Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters
Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ribeiro, Ana R. [verfasserIn] Gonçalves, Virgínia M. F. [verfasserIn] Maia, Alexandra S. [verfasserIn] Ribeiro, Cláudia [verfasserIn] Castro, Paula M. L. [verfasserIn] Tiritan, Maria E. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Environmental chemistry letters - Berlin [u.a.] : Springer, 2003, 13(2015), 2 vom: 19. Feb., Seite 203-210 |
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| Übergeordnetes Werk: |
volume:13 ; year:2015 ; number:2 ; day:19 ; month:02 ; pages:203-210 |
| Links: |
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| DOI / URN: |
10.1007/s10311-015-0498-2 |
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| Katalog-ID: |
SPR009432914 |
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| 520 | |a Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. | ||
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| 650 | 4 | |a Sample preparation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a HPLC-FD |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chiral pharmaceuticals |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chirobiotic V |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wastewaters |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Gonçalves, Virgínia M. F. |e verfasserin |4 aut | |
| 700 | 1 | |a Maia, Alexandra S. |e verfasserin |4 aut | |
| 700 | 1 | |a Ribeiro, Cláudia |e verfasserin |4 aut | |
| 700 | 1 | |a Castro, Paula M. L. |e verfasserin |4 aut | |
| 700 | 1 | |a Tiritan, Maria E. |e verfasserin |4 aut | |
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10.1007/s10311-015-0498-2 doi (DE-627)SPR009432914 (SPR)s10311-015-0498-2-e DE-627 ger DE-627 rakwb eng 540 ASE 43.12 bkl Ribeiro, Ana R. verfasserin aut Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. Dispersive liquid–liquid microextraction (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 HPLC-FD (dpeaa)DE-He213 Chiral pharmaceuticals (dpeaa)DE-He213 Chirobiotic V (dpeaa)DE-He213 Wastewaters (dpeaa)DE-He213 Gonçalves, Virgínia M. F. verfasserin aut Maia, Alexandra S. verfasserin aut Ribeiro, Cláudia verfasserin aut Castro, Paula M. L. verfasserin aut Tiritan, Maria E. verfasserin aut Enthalten in Environmental chemistry letters Berlin [u.a.] : Springer, 2003 13(2015), 2 vom: 19. Feb., Seite 203-210 (DE-627)363767770 (DE-600)2107984-5 1610-3661 nnns volume:13 year:2015 number:2 day:19 month:02 pages:203-210 https://dx.doi.org/10.1007/s10311-015-0498-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_702 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 ASE AR 13 2015 2 19 02 203-210 |
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10.1007/s10311-015-0498-2 doi (DE-627)SPR009432914 (SPR)s10311-015-0498-2-e DE-627 ger DE-627 rakwb eng 540 ASE 43.12 bkl Ribeiro, Ana R. verfasserin aut Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. Dispersive liquid–liquid microextraction (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 HPLC-FD (dpeaa)DE-He213 Chiral pharmaceuticals (dpeaa)DE-He213 Chirobiotic V (dpeaa)DE-He213 Wastewaters (dpeaa)DE-He213 Gonçalves, Virgínia M. F. verfasserin aut Maia, Alexandra S. verfasserin aut Ribeiro, Cláudia verfasserin aut Castro, Paula M. L. verfasserin aut Tiritan, Maria E. verfasserin aut Enthalten in Environmental chemistry letters Berlin [u.a.] : Springer, 2003 13(2015), 2 vom: 19. Feb., Seite 203-210 (DE-627)363767770 (DE-600)2107984-5 1610-3661 nnns volume:13 year:2015 number:2 day:19 month:02 pages:203-210 https://dx.doi.org/10.1007/s10311-015-0498-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_702 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 ASE AR 13 2015 2 19 02 203-210 |
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10.1007/s10311-015-0498-2 doi (DE-627)SPR009432914 (SPR)s10311-015-0498-2-e DE-627 ger DE-627 rakwb eng 540 ASE 43.12 bkl Ribeiro, Ana R. verfasserin aut Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. Dispersive liquid–liquid microextraction (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 HPLC-FD (dpeaa)DE-He213 Chiral pharmaceuticals (dpeaa)DE-He213 Chirobiotic V (dpeaa)DE-He213 Wastewaters (dpeaa)DE-He213 Gonçalves, Virgínia M. F. verfasserin aut Maia, Alexandra S. verfasserin aut Ribeiro, Cláudia verfasserin aut Castro, Paula M. L. verfasserin aut Tiritan, Maria E. verfasserin aut Enthalten in Environmental chemistry letters Berlin [u.a.] : Springer, 2003 13(2015), 2 vom: 19. Feb., Seite 203-210 (DE-627)363767770 (DE-600)2107984-5 1610-3661 nnns volume:13 year:2015 number:2 day:19 month:02 pages:203-210 https://dx.doi.org/10.1007/s10311-015-0498-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_702 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 ASE AR 13 2015 2 19 02 203-210 |
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10.1007/s10311-015-0498-2 doi (DE-627)SPR009432914 (SPR)s10311-015-0498-2-e DE-627 ger DE-627 rakwb eng 540 ASE 43.12 bkl Ribeiro, Ana R. verfasserin aut Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. Dispersive liquid–liquid microextraction (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 HPLC-FD (dpeaa)DE-He213 Chiral pharmaceuticals (dpeaa)DE-He213 Chirobiotic V (dpeaa)DE-He213 Wastewaters (dpeaa)DE-He213 Gonçalves, Virgínia M. F. verfasserin aut Maia, Alexandra S. verfasserin aut Ribeiro, Cláudia verfasserin aut Castro, Paula M. L. verfasserin aut Tiritan, Maria E. verfasserin aut Enthalten in Environmental chemistry letters Berlin [u.a.] : Springer, 2003 13(2015), 2 vom: 19. Feb., Seite 203-210 (DE-627)363767770 (DE-600)2107984-5 1610-3661 nnns volume:13 year:2015 number:2 day:19 month:02 pages:203-210 https://dx.doi.org/10.1007/s10311-015-0498-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_702 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 ASE AR 13 2015 2 19 02 203-210 |
