Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches
There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-valida...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sivaganesan, Mano [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
9 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal - Pandey, Avash ELSEVIER, 2021, a journal of the International Association on Water Quality (IAWQ), Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:156 ; year:2019 ; day:1 ; month:06 ; pages:456-464 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.watres.2019.03.011 |
|---|
| Katalog-ID: |
ELV046399100 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV046399100 | ||
| 003 | DE-627 | ||
| 005 | 20230626013635.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191021s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.watres.2019.03.011 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica |
| 035 | |a (DE-627)ELV046399100 | ||
| 035 | |a (ELSEVIER)S0043-1354(19)30224-6 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 333.7 |a 320 |q VZ |
| 100 | 1 | |a Sivaganesan, Mano |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches |
| 264 | 1 | |c 2019transfer abstract | |
| 300 | |a 9 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. | ||
| 520 | |a There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. | ||
| 650 | 7 | |a Data quality criteria |2 Elsevier | |
| 650 | 7 | |a Water quality criteria |2 Elsevier | |
| 650 | 7 | |a qPCR |2 Elsevier | |
| 650 | 7 | |a E. coli |2 Elsevier | |
| 650 | 7 | |a EPA method C |2 Elsevier | |
| 700 | 1 | |a Aw, Tiong Gim |4 oth | |
| 700 | 1 | |a Briggs, Shannon |4 oth | |
| 700 | 1 | |a Dreelin, Erin |4 oth | |
| 700 | 1 | |a Aslan, Asli |4 oth | |
| 700 | 1 | |a Dorevitch, Samuel |4 oth | |
| 700 | 1 | |a Shrestha, Abhilasha |4 oth | |
| 700 | 1 | |a Isaacs, Natasha |4 oth | |
| 700 | 1 | |a Kinzelman, Julie |4 oth | |
| 700 | 1 | |a Kleinheinz, Greg |4 oth | |
| 700 | 1 | |a Noble, Rachel |4 oth | |
| 700 | 1 | |a Rediske, Rick |4 oth | |
| 700 | 1 | |a Scull, Brian |4 oth | |
| 700 | 1 | |a Rosenberg, Susan |4 oth | |
| 700 | 1 | |a Weberman, Barbara |4 oth | |
| 700 | 1 | |a Sivy, Tami |4 oth | |
| 700 | 1 | |a Southwell, Ben |4 oth | |
| 700 | 1 | |a Siefring, Shawn |4 oth | |
| 700 | 1 | |a Oshima, Kevin |4 oth | |
| 700 | 1 | |a Haugland, Richard |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Pandey, Avash ELSEVIER |t Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal |d 2021 |d a journal of the International Association on Water Quality (IAWQ) |g Amsterdam [u.a.] |w (DE-627)ELV006716016 |
| 773 | 1 | 8 | |g volume:156 |g year:2019 |g day:1 |g month:06 |g pages:456-464 |g extent:9 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.watres.2019.03.011 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 951 | |a AR | ||
| 952 | |d 156 |j 2019 |b 1 |c 0601 |h 456-464 |g 9 | ||
| author_variant |
m s ms |
|---|---|
| matchkey_str |
sivaganesanmanoawtionggimbriggsshannondr:2019----:tnadzdaaultacpaccieifrrpdshrciclqcmtodatehdfraeqa |
| hierarchy_sort_str |
2019transfer abstract |
| publishDate |
2019 |
| allfields |
10.1016/j.watres.2019.03.011 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica (DE-627)ELV046399100 (ELSEVIER)S0043-1354(19)30224-6 DE-627 ger DE-627 rakwb eng 333.7 320 VZ Sivaganesan, Mano verfasserin aut Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Elsevier Aw, Tiong Gim oth Briggs, Shannon oth Dreelin, Erin oth Aslan, Asli oth Dorevitch, Samuel oth Shrestha, Abhilasha oth Isaacs, Natasha oth Kinzelman, Julie oth Kleinheinz, Greg oth Noble, Rachel oth Rediske, Rick oth Scull, Brian oth Rosenberg, Susan oth Weberman, Barbara oth Sivy, Tami oth Southwell, Ben oth Siefring, Shawn oth Oshima, Kevin oth Haugland, Richard oth Enthalten in Elsevier Science Pandey, Avash ELSEVIER Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal 2021 a journal of the International Association on Water Quality (IAWQ) Amsterdam [u.a.] (DE-627)ELV006716016 volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 https://doi.org/10.1016/j.watres.2019.03.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 156 2019 1 0601 456-464 9 |
| spelling |
10.1016/j.watres.2019.03.011 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica (DE-627)ELV046399100 (ELSEVIER)S0043-1354(19)30224-6 DE-627 ger DE-627 rakwb eng 333.7 320 VZ Sivaganesan, Mano verfasserin aut Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Elsevier Aw, Tiong Gim oth Briggs, Shannon oth Dreelin, Erin oth Aslan, Asli oth Dorevitch, Samuel oth Shrestha, Abhilasha oth Isaacs, Natasha oth Kinzelman, Julie oth Kleinheinz, Greg oth Noble, Rachel oth Rediske, Rick oth Scull, Brian oth Rosenberg, Susan oth Weberman, Barbara oth Sivy, Tami oth Southwell, Ben oth Siefring, Shawn oth Oshima, Kevin oth Haugland, Richard oth Enthalten in Elsevier Science Pandey, Avash ELSEVIER Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal 2021 a journal of the International Association on Water Quality (IAWQ) Amsterdam [u.a.] (DE-627)ELV006716016 volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 https://doi.org/10.1016/j.watres.2019.03.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 156 2019 1 0601 456-464 9 |
