Mathematical Approaches To Estimating Lag-Phase Duration and Growth Rate for Predicting Growth of Salmonella Serovars, Escherichia coli O157:H7, and Staphylococcus aureus in Raw Beef, Bratwurst, and Poultry
This study was done to optimize accuracy of predicting growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw beef, poultry, and bratwurst (with salt but without added nitrite). Four mathematical approaches were used with experimentally determine...
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| Veröffentlicht in: | Journal of food protection 2009-06, Vol.72 (6), p.1190-1200 |
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| description | This study was done to optimize accuracy of predicting growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw beef, poultry, and bratwurst (with salt but without added nitrite). Four mathematical approaches were used with experimentally determined lag-phase duration (LPD) and growth rate (GR) values to develop 12 versions of THERM (Temperature History Evaluation for Raw Meats; http://www.meathaccp.wisc.edu/THERM/calc.aspx), a computer-based tool that calculates elapsing lag phase or growth that occurs in each entered time interval and sums the results of all intervals to predict growth. Each THERM version utilized LPD values calculated by linear interpolation, quadratic equation, piecewise linear regression, or exponential decay curve and GR values calculated by linear interpolation, quadratic equation, or piecewise linear regression. Each combination of mathematical approaches for LPD and GR calculations was defined as another THERM version. Time, temperature, and pathogen level (log CFU per gram) data were obtained from 26 inoculation experiments with ground beef, pork sausages, and poultry. Time and temperature data were entered into the 12 THERM versions to obtain pathogen growth. Predicted and experimental results were qualitatively described and compared (growth defined as >0.3-log increase) or quantitatively compared. The 12 THERM versions had qualitative accuracies of 81.4 to 88.6% across 70 combinations of product, pathogen, and experiment. Quantitative accuracies within ±0.3 log CFU were obtained for 51.4 to 67.2% of the experimental combinations; 82.9 to 88.6% of the quantitative predictions were accurate or fail-safe. Piecewise linear regression or linear interpolation for calculating LPD and GR yielded the most accurate THERM performance. |
| doi_str_mv | 10.4315/0362-028X-72.6.1190 |
| format | Article |
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Four mathematical approaches were used with experimentally determined lag-phase duration (LPD) and growth rate (GR) values to develop 12 versions of THERM (Temperature History Evaluation for Raw Meats; http://www.meathaccp.wisc.edu/THERM/calc.aspx), a computer-based tool that calculates elapsing lag phase or growth that occurs in each entered time interval and sums the results of all intervals to predict growth. Each THERM version utilized LPD values calculated by linear interpolation, quadratic equation, piecewise linear regression, or exponential decay curve and GR values calculated by linear interpolation, quadratic equation, or piecewise linear regression. Each combination of mathematical approaches for LPD and GR calculations was defined as another THERM version. Time, temperature, and pathogen level (log CFU per gram) data were obtained from 26 inoculation experiments with ground beef, pork sausages, and poultry. Time and temperature data were entered into the 12 THERM versions to obtain pathogen growth. Predicted and experimental results were qualitatively described and compared (growth defined as >0.3-log increase) or quantitatively compared. The 12 THERM versions had qualitative accuracies of 81.4 to 88.6% across 70 combinations of product, pathogen, and experiment. Quantitative accuracies within ±0.3 log CFU were obtained for 51.4 to 67.2% of the experimental combinations; 82.9 to 88.6% of the quantitative predictions were accurate or fail-safe. Piecewise linear regression or linear interpolation for calculating LPD and GR yielded the most accurate THERM performance.</description><identifier>ISSN: 0362-028X</identifier><identifier>EISSN: 1944-9097</identifier><identifier>DOI: 10.4315/0362-028X-72.6.1190</identifier><identifier>PMID: 19610329</identifier><identifier>CODEN: JFPRDR</identifier><language>eng</language><publisher>Des Moines, IA: International Association for Food Protection</publisher><subject>accuracy ; Animals ; Bacteria ; bacterial contamination ; Beef ; Biological and medical sciences ; Colony Count, Microbial ; cured meats ; E coli ; equations ; Escherichia coli ; Escherichia coli O157 - growth & development ; Escherichia coli O157:H7 ; food contamination ; Food Contamination - analysis ; Food industries ; Food microbiology ; food pathogens ; Food safety ; food storage ; Fundamental and applied biological sciences. Psychology ; ground beef ; Growth models ; Humans ; Kinetics ; Laboratories ; Linear Models ; Mathematical functions ; mathematical models ; Meat - microbiology ; Meat and meat product industries ; Meat industry ; Meat processing ; Meat Products - microbiology ; microbial growth ; Models, Biological ; Pathogens ; pork ; Poultry ; Poultry - microbiology ; poultry meat ; predictive microbiology ; Predictive Value of Tests ; processed foods ; quantitative analysis ; raw meat ; Regression analysis ; Salmonella ; Salmonella - growth & development ; sausages ; sodium chloride ; sodium nitrite ; Staphylococcus aureus ; Staphylococcus aureus - growth & development ; Staphylococcus infections ; Temperature ; Time Factors</subject><ispartof>Journal of food protection, 2009-06, Vol.72 (6), p.1190-1200</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright International Association for Food Protection Jun 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c461t-f6e7354c7937760cca2922387dbcbe2a79eac9f3cde076ac86043bdc239271ef3</citedby><cites>FETCH-LOGICAL-c461t-f6e7354c7937760cca2922387dbcbe2a79eac9f3cde076ac86043bdc239271ef3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21650305$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19610329$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Borneman, Darand L</creatorcontrib><creatorcontrib>Ingham, Steven C</creatorcontrib><creatorcontrib>Ané, Cécile</creatorcontrib><title>Mathematical Approaches To Estimating Lag-Phase Duration and Growth Rate for Predicting Growth of Salmonella Serovars, Escherichia coli O157:H7, and Staphylococcus aureus in Raw Beef, Bratwurst, and Poultry</title><title>Journal of food protection</title><addtitle>J Food Prot</addtitle><description>This study was done to optimize accuracy of predicting growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw beef, poultry, and bratwurst (with salt but without added nitrite). Four mathematical approaches were used with experimentally determined lag-phase duration (LPD) and growth rate (GR) values to develop 12 versions of THERM (Temperature History Evaluation for Raw Meats; http://www.meathaccp.wisc.edu/THERM/calc.aspx), a computer-based tool that calculates elapsing lag phase or growth that occurs in each entered time interval and sums the results of all intervals to predict growth. Each THERM version utilized LPD values calculated by linear interpolation, quadratic equation, piecewise linear regression, or exponential decay curve and GR values calculated by linear interpolation, quadratic equation, or piecewise linear regression. Each combination of mathematical approaches for LPD and GR calculations was defined as another THERM version. Time, temperature, and pathogen level (log CFU per gram) data were obtained from 26 inoculation experiments with ground beef, pork sausages, and poultry. Time and temperature data were entered into the 12 THERM versions to obtain pathogen growth. Predicted and experimental results were qualitatively described and compared (growth defined as >0.3-log increase) or quantitatively compared. The 12 THERM versions had qualitative accuracies of 81.4 to 88.6% across 70 combinations of product, pathogen, and experiment. Quantitative accuracies within ±0.3 log CFU were obtained for 51.4 to 67.2% of the experimental combinations; 82.9 to 88.6% of the quantitative predictions were accurate or fail-safe. Piecewise linear regression or linear interpolation for calculating LPD and GR yielded the most accurate THERM performance.</description><subject>accuracy</subject><subject>Animals</subject><subject>Bacteria</subject><subject>bacterial contamination</subject><subject>Beef</subject><subject>Biological and medical sciences</subject><subject>Colony Count, Microbial</subject><subject>cured meats</subject><subject>E coli</subject><subject>equations</subject><subject>Escherichia coli</subject><subject>Escherichia coli O157 - growth & development</subject><subject>Escherichia coli O157:H7</subject><subject>food contamination</subject><subject>Food Contamination - analysis</subject><subject>Food industries</subject><subject>Food microbiology</subject><subject>food pathogens</subject><subject>Food safety</subject><subject>food storage</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>ground beef</subject><subject>Growth models</subject><subject>Humans</subject><subject>Kinetics</subject><subject>Laboratories</subject><subject>Linear Models</subject><subject>Mathematical functions</subject><subject>mathematical models</subject><subject>Meat - microbiology</subject><subject>Meat and meat product industries</subject><subject>Meat industry</subject><subject>Meat processing</subject><subject>Meat Products - microbiology</subject><subject>microbial growth</subject><subject>Models, Biological</subject><subject>Pathogens</subject><subject>pork</subject><subject>Poultry</subject><subject>Poultry - microbiology</subject><subject>poultry meat</subject><subject>predictive microbiology</subject><subject>Predictive Value of Tests</subject><subject>processed foods</subject><subject>quantitative analysis</subject><subject>raw meat</subject><subject>Regression analysis</subject><subject>Salmonella</subject><subject>Salmonella - growth & development</subject><subject>sausages</subject><subject>sodium chloride</subject><subject>sodium nitrite</subject><subject>Staphylococcus aureus</subject><subject>Staphylococcus aureus - growth & development</subject><subject>Staphylococcus infections</subject><subject>Temperature</subject><subject>Time Factors</subject><issn>0362-028X</issn><issn>1944-9097</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNpdkd9u0zAUxi0EYmXwBEhgISFumuJ_sevdbWNsSEWr6CZxZ506TpMpjYudUPUleSacNRoSV0fy-X3fOT4fQm8pmQlO88-ES5YRNv-ZKTaTM0o1eYYmVAuRaaLVczR5Ik7QqxgfCCFMM_kSnVAtKeFMT9Cf79BVbgtdbaHB57td8GArF_Gdx1exq4dOu8EL2GTLCqLDX_qQnnyLoS3wdfD7rsI_oHO49AEvgytq-6gYW77EK2i2vnVNA3jlgv8NIU6Td5oSalvVgK1vanxLc3V2o6aPvqsOdtWh8dZb20cMfXCp1G2atMcXzpVTfJHW2PchdkfF0vdNFw6v0YsSmujejPUU3X-9uru8yRa3198uzxeZFZJ2WSmd4rmwSnOlJLEW0mEYn6tibdeOgdIOrC65LRxREuxcEsHXhWVcM0VdyU_Rp6Nvutev3sXObOtohz-2zvfRKCEUVUqLRH74j3zwfWjTcoZxypmaz0mC-BGywccYXGl2IZ0-HAwlZgjbDFGaIUqjmJFmCDup3o3W_Xrrin-aMd0EfBwBiCneMkBr6_jEMSpzwkmeuPdHrgRvYBMSc79ihHJCpUiDBf8LJBK9DA</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>Borneman, Darand L</creator><creator>Ingham, Steven C</creator><creator>Ané, Cécile</creator><general>International Association for Food Protection</general><general>Elsevier Limited</general><scope>FBQ</scope><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>3V.