Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis
Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal qua...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yolci Omeroglu, Perihan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Food analytical methods - New York, NY : Springer, 2008, 6(2012), 1 vom: 08. Mai, Seite 238-247 |
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| Übergeordnetes Werk: |
volume:6 ; year:2012 ; number:1 ; day:08 ; month:05 ; pages:238-247 |
| Links: |
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| DOI / URN: |
10.1007/s12161-012-9436-0 |
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| Katalog-ID: |
SPR025569244 |
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| 520 | |a Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. | ||
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| 650 | 4 | |a Pesticide residue analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Uncertainty |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sample processing |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Boyacioglu, Dilek |4 aut | |
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10.1007/s12161-012-9436-0 doi (DE-627)SPR025569244 (SPR)s12161-012-9436-0-e DE-627 ger DE-627 rakwb eng Yolci Omeroglu, Perihan verfasserin aut Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. Pesticide (dpeaa)DE-He213 Pesticide residue analysis (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Sample processing (dpeaa)DE-He213 Ambrus, Árpad aut Boyacioglu, Dilek aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 238-247 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:238-247 https://dx.doi.org/10.1007/s12161-012-9436-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 AR 6 2012 1 08 05 238-247 |
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10.1007/s12161-012-9436-0 doi (DE-627)SPR025569244 (SPR)s12161-012-9436-0-e DE-627 ger DE-627 rakwb eng Yolci Omeroglu, Perihan verfasserin aut Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. Pesticide (dpeaa)DE-He213 Pesticide residue analysis (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Sample processing (dpeaa)DE-He213 Ambrus, Árpad aut Boyacioglu, Dilek aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 238-247 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:238-247 https://dx.doi.org/10.1007/s12161-012-9436-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 AR 6 2012 1 08 05 238-247 |
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10.1007/s12161-012-9436-0 doi (DE-627)SPR025569244 (SPR)s12161-012-9436-0-e DE-627 ger DE-627 rakwb eng Yolci Omeroglu, Perihan verfasserin aut Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. Pesticide (dpeaa)DE-He213 Pesticide residue analysis (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Sample processing (dpeaa)DE-He213 Ambrus, Árpad aut Boyacioglu, Dilek aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 238-247 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:238-247 https://dx.doi.org/10.1007/s12161-012-9436-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 AR 6 2012 1 08 05 238-247 |
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10.1007/s12161-012-9436-0 doi (DE-627)SPR025569244 (SPR)s12161-012-9436-0-e DE-627 ger DE-627 rakwb eng Yolci Omeroglu, Perihan verfasserin aut Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. Pesticide (dpeaa)DE-He213 Pesticide residue analysis (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Sample processing (dpeaa)DE-He213 Ambrus, Árpad aut Boyacioglu, Dilek aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 238-247 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:238-247 https://dx.doi.org/10.1007/s12161-012-9436-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 AR 6 2012 1 08 05 238-247 |
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10.1007/s12161-012-9436-0 doi (DE-627)SPR025569244 (SPR)s12161-012-9436-0-e DE-627 ger DE-627 rakwb eng Yolci Omeroglu, Perihan verfasserin aut Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. Pesticide (dpeaa)DE-He213 Pesticide residue analysis (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Sample processing (dpeaa)DE-He213 Ambrus, Árpad aut Boyacioglu, Dilek aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 238-247 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:238-247 https://dx.doi.org/10.1007/s12161-012-9436-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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 AR 6 2012 1 08 05 238-247 |
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| author |
Yolci Omeroglu, Perihan |
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Yolci Omeroglu, Perihan misc Pesticide misc Pesticide residue analysis misc Uncertainty misc Sample processing Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis |
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Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis Pesticide (dpeaa)DE-He213 Pesticide residue analysis (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Sample processing (dpeaa)DE-He213 |
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Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis |
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estimation of sample processing uncertainty of large-size crops in pesticide residue analysis |
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Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis |
| abstract |
Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. © Springer Science+Business Media, LLC 2012 |
| abstractGer |
Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. © Springer Science+Business Media, LLC 2012 |
| abstract_unstemmed |
Abstract Sample processing procedure should result in analytical portions that are representative of the analytical sample, even if 1–5 g portion are withdrawn from a large-size crops. Therefore, the efficiency of sample processing should be regularly tested during method validation and internal quality control studies. In the scope of this study, sample processing uncertainty was investigated by surface treatment of the cucumber, papaya, and jackfruit representing large-size crops with radio-labelled chlorpyrifos methyl. After homogenization in a chopper, five small and five large test portions were withdrawn from “statistically” well-mixed materials and their residue content was determined with liquid scintillation counter, which provided a quick and well reproducible mode of quantitative determination of residues. The efficiency of sample processing characterized with the sampling constant, changed between 0.78 and 3.01 kg with typical value of 1.61 kg for processing at ambient temperature. Once the sampling constant was established, it was used to predict the uncertainty of sample processing for different test portion sizes. Uncertainty of sample processing at 30 g analytical portion varied between 4.70 and 10.55 %. Furthermore, addition of dry ice to papaya resulted in well-mixed samples as well as 50 % reduction in sampling constant value. © Springer Science+Business Media, LLC 2012 |
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Estimation of Sample Processing Uncertainty of Large-Size Crops in Pesticide Residue Analysis |
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https://dx.doi.org/10.1007/s12161-012-9436-0 |
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Ambrus, Árpad Boyacioglu, Dilek |
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10.1007/s12161-012-9436-0 |
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2024-07-03T16:44:29.426Z |
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|
| score |
7.399913 |