False negative rate and other performance measures of a sponge-wipe surface sampling method for low contaminant concentrations. [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 630.731 Agriculture and related technologies

Thông tin xuất bản: Albuquerque, N.M. : Oak Ridge, Tenn. : Sandia National Laboratories. ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2011

Mô tả vật lý: Size: 41 p. : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 265494

Recovery of spores from environmental surfaces is known to vary due to sampling methodology, techniques, spore size and characteristics, surface materials, and environmental conditions. A series of tests were performed to evaluate a new, validated sponge-wipe method. Specific factors evaluated were the effects of contaminant concentrations and surface materials on recovery efficiency (RE), false negative rate (FNR), limit of detection (LOD) - and the uncertainties of these quantities. Ceramic tile and stainless steel had the highest mean RE values (48.9 and 48.1%, respectively). Faux leather, vinyl tile, and painted wood had mean RE values of 30.3, 25.6, and 25.5, respectively, while plastic had the lowest mean RE (9.8%). Results show a roughly linear dependence of surface roughness on RE, where the smoothest surfaces have the highest mean RE values. REs were not influenced by the low spore concentrations tested (3 x 10{sup -3} to 1.86 CFU/cm{sup 2}). The FNR data were consistent with RE data, showing a trend of smoother surfaces resulting in higher REs and lower FNRs. Stainless steel generally had the lowest mean FNR (0.123) and plastic had the highest mean FNR (0.479). The LOD{sub 90} varied with surface material, from 0.015 CFU/cm{sup 2} on stainless steel up to 0.039 on plastic. Selecting sampling locations on the basis of surface roughness and using roughness to interpret spore recovery data can improve sampling. Further, FNR values, calculated as a function of concentration and surface material, can be used pre-sampling to calculate the numbers of samples for statistical sampling plans with desired performance, and post-sampling to calculate the confidence in characterization and clearance decisions.
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