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A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing
A stochastic simulation modelling approach was taken to determine the extent of Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for E. coli O157:H7 cross contamination when contaminated lettuce...
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| Published in: | Food microbiology 2011-06, Vol.28 (4), p.694-701 |
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| Main Authors: | , , , , , , , |
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| container_end_page | 701 |
| container_issue | 4 |
| container_start_page | 694 |
| container_title | Food microbiology |
| container_volume | 28 |
| creator | Pérez Rodríguez, F. Campos, D. Ryser, E.T. Buchholz, A.L. Posada-Izquierdo, G.D. Marks, B.P. Zurera, G. Todd, E. |
| description | A stochastic simulation modelling approach was taken to determine the extent of
Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for
E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line. Simulation of the model was performed using @Risk Palisade© Software, providing an estimate of concentration and prevalence in the final bags of product. Three different scenarios, named S1, S2, and S3, were considered to represent the initial concentration on the contaminated batch entering the processing line which corresponded to 0.01, 1 and 100
cfu/g, respectively. The model was satisfactorily validated based on Standard Error of Prediction (SEP), which ranged from 0.00-35%. ANOVA analysis performed on simulated data revealed that the initial concentration in the contaminated batch (i.e., S1, S2, and S3) did not influence significantly (
p
=
0.4) the
E. coli O157:H7 levels in bags derived from cross contamination. In addition, significantly different (
p
<
0.001) prevalence was observed at the different levels simulated (S1; S2 and S3). At the lowest contamination level (0.01
cfu/g), bags were cross-contaminated sporadically, resulting in very low
E. coli O157:H7 populations (mean: ≤2
cfu/bag) and prevalence levels ( |
| doi_str_mv | 10.1016/j.fm.2010.06.008 |
| format | article |
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Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for
E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line. Simulation of the model was performed using @Risk Palisade© Software, providing an estimate of concentration and prevalence in the final bags of product. Three different scenarios, named S1, S2, and S3, were considered to represent the initial concentration on the contaminated batch entering the processing line which corresponded to 0.01, 1 and 100
cfu/g, respectively. The model was satisfactorily validated based on Standard Error of Prediction (SEP), which ranged from 0.00-35%. ANOVA analysis performed on simulated data revealed that the initial concentration in the contaminated batch (i.e., S1, S2, and S3) did not influence significantly (
p
=
0.4) the
E. coli O157:H7 levels in bags derived from cross contamination. In addition, significantly different (
p
<
0.001) prevalence was observed at the different levels simulated (S1; S2 and S3). At the lowest contamination level (0.01
cfu/g), bags were cross-contaminated sporadically, resulting in very low
E. coli O157:H7 populations (mean: ≤2
cfu/bag) and prevalence levels (<1%). In contrast, higher average prevalence levels were obtained for S2 and S3 corresponding to 3.05 and 13.39%, respectively. Furthermore, the impact of different interventions on
E. coli O157:H7 cross-contamination (e.g., pathogen testing, chlorination, irradiation, and cleaning and disinfection procedures) was evaluated. Model showed that the pathogen was able to survive and be present in the final bags in all simulated interventions scenarios although irradiation (0.5
KGy) was a more effective decontamination step in reducing prevalence than chlorination or pathogen testing under the same simulated conditions.</description><identifier>ISSN: 0740-0020</identifier><identifier>EISSN: 1095-9998</identifier><identifier>DOI: 10.1016/j.fm.2010.06.008</identifier><identifier>PMID: 21511129</identifier><identifier>CODEN: FOMIE5</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Analysis of variance ; Bags ; Biological and medical sciences ; Chlorination ; Computer Simulation ; Contamination ; Cross-contamination ; Escherichia coli ; Escherichia coli Infections - prevention & control ; Escherichia coli O157 - growth & development ; Escherichia coli O157:H7 ; Food industries ; Food microbiology ; Food Microbiology - methods ; Food-Processing Industry - methods ; Fruit and vegetable industries ; Fundamental and applied biological sciences. Psychology ; Irradiation ; Lactuca - microbiology ; Lettuce ; Lettuces ; Mathematical modelling ; Mathematical models ; Models, Biological ; Models, Statistical ; Pathogens ; Risk ; Risk assessment ; Sampling plan</subject><ispartof>Food microbiology, 2011-06, Vol.28 (4), p.694-701</ispartof><rights>2010 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><rights>Copyright © 2010 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-12542b79ea06e0f8a657e697ac71d2b636f808a359363ecb0182662e4246404b3</citedby><cites>FETCH-LOGICAL-c444t-12542b79ea06e0f8a657e697ac71d2b636f808a359363ecb0182662e4246404b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,789,790,23930,23931,25140,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24187044$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21511129$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pérez Rodríguez, F.