| allfields_unstemmed |
10.1016/j.watres.2019.03.011 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica (DE-627)ELV046399100 (ELSEVIER)S0043-1354(19)30224-6 DE-627 ger DE-627 rakwb eng 333.7 320 VZ Sivaganesan, Mano verfasserin aut Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Elsevier Aw, Tiong Gim oth Briggs, Shannon oth Dreelin, Erin oth Aslan, Asli oth Dorevitch, Samuel oth Shrestha, Abhilasha oth Isaacs, Natasha oth Kinzelman, Julie oth Kleinheinz, Greg oth Noble, Rachel oth Rediske, Rick oth Scull, Brian oth Rosenberg, Susan oth Weberman, Barbara oth Sivy, Tami oth Southwell, Ben oth Siefring, Shawn oth Oshima, Kevin oth Haugland, Richard oth Enthalten in Elsevier Science Pandey, Avash ELSEVIER Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal 2021 a journal of the International Association on Water Quality (IAWQ) Amsterdam [u.a.] (DE-627)ELV006716016 volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 https://doi.org/10.1016/j.watres.2019.03.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 156 2019 1 0601 456-464 9 |
| allfieldsGer |
10.1016/j.watres.2019.03.011 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica (DE-627)ELV046399100 (ELSEVIER)S0043-1354(19)30224-6 DE-627 ger DE-627 rakwb eng 333.7 320 VZ Sivaganesan, Mano verfasserin aut Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Elsevier Aw, Tiong Gim oth Briggs, Shannon oth Dreelin, Erin oth Aslan, Asli oth Dorevitch, Samuel oth Shrestha, Abhilasha oth Isaacs, Natasha oth Kinzelman, Julie oth Kleinheinz, Greg oth Noble, Rachel oth Rediske, Rick oth Scull, Brian oth Rosenberg, Susan oth Weberman, Barbara oth Sivy, Tami oth Southwell, Ben oth Siefring, Shawn oth Oshima, Kevin oth Haugland, Richard oth Enthalten in Elsevier Science Pandey, Avash ELSEVIER Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal 2021 a journal of the International Association on Water Quality (IAWQ) Amsterdam [u.a.] (DE-627)ELV006716016 volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 https://doi.org/10.1016/j.watres.2019.03.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 156 2019 1 0601 456-464 9 |
| allfieldsSound |
10.1016/j.watres.2019.03.011 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica (DE-627)ELV046399100 (ELSEVIER)S0043-1354(19)30224-6 DE-627 ger DE-627 rakwb eng 333.7 320 VZ Sivaganesan, Mano verfasserin aut Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Elsevier Aw, Tiong Gim oth Briggs, Shannon oth Dreelin, Erin oth Aslan, Asli oth Dorevitch, Samuel oth Shrestha, Abhilasha oth Isaacs, Natasha oth Kinzelman, Julie oth Kleinheinz, Greg oth Noble, Rachel oth Rediske, Rick oth Scull, Brian oth Rosenberg, Susan oth Weberman, Barbara oth Sivy, Tami oth Southwell, Ben oth Siefring, Shawn oth Oshima, Kevin oth Haugland, Richard oth Enthalten in Elsevier Science Pandey, Avash ELSEVIER Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal 2021 a journal of the International Association on Water Quality (IAWQ) Amsterdam [u.a.] (DE-627)ELV006716016 volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 https://doi.org/10.1016/j.watres.2019.03.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 156 2019 1 0601 456-464 9 |
| language |
English |
| source |
Enthalten in Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal Amsterdam [u.a.] volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 |
| sourceStr |
Enthalten in Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal Amsterdam [u.a.] volume:156 year:2019 day:1 month:06 pages:456-464 extent:9 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Data quality criteria Water quality criteria qPCR E. coli EPA method C |
| dewey-raw |
333.7 |
| isfreeaccess_bool |
false |
| container_title |
Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal |
| authorswithroles_txt_mv |
Sivaganesan, Mano @@aut@@ Aw, Tiong Gim @@oth@@ Briggs, Shannon @@oth@@ Dreelin, Erin @@oth@@ Aslan, Asli @@oth@@ Dorevitch, Samuel @@oth@@ Shrestha, Abhilasha @@oth@@ Isaacs, Natasha @@oth@@ Kinzelman, Julie @@oth@@ Kleinheinz, Greg @@oth@@ Noble, Rachel @@oth@@ Rediske, Rick @@oth@@ Scull, Brian @@oth@@ Rosenberg, Susan @@oth@@ Weberman, Barbara @@oth@@ Sivy, Tami @@oth@@ Southwell, Ben @@oth@@ Siefring, Shawn @@oth@@ Oshima, Kevin @@oth@@ Haugland, Richard @@oth@@ |
| publishDateDaySort_date |
2019-01-01T00:00:00Z |
| hierarchy_top_id |
ELV006716016 |
| dewey-sort |
3333.7 |
| id |
