Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data
We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Furukawa, Makoto [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Inductively coupled plasma mass spectrometry |
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| Anmerkung: |
© The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Analytical sciences - [Cham] : Springer International Publishing, 1985, 39(2023), 12 vom: 18. Aug., Seite 1957-1966 |
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| Übergeordnetes Werk: |
volume:39 ; year:2023 ; number:12 ; day:18 ; month:08 ; pages:1957-1966 |
| Links: |
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| DOI / URN: |
10.1007/s44211-023-00403-8 |
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| Katalog-ID: |
SPR053843886 |
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| 520 | |a We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract | ||
| 650 | 4 | |a Principal-component analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Normalization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Characteristic classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inductively coupled plasma mass spectrometry |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fourier transform infrared spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Head space–gas chromatograph/mass spectrometry |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Niida, Yasuhiro |4 aut | |
| 700 | 1 | |a Kobayashi, Kyoko |4 aut | |
| 700 | 1 | |a Furuishi, Makiko |4 aut | |
| 700 | 1 | |a Umezawa, Rika |4 aut | |
| 700 | 1 | |a Shikino, Osamu |4 aut | |
| 700 | 1 | |a Suzuki, Toshiyuki |4 aut | |
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10.1007/s44211-023-00403-8 doi (DE-627)SPR053843886 (SPR)s44211-023-00403-8-e DE-627 ger DE-627 rakwb eng Furukawa, Makoto verfasserin (orcid)0000-0001-5726-876X aut Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract Principal-component analysis (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Characteristic classification (dpeaa)DE-He213 Inductively coupled plasma mass spectrometry (dpeaa)DE-He213 Fourier transform infrared spectroscopy (dpeaa)DE-He213 Head space–gas chromatograph/mass spectrometry (dpeaa)DE-He213 Niida, Yasuhiro aut Kobayashi, Kyoko aut Furuishi, Makiko aut Umezawa, Rika aut Shikino, Osamu aut Suzuki, Toshiyuki aut Enthalten in Analytical sciences [Cham] : Springer International Publishing, 1985 39(2023), 12 vom: 18. Aug., Seite 1957-1966 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 https://dx.doi.org/10.1007/s44211-023-00403-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 12 18 08 1957-1966 |
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10.1007/s44211-023-00403-8 doi (DE-627)SPR053843886 (SPR)s44211-023-00403-8-e DE-627 ger DE-627 rakwb eng Furukawa, Makoto verfasserin (orcid)0000-0001-5726-876X aut Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract Principal-component analysis (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Characteristic classification (dpeaa)DE-He213 Inductively coupled plasma mass spectrometry (dpeaa)DE-He213 Fourier transform infrared spectroscopy (dpeaa)DE-He213 Head space–gas chromatograph/mass spectrometry (dpeaa)DE-He213 Niida, Yasuhiro aut Kobayashi, Kyoko aut Furuishi, Makiko aut Umezawa, Rika aut Shikino, Osamu aut Suzuki, Toshiyuki aut Enthalten in Analytical sciences [Cham] : Springer International Publishing, 1985 39(2023), 12 vom: 18. Aug., Seite 1957-1966 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 https://dx.doi.org/10.1007/s44211-023-00403-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 12 18 08 1957-1966 |
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10.1007/s44211-023-00403-8 doi (DE-627)SPR053843886 (SPR)s44211-023-00403-8-e DE-627 ger DE-627 rakwb eng Furukawa, Makoto verfasserin (orcid)0000-0001-5726-876X aut Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract Principal-component analysis (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Characteristic classification (dpeaa)DE-He213 Inductively coupled plasma mass spectrometry (dpeaa)DE-He213 Fourier transform infrared spectroscopy (dpeaa)DE-He213 Head space–gas chromatograph/mass spectrometry (dpeaa)DE-He213 Niida, Yasuhiro aut Kobayashi, Kyoko aut Furuishi, Makiko aut Umezawa, Rika aut Shikino, Osamu aut Suzuki, Toshiyuki aut Enthalten in Analytical sciences [Cham] : Springer International Publishing, 1985 39(2023), 12 vom: 18. Aug., Seite 1957-1966 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 https://dx.doi.org/10.1007/s44211-023-00403-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 12 18 08 1957-1966 |
