Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing
Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mazzara, Marco [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: |
© The Author(s) 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Food analytical methods - New York, NY : Springer, 2008, 6(2012), 1 vom: 08. Mai, Seite 210-220 |
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| Übergeordnetes Werk: |
volume:6 ; year:2012 ; number:1 ; day:08 ; month:05 ; pages:210-220 |
| Links: |
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| DOI / URN: |
10.1007/s12161-012-9407-5 |
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SPR025565834 |
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| 520 | |a Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. | ||
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| 650 | 4 | |a Limit of detection |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Paoletti, Claudia |4 aut | |
| 700 | 1 | |a Corbisier, Philippe |4 aut | |
| 700 | 1 | |a Grazioli, Emanuele |4 aut | |
| 700 | 1 | |a Larcher, Sara |4 aut | |
| 700 | 1 | |a Berben, Gilbert |4 aut | |
| 700 | 1 | |a De Loose, Marc |4 aut | |
| 700 | 1 | |a Folch, Imma |4 aut | |
| 700 | 1 | |a Henry, Christine |4 aut | |
| 700 | 1 | |a Hess, Norbert |4 aut | |
| 700 | 1 | |a Hougs, Lotte |4 aut | |
| 700 | 1 | |a Janssen, Eric |4 aut | |
| 700 | 1 | |a Moran, Gillian |4 aut | |
| 700 | 1 | |a Onori, Roberta |4 aut | |
| 700 | 1 | |a Van den Eede, Guy |4 aut | |
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10.1007/s12161-012-9407-5 doi (DE-627)SPR025565834 (SPR)s12161-012-9407-5-e DE-627 ger DE-627 rakwb eng Mazzara, Marco verfasserin aut Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2012 Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. Genetically modified organisms (dpeaa)DE-He213 DNA-based detection (dpeaa)DE-He213 Protein-based detection (dpeaa)DE-He213 Limit of detection (dpeaa)DE-He213 Paoletti, Claudia aut Corbisier, Philippe aut Grazioli, Emanuele aut Larcher, Sara aut Berben, Gilbert aut De Loose, Marc aut Folch, Imma aut Henry, Christine aut Hess, Norbert aut Hougs, Lotte aut Janssen, Eric aut Moran, Gillian aut Onori, Roberta aut Van den Eede, Guy aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 210-220 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:210-220 https://dx.doi.org/10.1007/s12161-012-9407-5 kostenfrei 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 05 210-220 |
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10.1007/s12161-012-9407-5 doi (DE-627)SPR025565834 (SPR)s12161-012-9407-5-e DE-627 ger DE-627 rakwb eng Mazzara, Marco verfasserin aut Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2012 Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. Genetically modified organisms (dpeaa)DE-He213 DNA-based detection (dpeaa)DE-He213 Protein-based detection (dpeaa)DE-He213 Limit of detection (dpeaa)DE-He213 Paoletti, Claudia aut Corbisier, Philippe aut Grazioli, Emanuele aut Larcher, Sara aut Berben, Gilbert aut De Loose, Marc aut Folch, Imma aut Henry, Christine aut Hess, Norbert aut Hougs, Lotte aut Janssen, Eric aut Moran, Gillian aut Onori, Roberta aut Van den Eede, Guy aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 210-220 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:210-220 https://dx.doi.org/10.1007/s12161-012-9407-5 kostenfrei 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 05 210-220 |
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10.1007/s12161-012-9407-5 doi (DE-627)SPR025565834 (SPR)s12161-012-9407-5-e DE-627 ger DE-627 rakwb eng Mazzara, Marco verfasserin aut Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2012 Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. Genetically modified organisms (dpeaa)DE-He213 DNA-based detection (dpeaa)DE-He213 Protein-based detection (dpeaa)DE-He213 Limit of detection (dpeaa)DE-He213 Paoletti, Claudia aut Corbisier, Philippe aut Grazioli, Emanuele aut Larcher, Sara aut Berben, Gilbert aut De Loose, Marc aut Folch, Imma aut Henry, Christine aut Hess, Norbert aut Hougs, Lotte aut Janssen, Eric aut Moran, Gillian aut Onori, Roberta aut Van den Eede, Guy aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 210-220 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:210-220 https://dx.doi.org/10.1007/s12161-012-9407-5 kostenfrei 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 05 210-220 |
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10.1007/s12161-012-9407-5 doi (DE-627)SPR025565834 (SPR)s12161-012-9407-5-e DE-627 ger DE-627 rakwb eng Mazzara, Marco verfasserin aut Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2012 Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. Genetically modified organisms (dpeaa)DE-He213 DNA-based detection (dpeaa)DE-He213 Protein-based detection (dpeaa)DE-He213 Limit of detection (dpeaa)DE-He213 Paoletti, Claudia aut Corbisier, Philippe aut Grazioli, Emanuele aut Larcher, Sara aut Berben, Gilbert aut De Loose, Marc aut Folch, Imma aut Henry, Christine aut Hess, Norbert aut Hougs, Lotte aut Janssen, Eric aut Moran, Gillian aut Onori, Roberta aut Van den Eede, Guy aut Enthalten in Food analytical methods New York, NY : Springer, 2008 6(2012), 1 vom: 08. Mai, Seite 210-220 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:6 year:2012 number:1 day:08 month:05 pages:210-220 https://dx.doi.org/10.1007/s12161-012-9407-5 kostenfrei 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 05 210-220 |