ELV046399100 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV046399100</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626013635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191021s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.watres.2019.03.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV046399100</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0043-1354(19)30224-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">333.7</subfield><subfield code="a">320</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sivaganesan, Mano</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">9</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data quality criteria</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Water quality criteria</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">qPCR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">E. coli</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">EPA method C</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Aw, Tiong Gim</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Briggs, Shannon</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dreelin, Erin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Aslan, Asli</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dorevitch, Samuel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shrestha, Abhilasha</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Isaacs, Natasha</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kinzelman, Julie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kleinheinz, Greg</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Noble, Rachel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rediske, Rick</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scull, Brian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rosenberg, Susan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Weberman, Barbara</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sivy, Tami</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Southwell, Ben</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Siefring, Shawn</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oshima, Kevin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haugland, Richard</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Pandey, Avash ELSEVIER</subfield><subfield code="t">Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal</subfield><subfield code="d">2021</subfield><subfield code="d">a journal of the International Association on Water Quality (IAWQ)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV006716016</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:156</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:1</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:456-464</subfield><subfield code="g">extent:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.watres.2019.03.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">156</subfield><subfield code="j">2019</subfield><subfield code="b">1</subfield><subfield code="c">0601</subfield><subfield code="h">456-464</subfield><subfield code="g">9</subfield></datafield></record></collection>
|
| author |
Sivaganesan, Mano |
| spellingShingle |
Sivaganesan, Mano ddc 333.7 Elsevier Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches |
| authorStr |
Sivaganesan, Mano |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV006716016 |
| format |
electronic Article |
| dewey-ones |
333 - Economics of land & energy 320 - Political science |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
333.7 320 VZ Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C Elsevier |
| topic |
ddc 333.7 Elsevier Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C |
| topic_unstemmed |
ddc 333.7 Elsevier Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C |
| topic_browse |
ddc 333.7 Elsevier Data quality criteria Elsevier Water quality criteria Elsevier qPCR Elsevier E. coli Elsevier EPA method C |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
t g a tg tga s b sb e d ed a a aa s d sd a s as n i ni j k jk g k gk r n rn r r rr b s bs s r sr b w bw t s ts b s bs s s ss k o ko r h rh |
| hierarchy_parent_title |
Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal |
| hierarchy_parent_id |
ELV006716016 |
| dewey-tens |
330 - Economics 320 - Political science |
| hierarchy_top_title |
Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV006716016 |
| title |
Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches |
| ctrlnum |
(DE-627)ELV046399100 (ELSEVIER)S0043-1354(19)30224-6 |
| title_full |
Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches |
| author_sort |
Sivaganesan, Mano |
| journal |
Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal |
| journalStr |
Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
300 - Social sciences |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
456 |
| author_browse |
Sivaganesan, Mano |
| container_volume |
156 |
| physical |
9 |
| class |
333.7 320 VZ |
| format_se |
