Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools
Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the be...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Marengo, Emilio [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Anmerkung: |
© American Society for Mass Spectrometry 2009 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the American Society for Mass Spectrometry - Washington, DC : ACS Publications, 1990, 20(2009), 10 vom: 01. Juli, Seite 1859-1867 |
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| Übergeordnetes Werk: |
volume:20 ; year:2009 ; number:10 ; day:01 ; month:07 ; pages:1859-1867 |
| Links: |
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| DOI / URN: |
10.1016/j.jasms.2009.06.011 |
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| Katalog-ID: |
SPR031498396 |
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| 245 | 1 | 0 | |a Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools |
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| 520 | |a Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. | ||
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| 650 | 4 | |a Standard Mixture |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Noise Evaluation |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Gosetti, Fabio |4 aut | |
| 700 | 1 | |a Zerbinati, Orfeo |4 aut | |
| 700 | 1 | |a Gennaro, Maria Carla |4 aut | |
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10.1016/j.jasms.2009.06.011 doi (DE-627)SPR031498396 (SPR)j.jasms.2009.06.011-e DE-627 ger DE-627 rakwb eng Marengo, Emilio verfasserin aut Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Society for Mass Spectrometry 2009 Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. Principal Component Analysis (dpeaa)DE-He213 Autocorrelation Function (dpeaa)DE-He213 Target Function (dpeaa)DE-He213 Standard Mixture (dpeaa)DE-He213 Noise Evaluation (dpeaa)DE-He213 Robotti, Elisa aut Gosetti, Fabio aut Zerbinati, Orfeo aut Gennaro, Maria Carla aut Enthalten in Journal of the American Society for Mass Spectrometry Washington, DC : ACS Publications, 1990 20(2009), 10 vom: 01. Juli, Seite 1859-1867 (DE-627)320598799 (DE-600)2019911-9 1879-1123 nnns volume:20 year:2009 number:10 day:01 month:07 pages:1859-1867 https://dx.doi.org/10.1016/j.jasms.2009.06.011 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 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_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 20 2009 10 01 07 1859-1867 |
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10.1016/j.jasms.2009.06.011 doi (DE-627)SPR031498396 (SPR)j.jasms.2009.06.011-e DE-627 ger DE-627 rakwb eng Marengo, Emilio verfasserin aut Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Society for Mass Spectrometry 2009 Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. Principal Component Analysis (dpeaa)DE-He213 Autocorrelation Function (dpeaa)DE-He213 Target Function (dpeaa)DE-He213 Standard Mixture (dpeaa)DE-He213 Noise Evaluation (dpeaa)DE-He213 Robotti, Elisa aut Gosetti, Fabio aut Zerbinati, Orfeo aut Gennaro, Maria Carla aut Enthalten in Journal of the American Society for Mass Spectrometry Washington, DC : ACS Publications, 1990 20(2009), 10 vom: 01. Juli, Seite 1859-1867 (DE-627)320598799 (DE-600)2019911-9 1879-1123 nnns volume:20 year:2009 number:10 day:01 month:07 pages:1859-1867 https://dx.doi.org/10.1016/j.jasms.2009.06.011 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 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_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 20 2009 10 01 07 1859-1867 |
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10.1016/j.jasms.2009.06.011 doi (DE-627)SPR031498396 (SPR)j.jasms.2009.06.011-e DE-627 ger DE-627 rakwb eng Marengo, Emilio verfasserin aut Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Society for Mass Spectrometry 2009 Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. Principal Component Analysis (dpeaa)DE-He213 Autocorrelation Function (dpeaa)DE-He213 Target Function (dpeaa)DE-He213 Standard Mixture (dpeaa)DE-He213 Noise Evaluation (dpeaa)DE-He213 Robotti, Elisa aut Gosetti, Fabio aut Zerbinati, Orfeo aut Gennaro, Maria Carla aut Enthalten in Journal of the American Society for Mass Spectrometry Washington, DC : ACS Publications, 1990 20(2009), 10 vom: 01. Juli, Seite 1859-1867 (DE-627)320598799 (DE-600)2019911-9 1879-1123 nnns volume:20 year:2009 number:10 day:01 month:07 pages:1859-1867 https://dx.doi.org/10.1016/j.jasms.2009.06.011 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 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_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 20 2009 10 01 07 1859-1867 |
