Estimating animal abundance in ground beef batches assayed with molecular markers.
Estimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals f...
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2012-01-01
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doaj-6ff321ef146344c09daf4ddae7ce8feb2020-11-25T01:53:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0173e3419110.1371/journal.pone.0034191Estimating animal abundance in ground beef batches assayed with molecular markers.Xin-Sheng HuJanika SimilaSindey Schueler PlatzStephen S MooreGraham PlastowCiaran N MeghenEstimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals from diverse sources in a single product batch, which produces a high heterogeneity in capture probability. In order to estimate animal abundance through DNA profiling of ground beef constituents, two parameter-based statistical models were developed for incidence data. Simulations were applied to evaluate the maximum likelihood estimate (MLE) of a joint likelihood function from multiple surveys, showing superiority in the presence of high capture heterogeneity with small sample sizes, or comparable estimation in the presence of low capture heterogeneity with a large sample size when compared to other existing models. Our model employs the full information on the pattern of the capture-recapture frequencies from multiple samples. We applied the proposed models to estimate animal abundance in six manufacturing beef batches, genotyped using 30 single nucleotide polymorphism (SNP) markers, from a large scale beef grinding facility. Results show that between 411∼1367 animals were present in six manufacturing beef batches. These estimates are informative as a reference for improving recall processes and tracing finished meat products back to source.http://europepmc.org/articles/PMC3316629?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xin-Sheng Hu Janika Simila Sindey Schueler Platz Stephen S Moore Graham Plastow Ciaran N Meghen |
spellingShingle |
Xin-Sheng Hu Janika Simila Sindey Schueler Platz Stephen S Moore Graham Plastow Ciaran N Meghen Estimating animal abundance in ground beef batches assayed with molecular markers. PLoS ONE |
author_facet |
Xin-Sheng Hu Janika Simila Sindey Schueler Platz Stephen S Moore Graham Plastow Ciaran N Meghen |
author_sort |
Xin-Sheng Hu |
title |
Estimating animal abundance in ground beef batches assayed with molecular markers. |
title_short |
Estimating animal abundance in ground beef batches assayed with molecular markers. |
title_full |
Estimating animal abundance in ground beef batches assayed with molecular markers. |
title_fullStr |
Estimating animal abundance in ground beef batches assayed with molecular markers. |
title_full_unstemmed |
Estimating animal abundance in ground beef batches assayed with molecular markers. |
title_sort |
estimating animal abundance in ground beef batches assayed with molecular markers. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2012-01-01 |
description |
Estimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals from diverse sources in a single product batch, which produces a high heterogeneity in capture probability. In order to estimate animal abundance through DNA profiling of ground beef constituents, two parameter-based statistical models were developed for incidence data. Simulations were applied to evaluate the maximum likelihood estimate (MLE) of a joint likelihood function from multiple surveys, showing superiority in the presence of high capture heterogeneity with small sample sizes, or comparable estimation in the presence of low capture heterogeneity with a large sample size when compared to other existing models. Our model employs the full information on the pattern of the capture-recapture frequencies from multiple samples. We applied the proposed models to estimate animal abundance in six manufacturing beef batches, genotyped using 30 single nucleotide polymorphism (SNP) markers, from a large scale beef grinding facility. Results show that between 411∼1367 animals were present in six manufacturing beef batches. These estimates are informative as a reference for improving recall processes and tracing finished meat products back to source. |
url |
http://europepmc.org/articles/PMC3316629?pdf=render |
work_keys_str_mv |
AT xinshenghu estimatinganimalabundanceingroundbeefbatchesassayedwithmolecularmarkers AT janikasimila estimatinganimalabundanceingroundbeefbatchesassayedwithmolecularmarkers AT sindeyschuelerplatz estimatinganimalabundanceingroundbeefbatchesassayedwithmolecularmarkers AT stephensmoore estimatinganimalabundanceingroundbeefbatchesassayedwithmolecularmarkers AT grahamplastow estimatinganimalabundanceingroundbeefbatchesassayedwithmolecularmarkers AT ciarannmeghen estimatinganimalabundanceingroundbeefbatchesassayedwithmolecularmarkers |
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