Elektronische Aufsätze |
| author-letter |
Sivaganesan, Mano |
| doi_str_mv |
10.1016/j.watres.2019.03.011 |
| dewey-full |
333.7 320 |
| title_sort |
standardized data quality acceptance criteria for a rapid escherichia coli qpcr method (draft method c) for water quality monitoring at recreational beaches |
| title_auth |
Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches |
| abstract |
There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. |
| abstractGer |
There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. |
| abstract_unstemmed |
There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches |
| url |
https://doi.org/10.1016/j.watres.2019.03.011 |
| remote_bool |
true |
| author2 |
Aw, Tiong Gim Briggs, Shannon Dreelin, Erin Aslan, Asli Dorevitch, Samuel Shrestha, Abhilasha Isaacs, Natasha Kinzelman, Julie Kleinheinz, Greg Noble, Rachel Rediske, Rick Scull, Brian Rosenberg, Susan Weberman, Barbara Sivy, Tami Southwell, Ben Siefring, Shawn Oshima, Kevin Haugland, Richard |
| author2Str |
Aw, Tiong Gim Briggs, Shannon Dreelin, Erin Aslan, Asli Dorevitch, Samuel Shrestha, Abhilasha Isaacs, Natasha Kinzelman, Julie Kleinheinz, Greg Noble, Rachel Rediske, Rick Scull, Brian Rosenberg, Susan Weberman, Barbara Sivy, Tami Southwell, Ben Siefring, Shawn Oshima, Kevin Haugland, Richard |
| ppnlink |
ELV006716016 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth oth oth oth oth oth oth oth oth oth oth oth oth oth |
| doi_str |
10.1016/j.watres.2019.03.011 |
| up_date |
2024-07-06T20:07:46.972Z |
| _version_ |
1803861588153729024 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV046399100</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626013635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191021s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.watres.2019.03.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000835.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV046399100</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0043-1354(19)30224-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">333.7</subfield><subfield code="a">320</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sivaganesan, Mano</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">9</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data quality criteria</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Water quality criteria</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">qPCR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">E. coli</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">EPA method C</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Aw, Tiong Gim</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Briggs, Shannon</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dreelin, Erin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Aslan, Asli</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dorevitch, Samuel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shrestha, Abhilasha</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Isaacs, Natasha</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kinzelman, Julie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kleinheinz, Greg</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Noble, Rachel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rediske, Rick</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scull, Brian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rosenberg, Susan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Weberman, Barbara</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sivy, Tami</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Southwell, Ben</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Siefring, Shawn</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oshima, Kevin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haugland, Richard</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Pandey, Avash ELSEVIER</subfield><subfield code="t">Matches, mismatches and priorities of pathways from a climate-resilient development perspective in the mountains of Nepal</subfield><subfield code="d">2021</subfield><subfield code="d">a journal of the International Association on Water Quality (IAWQ)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV006716016</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:156</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:1</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:456-464</subfield><subfield code="g">extent:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.watres.2019.03.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">156</subfield><subfield code="j">2019</subfield><subfield code="b">1</subfield><subfield code="c">0601</subfield><subfield code="h">456-464</subfield><subfield code="g">9</subfield></datafield></record></collection>
|
| score |
7.399868 |