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10.1016/j.jasms.2009.06.011 doi (DE-627)SPR031498396 (SPR)j.jasms.2009.06.011-e DE-627 ger DE-627 rakwb eng Marengo, Emilio verfasserin aut Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Society for Mass Spectrometry 2009 Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. Principal Component Analysis (dpeaa)DE-He213 Autocorrelation Function (dpeaa)DE-He213 Target Function (dpeaa)DE-He213 Standard Mixture (dpeaa)DE-He213 Noise Evaluation (dpeaa)DE-He213 Robotti, Elisa aut Gosetti, Fabio aut Zerbinati, Orfeo aut Gennaro, Maria Carla aut Enthalten in Journal of the American Society for Mass Spectrometry Washington, DC : ACS Publications, 1990 20(2009), 10 vom: 01. Juli, Seite 1859-1867 (DE-627)320598799 (DE-600)2019911-9 1879-1123 nnns volume:20 year:2009 number:10 day:01 month:07 pages:1859-1867 https://dx.doi.org/10.1016/j.jasms.2009.06.011 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 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_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 20 2009 10 01 07 1859-1867 |
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10.1016/j.jasms.2009.06.011 doi (DE-627)SPR031498396 (SPR)j.jasms.2009.06.011-e DE-627 ger DE-627 rakwb eng Marengo, Emilio verfasserin aut Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © American Society for Mass Spectrometry 2009 Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. Principal Component Analysis (dpeaa)DE-He213 Autocorrelation Function (dpeaa)DE-He213 Target Function (dpeaa)DE-He213 Standard Mixture (dpeaa)DE-He213 Noise Evaluation (dpeaa)DE-He213 Robotti, Elisa aut Gosetti, Fabio aut Zerbinati, Orfeo aut Gennaro, Maria Carla aut Enthalten in Journal of the American Society for Mass Spectrometry Washington, DC : ACS Publications, 1990 20(2009), 10 vom: 01. Juli, Seite 1859-1867 (DE-627)320598799 (DE-600)2019911-9 1879-1123 nnns volume:20 year:2009 number:10 day:01 month:07 pages:1859-1867 https://dx.doi.org/10.1016/j.jasms.2009.06.011 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 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_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 20 2009 10 01 07 1859-1867 |
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Principal Component Analysis Autocorrelation Function Target Function Standard Mixture Noise Evaluation |
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Marengo, Emilio @@aut@@ Robotti, Elisa @@aut@@ Gosetti, Fabio @@aut@@ Zerbinati, Orfeo @@aut@@ Gennaro, Maria Carla @@aut@@ |
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Marengo, Emilio |
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Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools Principal Component Analysis (dpeaa)DE-He213 Autocorrelation Function (dpeaa)DE-He213 Target Function (dpeaa)DE-He213 Standard Mixture (dpeaa)DE-He213 Noise Evaluation (dpeaa)DE-He213 |
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Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools |
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Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools |
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evaluation of signal and noise and identification of a suitable target function in the tuning of an esi ion trap mass spectrometer by multivariate pattern recognition tools |
| title_auth |
Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools |
| abstract |
Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. © American Society for Mass Spectrometry 2009 |
| abstractGer |
Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. © American Society for Mass Spectrometry 2009 |
| abstract_unstemmed |
Abstract When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration. © American Society for Mass Spectrometry 2009 |
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Evaluation of signal and noise and identification of a suitable target function in the tuning of an ESI ion trap mass spectrometer by multivariate pattern recognition tools |
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Robotti, Elisa Gosetti, Fabio Zerbinati, Orfeo Gennaro, Maria Carla |
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