Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria
Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kaneda, Tomomi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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© The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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 - Springer Nature Singapore, 1985, 40(2024), 4 vom: 20. Feb., Seite 691-699 |
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| Übergeordnetes Werk: |
volume:40 ; year:2024 ; number:4 ; day:20 ; month:02 ; pages:691-699 |
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| DOI / URN: |
10.1007/s44211-023-00501-7 |
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SPR055280331 |
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| 520 | |a Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract | ||
| 650 | 4 | |a Periodontitis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fourier transform infrared spectroscopy (FTIR) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
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10.1007/s44211-023-00501-7 doi (DE-627)SPR055280331 (SPR)s44211-023-00501-7-e DE-627 ger DE-627 rakwb eng 540 VZ 35.23 bkl Kaneda, Tomomi verfasserin (orcid)0009-0006-2751-6201 aut Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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. Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract Periodontitis (dpeaa)DE-He213 Fourier transform infrared spectroscopy (FTIR) (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Watanabe, Masahiro (orcid)0000-0002-4638-6805 aut Honda, Hidehiko (orcid)0000-0001-7990-8991 aut Yamamoto, Masato (orcid)0000-0002-0761-9258 aut Inagaki, Takae aut Hironaka, Shouji (orcid)0000-0001-8049-6795 aut Enthalten in Analytical sciences Springer Nature Singapore, 1985 40(2024), 4 vom: 20. Feb., Seite 691-699 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:40 year:2024 number:4 day:20 month:02 pages:691-699 https://dx.doi.org/10.1007/s44211-023-00501-7 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_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 35.23 VZ AR 40 2024 4 20 02 691-699 |
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10.1007/s44211-023-00501-7 doi (DE-627)SPR055280331 (SPR)s44211-023-00501-7-e DE-627 ger DE-627 rakwb eng 540 VZ 35.23 bkl Kaneda, Tomomi verfasserin (orcid)0009-0006-2751-6201 aut Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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. Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract Periodontitis (dpeaa)DE-He213 Fourier transform infrared spectroscopy (FTIR) (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Watanabe, Masahiro (orcid)0000-0002-4638-6805 aut Honda, Hidehiko (orcid)0000-0001-7990-8991 aut Yamamoto, Masato (orcid)0000-0002-0761-9258 aut Inagaki, Takae aut Hironaka, Shouji (orcid)0000-0001-8049-6795 aut Enthalten in Analytical sciences Springer Nature Singapore, 1985 40(2024), 4 vom: 20. Feb., Seite 691-699 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:40 year:2024 number:4 day:20 month:02 pages:691-699 https://dx.doi.org/10.1007/s44211-023-00501-7 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_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 35.23 VZ AR 40 2024 4 20 02 691-699 |
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10.1007/s44211-023-00501-7 doi (DE-627)SPR055280331 (SPR)s44211-023-00501-7-e DE-627 ger DE-627 rakwb eng 540 VZ 35.23 bkl Kaneda, Tomomi verfasserin (orcid)0009-0006-2751-6201 aut Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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. Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract Periodontitis (dpeaa)DE-He213 Fourier transform infrared spectroscopy (FTIR) (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Watanabe, Masahiro (orcid)0000-0002-4638-6805 aut Honda, Hidehiko (orcid)0000-0001-7990-8991 aut Yamamoto, Masato (orcid)0000-0002-0761-9258 aut Inagaki, Takae aut Hironaka, Shouji (orcid)0000-0001-8049-6795 aut Enthalten in Analytical sciences Springer Nature Singapore, 1985 40(2024), 4 vom: 20. Feb., Seite 691-699 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:40 year:2024 number:4 day:20 month:02 pages:691-699 https://dx.doi.org/10.1007/s44211-023-00501-7 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_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 35.23 VZ AR 40 2024 4 20 02 691-699 |
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10.1007/s44211-023-00501-7 doi (DE-627)SPR055280331 (SPR)s44211-023-00501-7-e DE-627 ger DE-627 rakwb eng 540 VZ 35.23 bkl Kaneda, Tomomi verfasserin (orcid)0009-0006-2751-6201 aut Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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. Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract Periodontitis (dpeaa)DE-He213 Fourier transform infrared spectroscopy (FTIR) (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Watanabe, Masahiro (orcid)0000-0002-4638-6805 aut Honda, Hidehiko (orcid)0000-0001-7990-8991 aut Yamamoto, Masato (orcid)0000-0002-0761-9258 aut Inagaki, Takae aut Hironaka, Shouji (orcid)0000-0001-8049-6795 aut Enthalten in Analytical sciences Springer Nature Singapore, 1985 40(2024), 4 vom: 20. Feb., Seite 691-699 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:40 year:2024 number:4 day:20 month:02 pages:691-699 https://dx.doi.org/10.1007/s44211-023-00501-7 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_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 35.23 VZ AR 40 2024 4 20 02 691-699 |
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10.1007/s44211-023-00501-7 doi (DE-627)SPR055280331 (SPR)s44211-023-00501-7-e DE-627 ger DE-627 rakwb eng 540 VZ 35.23 bkl Kaneda, Tomomi verfasserin (orcid)0009-0006-2751-6201 aut Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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. Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract Periodontitis (dpeaa)DE-He213 Fourier transform infrared spectroscopy (FTIR) (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Watanabe, Masahiro (orcid)0000-0002-4638-6805 aut Honda, Hidehiko (orcid)0000-0001-7990-8991 aut Yamamoto, Masato (orcid)0000-0002-0761-9258 aut Inagaki, Takae aut Hironaka, Shouji (orcid)0000-0001-8049-6795 aut Enthalten in Analytical sciences Springer Nature Singapore, 1985 40(2024), 4 vom: 20. Feb., Seite 691-699 (DE-627)300895925 (DE-600)1483376-1 1348-2246 nnns volume:40 year:2024 number:4 day:20 month:02 pages:691-699 https://dx.doi.org/10.1007/s44211-023-00501-7 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_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 35.23 VZ AR 40 2024 4 20 02 691-699 |
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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">Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. 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Kaneda, Tomomi |
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540 VZ 35.23 bkl Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria Periodontitis (dpeaa)DE-He213 Fourier transform infrared spectroscopy (FTIR) (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 |
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Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria |
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fourier transform infrared spectroscopy and machine learning for porphyromonas gingivalis detection in oral bacteria |
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Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria |
| abstract |
Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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 |
Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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 |
Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 $ cm^{–1} $ within the wavenumber range of 500–7500 $ cm^{–1} $. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia ($ NH_{3} $) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, $ NH_{3} $, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of $ NH_{3} $ and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique. Graphical abstract © The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry 2024. 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 |
Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria |
| url |
https://dx.doi.org/10.1007/s44211-023-00501-7 |
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| author2 |
Watanabe, Masahiro Honda, Hidehiko Yamamoto, Masato Inagaki, Takae Hironaka, Shouji |
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Watanabe, Masahiro Honda, Hidehiko Yamamoto, Masato Inagaki, Takae Hironaka, Shouji |
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| doi_str |
10.1007/s44211-023-00501-7 |
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2024-07-03T14:33:48.522Z |
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| score |
7.4028378 |