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Enthalten in Food analytical methods 6(2012), 1 vom: 08. Mai, Seite 210-220 volume:6 year:2012 number:1 day:08 month:05 pages:210-220 |
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Mazzara, Marco @@aut@@ Paoletti, Claudia @@aut@@ Corbisier, Philippe @@aut@@ Grazioli, Emanuele @@aut@@ Larcher, Sara @@aut@@ Berben, Gilbert @@aut@@ De Loose, Marc @@aut@@ Folch, Imma @@aut@@ Henry, Christine @@aut@@ Hess, Norbert @@aut@@ Hougs, Lotte @@aut@@ Janssen, Eric @@aut@@ Moran, Gillian @@aut@@ Onori, Roberta @@aut@@ Van den Eede, Guy @@aut@@ |
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This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. 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Mazzara, Marco |
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Mazzara, Marco misc Genetically modified organisms misc DNA-based detection misc Protein-based detection misc Limit of detection Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing |
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Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing Genetically modified organisms (dpeaa)DE-He213 DNA-based detection (dpeaa)DE-He213 Protein-based detection (dpeaa)DE-He213 Limit of detection (dpeaa)DE-He213 |
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misc Genetically modified organisms misc DNA-based detection misc Protein-based detection misc Limit of detection |
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Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing |
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Mazzara, Marco |
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Food analytical methods |
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Mazzara, Marco Paoletti, Claudia Corbisier, Philippe Grazioli, Emanuele Larcher, Sara Berben, Gilbert De Loose, Marc Folch, Imma Henry, Christine Hess, Norbert Hougs, Lotte Janssen, Eric Moran, Gillian Onori, Roberta Van den Eede, Guy |
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kernel lot distribution assessment (kelda): a comparative study of protein and dna-based detection methods for gmo testing |
| title_auth |
Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing |
| abstract |
Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. © The Author(s) 2012 |
| abstractGer |
Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. © The Author(s) 2012 |
| abstract_unstemmed |
Abstract Monitoring of market products for detection of genetically modified organisms (GMO) is needed to comply with legislation in force in many regions of the world, to enforce traceability and to allow official control along the production and the distribution chains. This objective can be more easily achieved if reliable, time and cost-effective analytical methods are available. A GMO can be detected using either DNA-based or protein-based methods; both present advantages and disadvantages. The objective of this work was to assess the performance of a protein-based (lateral flow strips—LFT) and of a DNA-based (polymerase chain reaction—PCR) detection method for GMO analysis. One thousand five hundred samples of soybean, deriving from the sampling of 15 independent bulk lots in large shipments, were analysed to assess and compare the performance of the analytical methods and evaluate their suitability for GMO testing. Several indicators were used to compare the performance of the methods, including the percentage correlation between the PCR and LFT results. The GMO content of the samples ranged from 0 up to 100 %, allowing a full assessment of both analytical approaches with respect to all possible GMO content scenarios. The study revealed a very similar performance of the two methodologies, with low false-negative and false-positive results, and a very satisfactory capacity of both methods in detecting low amounts of target. While determining the fitness for purpose of both analytical approaches, this study also underlines the importance of alternative method characteristics, like costs and time. © The Author(s) 2012 |
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| title_short |
Kernel Lot Distribution Assessment (KeLDA): a Comparative Study of Protein and DNA-Based Detection Methods for GMO Testing |
| url |
https://dx.doi.org/10.1007/s12161-012-9407-5 |
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| author2 |
Paoletti, Claudia Corbisier, Philippe Grazioli, Emanuele Larcher, Sara Berben, Gilbert De Loose, Marc Folch, Imma Henry, Christine Hess, Norbert Hougs, Lotte Janssen, Eric Moran, Gillian Onori, Roberta Van den Eede, Guy |
| author2Str |
Paoletti, Claudia Corbisier, Philippe Grazioli, Emanuele Larcher, Sara Berben, Gilbert De Loose, Marc Folch, Imma Henry, Christine Hess, Norbert Hougs, Lotte Janssen, Eric Moran, Gillian Onori, Roberta Van den Eede, Guy |
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| doi_str |
10.1007/s12161-012-9407-5 |
| up_date |
2024-07-03T16:43:18.386Z |
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| score |
7.400218 |