Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches.


Journal

Water research
ISSN: 1879-2448
Titre abrégé: Water Res
Pays: England
ID NLM: 0105072

Informations de publication

Date de publication:
01 Jun 2019
Historique:
received: 16 08 2018
revised: 05 03 2019
accepted: 12 03 2019
pubmed: 6 4 2019
medline: 2 11 2019
entrez: 6 4 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 30952079
pii: S0043-1354(19)30224-6
doi: 10.1016/j.watres.2019.03.011
pmc: PMC9943056
mid: NIHMS1528393
pii:
doi:

Substances chimiques

Water 059QF0KO0R

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

456-464

Subventions

Organisme : Intramural EPA
ID : EPA999999
Pays : United States

Informations de copyright

Published by Elsevier Ltd.

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Auteurs

Mano Sivaganesan (M)

U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.

Tiong Gim Aw (TG)

Department of Global Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA, 70112, USA.

Shannon Briggs (S)

Water Resources Division, Michigan Department of Environmental Quality, P. O. Box 30458, 525 West Allegan Street, Lansing, MI, 48909, USA.

Erin Dreelin (E)

Center for Water Sciences, Michigan State University, 1405 South Harrison Road, East Lansing, MI, 48823, USA.

Asli Aslan (A)

Georgia Southern University, Department of Environmental Health Sciences, 501 Forest Drive, Statesboro, GA, 30458, USA.

Samuel Dorevitch (S)

University of Illinois at Chicago, School of Public Health, 2121 W. Taylor Street, Chicago, IL, 60612, USA.

Abhilasha Shrestha (A)

University of Illinois at Chicago, School of Public Health, 2121 W. Taylor Street, Chicago, IL, 60612, USA.

Natasha Isaacs (N)

U.S. Geological Survey, Upper Midwest Water Science Center, 6520 Mercantile Way, Ste 5, Lansing, MI, 48911, USA.

Julie Kinzelman (J)

City of Racine Public Health Department, 730 Washington Ave, Racine, WI, 53403, USA.

Greg Kleinheinz (G)

University of Wisconsin-Oshkosh, Environmental Research Laboratory, 800 Algoma Boulevard, Oshkosh, WI, 54901, USA.

Rachel Noble (R)

Institute of Marine Sciences, University of North Carolina at Chapel Hill, 3431 Arendell Street, Morehead City, NC, 28557, USA.

Rick Rediske (R)

Annis Water Resources Institute, Lake Michigan Center, 740 W. Shoreline Dr, Muskegon, MI, 49441, USA.

Brian Scull (B)

Annis Water Resources Institute, Lake Michigan Center, 740 W. Shoreline Dr, Muskegon, MI, 49441, USA.

Susan Rosenberg (S)

Oakland County Health Division Laboratory, 1200 N. Telegraph, Pontiac, MI, 48341, USA.

Barbara Weberman (B)

Oakland County Health Division Laboratory, 1200 N. Telegraph, Pontiac, MI, 48341, USA.

Tami Sivy (T)

Saginaw Valley State University, Department of Chemistry, 7400 Bay Road, University Center, MI, 48710, USA.

Ben Southwell (B)

Lake Superior State University, Environmental Analysis Laboratory, 650 W. Easterday Ave, Sault Ste Marie, MI, 49783, USA.

Shawn Siefring (S)

U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.

Kevin Oshima (K)

U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.

Richard Haugland (R)

U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA. Electronic address: haugland.rich@epa.gov.

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