</creatorcontrib><creatorcontrib>Campos, D.</creatorcontrib><creatorcontrib>Ryser, E.T.</creatorcontrib><creatorcontrib>Buchholz, A.L.</creatorcontrib><creatorcontrib>Posada-Izquierdo, G.D.</creatorcontrib><creatorcontrib>Marks, B.P.</creatorcontrib><creatorcontrib>Zurera, G.</creatorcontrib><creatorcontrib>Todd, E.</creatorcontrib><title>A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing</title><title>Food microbiology</title><addtitle>Food Microbiol</addtitle><description>A stochastic simulation modelling approach was taken to determine the extent of
Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for
E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line. Simulation of the model was performed using @Risk Palisade© Software, providing an estimate of concentration and prevalence in the final bags of product. Three different scenarios, named S1, S2, and S3, were considered to represent the initial concentration on the contaminated batch entering the processing line which corresponded to 0.01, 1 and 100
cfu/g, respectively. The model was satisfactorily validated based on Standard Error of Prediction (SEP), which ranged from 0.00-35%. ANOVA analysis performed on simulated data revealed that the initial concentration in the contaminated batch (i.e., S1, S2, and S3) did not influence significantly (
p
=
0.4) the
E. coli O157:H7 levels in bags derived from cross contamination. In addition, significantly different (
p
<
0.001) prevalence was observed at the different levels simulated (S1; S2 and S3). At the lowest contamination level (0.01
cfu/g), bags were cross-contaminated sporadically, resulting in very low
E. coli O157:H7 populations (mean: ≤2
cfu/bag) and prevalence levels (<1%). In contrast, higher average prevalence levels were obtained for S2 and S3 corresponding to 3.05 and 13.39%, respectively. Furthermore, the impact of different interventions on
E. coli O157:H7 cross-contamination (e.g., pathogen testing, chlorination, irradiation, and cleaning and disinfection procedures) was evaluated. Model showed that the pathogen was able to survive and be present in the final bags in all simulated interventions scenarios although irradiation (0.5
KGy) was a more effective decontamination step in reducing prevalence than chlorination or pathogen testing under the same simulated conditions.</description><subject>Analysis of variance</subject><subject>Bags</subject><subject>Biological and medical sciences</subject><subject>Chlorination</subject><subject>Computer Simulation</subject><subject>Contamination</subject><subject>Cross-contamination</subject><subject>Escherichia coli</subject><subject>Escherichia coli Infections - prevention & control</subject><subject>Escherichia coli O157 - growth & development</subject><subject>Escherichia coli O157:H7</subject><subject>Food industries</subject><subject>Food microbiology</subject><subject>Food Microbiology - methods</subject><subject>Food-Processing Industry - methods</subject><subject>Fruit and vegetable industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Irradiation</subject><subject>Lactuca - microbiology</subject><subject>Lettuce</subject><subject>Lettuces</subject><subject>Mathematical modelling</subject><subject>Mathematical models</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Pathogens</subject><subject>Risk</subject><subject>Risk assessment</subject><subject>Sampling plan</subject><issn>0740-0020</issn><issn>1095-9998</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kUFv1DAQhS0EotvCnRPyBdFLlhnHsePeqqpQpEq9wNlynEnXSxIXO6nEv8dlFzjBxWNL35vxvMfYG4QtAqoP--0wbQWUJ6gtQPuMbRBMUxlj2udsA1pCBSDghJ3mvAdAbGrzkp0IbBBRmA27v-STW3ZUjuDdyFPI3_gUexr5EBO_zn5HKfhdcNzHMfA7bPTFjeY-xZwrH-fFTWEu4jjzOPCRlmX1xPs1hfmeP6ToKedyfcVeDG7M9PpYz9jXj9dfrm6q27tPn68ubysvpVwqFI0UnTbkQBEMrVONJmW08xp70alaDS20rm5MrWryHWArlBIkhVQSZFefsfeHvmX095XyYqeQPY2jmymu2bZaCdE0RhTy_L8kKo11q4zAgsIB_bV1osE-pDC59MMi2Kcg7N4Ok30KwoKyJYgieXvsvnYT9X8Ev50vwLsj4HIxfkhu9iH_5SS2GqQs3MWBo-LaY6Bksw80e-pDIr_YPoZ__-InRH-jAg</recordid><startdate>20110601</startdate><enddate>20110601</enddate><creator>Pérez Rodríguez, F.</creator><creator>Campos, D.</creator><creator>Ryser, E.T.</creator><creator>Buchholz, A.L.</creator><creator>Posada-Izquierdo, G.D.</creator><creator>Marks, B.P.</creator><creator>Zurera, G.</creator><creator>Todd, E.