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10.1007/s10311-015-0498-2 doi (DE-627)SPR009432914 (SPR)s10311-015-0498-2-e DE-627 ger DE-627 rakwb eng 540 ASE 43.12 bkl Ribeiro, Ana R. verfasserin aut Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. Dispersive liquid–liquid microextraction (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 HPLC-FD (dpeaa)DE-He213 Chiral pharmaceuticals (dpeaa)DE-He213 Chirobiotic V (dpeaa)DE-He213 Wastewaters (dpeaa)DE-He213 Gonçalves, Virgínia M. F. verfasserin aut Maia, Alexandra S. verfasserin aut Ribeiro, Cláudia verfasserin aut Castro, Paula M. L. verfasserin aut Tiritan, Maria E. verfasserin aut Enthalten in Environmental chemistry letters Berlin [u.a.] : Springer, 2003 13(2015), 2 vom: 19. Feb., Seite 203-210 (DE-627)363767770 (DE-600)2107984-5 1610-3661 nnns volume:13 year:2015 number:2 day:19 month:02 pages:203-210 https://dx.doi.org/10.1007/s10311-015-0498-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_702 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 ASE AR 13 2015 2 19 02 203-210 |
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Ribeiro, Ana R. @@aut@@ Gonçalves, Virgínia M. F. @@aut@@ Maia, Alexandra S. @@aut@@ Ribeiro, Cláudia @@aut@@ Castro, Paula M. L. @@aut@@ Tiritan, Maria E. @@aut@@ |
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Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. 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Ribeiro, Ana R. |
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Ribeiro, Ana R. ddc 540 bkl 43.12 misc Dispersive liquid–liquid microextraction misc Sample preparation misc HPLC-FD misc Chiral pharmaceuticals misc Chirobiotic V misc Wastewaters Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters |
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540 ASE 43.12 bkl Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters Dispersive liquid–liquid microextraction (dpeaa)DE-He213 Sample preparation (dpeaa)DE-He213 HPLC-FD (dpeaa)DE-He213 Chiral pharmaceuticals (dpeaa)DE-He213 Chirobiotic V (dpeaa)DE-He213 Wastewaters (dpeaa)DE-He213 |
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ddc 540 bkl 43.12 misc Dispersive liquid–liquid microextraction misc Sample preparation misc HPLC-FD misc Chiral pharmaceuticals misc Chirobiotic V misc Wastewaters |
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ddc 540 bkl 43.12 misc Dispersive liquid–liquid microextraction misc Sample preparation misc HPLC-FD misc Chiral pharmaceuticals misc Chirobiotic V misc Wastewaters |
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Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters |
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Ribeiro, Ana R. Gonçalves, Virgínia M. F. Maia, Alexandra S. Ribeiro, Cláudia Castro, Paula M. L. Tiritan, Maria E. |
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dispersive liquid–liquid microextraction and hplc to analyse fluoxetine and metoprolol enantiomers in wastewaters |
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Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters |
| abstract |
Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. |
| abstractGer |
Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. |
| abstract_unstemmed |
Abstract Sample extraction is a major step in environmental analyses due both to the high complexity of matrices and to the low concentration of the target analytes. Sample extraction is usually expensive, laborious, time-consuming and requires a high amount of organic solvents. Actually, there is a lack of miniaturized methodologies for sample extraction and chiral analyses. Here, we developed a dispersive liquid–liquid microextraction (DLLME) to extract the pharmaceuticals fluoxetine and metoprolol, as models of basic chiral compounds, from wastewater samples. Compounds were then analysed by enantioselective high-performance liquid chromatography. We monitored the influence of sample pH, extracting and dispersive solvent and respective volumes, salt addition, extracting and vortexing time. The DLLME method was validated within the range of 1–10 µg $ L^{−1} $ for fluoxetine enantiomers and 0.5–10 µg $ L^{−1} $ for metoprolol enantiomers. Accuracy ranged from 90.6 to 106 % and recovery rates from 54.5 to 81.5 %. Relative standard deviation values lower than 7.84 and 9.00 % were obtained for intra- and inter-batch precision, respectively. |
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Dispersive liquid–liquid microextraction and HPLC to analyse fluoxetine and metoprolol enantiomers in wastewaters |
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Gonçalves, Virgínia M. F. Maia, Alexandra S. Ribeiro, Cláudia Castro, Paula M. L. Tiritan, Maria E. |
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Gonçalves, Virgínia M. F. Maia, Alexandra S. Ribeiro, Cláudia Castro, Paula M. L. Tiritan, Maria E. |
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| score |
7.400943 |