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10.1007/s44211-023-00403-8 doi (DE-627)SPR053843886 (SPR)s44211-023-00403-8-e DE-627 ger DE-627 rakwb eng Furukawa, Makoto verfasserin (orcid)0000-0001-5726-876X aut Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract Principal-component analysis (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Characteristic classification (dpeaa)DE-He213 Inductively coupled plasma mass spectrometry (dpeaa)DE-He213 Fourier transform infrared spectroscopy (dpeaa)DE-He213 Head space–gas chromatograph/mass spectrometry (dpeaa)DE-He213 Niida, Yasuhiro aut Kobayashi, Kyoko aut Furuishi, Makiko aut Umezawa, Rika aut Shikino, Osamu aut Suzuki, Toshiyuki aut Enthalten in Analytical sciences [Cham] : Springer International Publishing, 1985 39(2023), 12 vom: 18. Aug., Seite 1957-1966 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 https://dx.doi.org/10.1007/s44211-023-00403-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 12 18 08 1957-1966 |
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10.1007/s44211-023-00403-8 doi (DE-627)SPR053843886 (SPR)s44211-023-00403-8-e DE-627 ger DE-627 rakwb eng Furukawa, Makoto verfasserin (orcid)0000-0001-5726-876X aut Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract Principal-component analysis (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Characteristic classification (dpeaa)DE-He213 Inductively coupled plasma mass spectrometry (dpeaa)DE-He213 Fourier transform infrared spectroscopy (dpeaa)DE-He213 Head space–gas chromatograph/mass spectrometry (dpeaa)DE-He213 Niida, Yasuhiro aut Kobayashi, Kyoko aut Furuishi, Makiko aut Umezawa, Rika aut Shikino, Osamu aut Suzuki, Toshiyuki aut Enthalten in Analytical sciences [Cham] : Springer International Publishing, 1985 39(2023), 12 vom: 18. Aug., Seite 1957-1966 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 https://dx.doi.org/10.1007/s44211-023-00403-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 12 18 08 1957-1966 |
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Enthalten in Analytical sciences 39(2023), 12 vom: 18. Aug., Seite 1957-1966 volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 |
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Enthalten in Analytical sciences 39(2023), 12 vom: 18. Aug., Seite 1957-1966 volume:39 year:2023 number:12 day:18 month:08 pages:1957-1966 |
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Principal-component analysis Normalization Characteristic classification Inductively coupled plasma mass spectrometry Fourier transform infrared spectroscopy Head space–gas chromatograph/mass spectrometry |
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Furukawa, Makoto @@aut@@ Niida, Yasuhiro @@aut@@ Kobayashi, Kyoko @@aut@@ Furuishi, Makiko @@aut@@ Umezawa, Rika @@aut@@ Shikino, Osamu @@aut@@ Suzuki, Toshiyuki @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. 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Furukawa, Makoto |
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Furukawa, Makoto misc Principal-component analysis misc Normalization misc Characteristic classification misc Inductively coupled plasma mass spectrometry misc Fourier transform infrared spectroscopy misc Head space–gas chromatograph/mass spectrometry Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data |
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Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data Principal-component analysis (dpeaa)DE-He213 Normalization (dpeaa)DE-He213 Characteristic classification (dpeaa)DE-He213 Inductively coupled plasma mass spectrometry (dpeaa)DE-He213 Fourier transform infrared spectroscopy (dpeaa)DE-He213 Head space–gas chromatograph/mass spectrometry (dpeaa)DE-He213 |
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arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data |
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Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data |
| abstract |
We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the “PCA-merge” method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space–gas chromatograph/mass spectrometry while drying the paint films for 1–48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications. Graphical abstract © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data |
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https://dx.doi.org/10.1007/s44211-023-00403-8 |
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Niida, Yasuhiro Kobayashi, Kyoko Furuishi, Makiko Umezawa, Rika Shikino, Osamu Suzuki, Toshiyuki |
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Niida, Yasuhiro Kobayashi, Kyoko Furuishi, Makiko Umezawa, Rika Shikino, Osamu Suzuki, Toshiyuki |
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10.1007/s44211-023-00403-8 |
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2024-07-03T22:24:54.263Z |
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| score |
7.3984118 |