</subject><issn>0886-9383</issn><issn>1099-128X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp10EtLw0AUBeBBFKxV8CcE3NRF6rwySZYSahVqRKvY3XCb3sjUaWJnEh__3lRFUXB1F_fjwDmEHDI6ZJTykwJXQ66U2iI9RtM0ZDyZbZMeTRIVpiIRu2TP-yWl3U_IHsnzurKmQnDBqrWNeQZnoMGgaN0zBg59bdvG1FUAtkFXQWOqh8Ai-Cbw6xY6EAzySXiZ3YSnk-nxPtkpwXo8-Lp9cnc2us3Ow8nV-CI7nYSFFFKFwBIpGZuzckGjMo2BSibnBV9wRpFDyUQqhSoBeMFpyiWiWNBiMecQS6piFH0y-Mx9cvW6Rd_olfEFWgsV1q3XTPFURBGntKNHf-iybrsm9kNFUSJ4kvwEFq723mGpn5xZgXvTjOrNsLobVm-G7Wj4SV-Mxbd_nc5Gl7-98Q2-fntwj1rFIo70fT7W-XTG0-l1pDPxDum9hyA</recordid><startdate>201410</startdate><enddate>201410</enddate><creator>Pomerantsev, Alexey L.</creator><creator>Zontov, Yuri V.</creator><creator>Rodionova, Oxana Ye</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201410</creationdate><title>Nonlinear multivariate curve resolution alternating least squares (NL-MCR-ALS)</title><author>Pomerantsev, Alexey L. ; Zontov, Yuri V. ; Rodionova, Oxana Ye</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4346-a184411b1fd05f97a0414bc2d210e2af139436faa2c20924ee3d0cdb2a74067e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Absorbance</topic><topic>Accuracy</topic><topic>ALS</topic><topic>Calibration</topic><topic>Chemometrics</topic><topic>Flattening</topic><topic>Lambert-Beer law violations</topic><topic>Law</topic><topic>Least squares method</topic><topic>MCR</topic><topic>Multivariate analysis</topic><topic>nitric acid</topic><topic>Nonlinearity</topic><topic>nonlinearity, peak saturation</topic><topic>peak saturation</topic><topic>Spectra</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pomerantsev, Alexey L.</creatorcontrib><creatorcontrib>Zontov, Yuri V.</creatorcontrib><creatorcontrib>Rodionova, Oxana Ye</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of chemometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pomerantsev, Alexey L.</au><au>Zontov, Yuri V.</au><au>Rodionova, Oxana Ye</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear multivariate curve resolution alternating least squares (NL-MCR-ALS)</atitle><jtitle>Journal of chemometrics</jtitle><addtitle>J. Chemometrics</addtitle><date>2014-10</date><risdate>2014</risdate><volume>28</volume><issue>10</issue><spage>740</spage><epage>748</epage><pages>740-748</pages><issn>0886-9383</issn><eissn>1099-128X</eissn><abstract>Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods. Copyright © 2014 John Wiley & Sons, Ltd.
Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods.</abstract><cop>Chichester</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/cem.2666</doi><tpages>9</tpages></addata></record> |
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| subjects | Absorbance Accuracy ALS Calibration Chemometrics Flattening Lambert-Beer law violations Law Least squares method MCR Multivariate analysis nitric acid Nonlinearity nonlinearity, peak saturation peak saturation Spectra |
| title | Nonlinear multivariate curve resolution alternating least squares (NL-MCR-ALS) |
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