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>7QL</scope><scope>7T7</scope><scope>C1K</scope><scope>P64</scope></search><sort><creationdate>20110601</creationdate><title>A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing</title><author>Pérez Rodríguez, F. ; Campos, D. ; Ryser, E.T. ; Buchholz, A.L. ; Posada-Izquierdo, G.D. ; Marks, B.P. ; Zurera, G. ; Todd, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-12542b79ea06e0f8a657e697ac71d2b636f808a359363ecb0182662e4246404b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Analysis of variance</topic><topic>Bags</topic><topic>Biological and medical sciences</topic><topic>Chlorination</topic><topic>Computer Simulation</topic><topic>Contamination</topic><topic>Cross-contamination</topic><topic>Escherichia coli</topic><topic>Escherichia coli Infections - prevention & control</topic><topic>Escherichia coli O157 - growth & development</topic><topic>Escherichia coli O157:H7</topic><topic>Food industries</topic><topic>Food microbiology</topic><topic>Food Microbiology - methods</topic><topic>Food-Processing Industry - methods</topic><topic>Fruit and vegetable industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Irradiation</topic><topic>Lactuca - microbiology</topic><topic>Lettuce</topic><topic>Lettuces</topic><topic>Mathematical modelling</topic><topic>Mathematical models</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Pathogens</topic><topic>Risk</topic><topic>Risk assessment</topic><topic>Sampling plan</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pérez Rodríguez, F.</creatorcontrib><creatorcontrib>Campos, D.</creatorcontrib><creatorcontrib>Ryser, E.T.</creatorcontrib><creatorcontrib>Buchholz, A.L.</creatorcontrib><creatorcontrib>Posada-Izquierdo, G.D.</creatorcontrib><creatorcontrib>Marks, B.P.</creatorcontrib><creatorcontrib>Zurera, G.</creatorcontrib><creatorcontrib>Todd, E.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Food microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pérez Rodríguez, F.</au><au>Campos, D.</au><au>Ryser, E.T.</au><au>Buchholz, A.L.</au><au>Posada-Izquierdo, G.D.</au><au>Marks, B.P.</au><au>Zurera, G.</au><au>Todd, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing</atitle><jtitle>Food microbiology</jtitle><addtitle>Food Microbiol</addtitle><date>2011-06-01</date><risdate>2011</risdate><volume>28</volume><issue>4</issue><spage>694</spage><epage>701</epage><pages>694-701</pages><issn>0740-0020</issn><eissn>1095-9998</eissn><coden>FOMIE5</coden><abstract>A stochastic simulation modelling approach was taken to determine the extent of
Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for
E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line. Simulation of the model was performed using @Risk Palisade© Software, providing an estimate of concentration and prevalence in the final bags of product. Three different scenarios, named S1, S2, and S3, were considered to represent the initial concentration on the contaminated batch entering the processing line which corresponded to 0.01, 1 and 100
cfu/g, respectively. The model was satisfactorily validated based on Standard Error of Prediction (SEP), which ranged from 0.00-35%. ANOVA analysis performed on simulated data revealed that the initial concentration in the contaminated batch (i.e., S1, S2, and S3) did not influence significantly (
p
=
0.4) the
E. coli O157:H7 levels in bags derived from cross contamination. In addition, significantly different (
p
<
0.001) prevalence was observed at the different levels simulated (S1; S2 and S3). At the lowest contamination level (0.01
cfu/g), bags were cross-contaminated sporadically, resulting in very low
E. coli O157:H7 populations (mean: ≤2
cfu/bag) and prevalence levels (<1%). In contrast, higher average prevalence levels were obtained for S2 and S3 corresponding to 3.05 and 13.39%, respectively. Furthermore, the impact of different interventions on
E. coli O157:H7 cross-contamination (e.g., pathogen testing, chlorination, irradiation, and cleaning and disinfection procedures) was evaluated. Model showed that the pathogen was able to survive and be present in the final bags in all simulated interventions scenarios although irradiation (0.5
KGy) was a more effective decontamination step in reducing prevalence than chlorination or pathogen testing under the same simulated conditions.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><pmid>21511129</pmid><doi>10.1016/j.fm.2010.06.008</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
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| ispartof | Food microbiology, 2011-06, Vol.28 (4), p.694-701 |
| issn | 0740-0020 1095-9998 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_876225592 |
| source | ScienceDirect Journals |
| subjects | Analysis of variance Bags Biological and medical sciences Chlorination Computer Simulation Contamination Cross-contamination Escherichia coli Escherichia coli Infections - prevention & control Escherichia coli O157 - growth & development Escherichia coli O157:H7 Food industries Food microbiology Food Microbiology - methods Food-Processing Industry - methods Fruit and vegetable industries Fundamental and applied biological sciences. Psychology Irradiation Lactuca - microbiology Lettuce Lettuces Mathematical modelling Mathematical models Models, Biological Models, Statistical Pathogens Risk Risk assessment Sampling plan |
| title | A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing |
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