</scope><scope>7RQ</scope><scope>7WY</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>883</scope><scope>88E</scope><scope>88I</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0F</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7QL</scope><scope>7T2</scope><scope>7U2</scope><scope>C1K</scope></search><sort><creationdate>20090601</creationdate><title>Mathematical Approaches To Estimating Lag-Phase Duration and Growth Rate for Predicting Growth of Salmonella Serovars, Escherichia coli O157:H7, and Staphylococcus aureus in Raw Beef, Bratwurst, and Poultry</title><author>Borneman, Darand L ; Ingham, Steven C ; Ané, Cécile</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c461t-f6e7354c7937760cca2922387dbcbe2a79eac9f3cde076ac86043bdc239271ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>accuracy</topic><topic>Animals</topic><topic>Bacteria</topic><topic>bacterial contamination</topic><topic>Beef</topic><topic>Biological and medical sciences</topic><topic>Colony Count, Microbial</topic><topic>cured meats</topic><topic>E coli</topic><topic>equations</topic><topic>Escherichia coli</topic><topic>Escherichia coli O157 - growth & development</topic><topic>Escherichia coli O157:H7</topic><topic>food contamination</topic><topic>Food Contamination - analysis</topic><topic>Food industries</topic><topic>Food microbiology</topic><topic>food pathogens</topic><topic>Food safety</topic><topic>food storage</topic><topic>Fundamental and applied biological sciences. 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growth & development</topic><topic>Staphylococcus infections</topic><topic>Temperature</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Borneman, Darand L</creatorcontrib><creatorcontrib>Ingham, Steven C</creatorcontrib><creatorcontrib>Ané, Cécile</creatorcontrib><collection>AGRIS</collection><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>ProQuest Central (Corporate)</collection><collection>Career & Technical Education Database</collection><collection>ABI/INFORM Collection</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Trade & Industry (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Trade & Industry</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Journal of food protection</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Borneman, Darand L</au><au>Ingham, Steven C</au><au>Ané, Cécile</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mathematical Approaches To Estimating Lag-Phase Duration and Growth Rate for Predicting Growth of Salmonella Serovars, Escherichia coli O157:H7, and Staphylococcus aureus in Raw Beef, Bratwurst, and Poultry</atitle><jtitle>Journal of food protection</jtitle><addtitle>J Food Prot</addtitle><date>2009-06-01</date><risdate>2009</risdate><volume>72</volume><issue>6</issue><spage>1190</spage><epage>1200</epage><pages>1190-1200</pages><issn>0362-028X</issn><eissn>1944-9097</eissn><coden>JFPRDR</coden><abstract>This study was done to optimize accuracy of predicting growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw beef, poultry, and bratwurst (with salt but without added nitrite). Four mathematical approaches were used with experimentally determined lag-phase duration (LPD) and growth rate (GR) values to develop 12 versions of THERM (Temperature History Evaluation for Raw Meats; http://www.meathaccp.wisc.edu/THERM/calc.aspx), a computer-based tool that calculates elapsing lag phase or growth that occurs in each entered time interval and sums the results of all intervals to predict growth. Each THERM version utilized LPD values calculated by linear interpolation, quadratic equation, piecewise linear regression, or exponential decay curve and GR values calculated by linear interpolation, quadratic equation, or piecewise linear regression. Each combination of mathematical approaches for LPD and GR calculations was defined as another THERM version. Time, temperature, and pathogen level (log CFU per gram) data were obtained from 26 inoculation experiments with ground beef, pork sausages, and poultry. Time and temperature data were entered into the 12 THERM versions to obtain pathogen growth. Predicted and experimental results were qualitatively described and compared (growth defined as >0.3-log increase) or quantitatively compared. The 12 THERM versions had qualitative accuracies of 81.4 to 88.6% across 70 combinations of product, pathogen, and experiment. Quantitative accuracies within ±0.3 log CFU were obtained for 51.4 to 67.2% of the experimental combinations; 82.9 to 88.6% of the quantitative predictions were accurate or fail-safe. Piecewise linear regression or linear interpolation for calculating LPD and GR yielded the most accurate THERM performance.</abstract><cop>Des Moines, IA</cop><pub>International Association for Food Protection</pub><pmid>19610329</pmid><doi>10.4315/0362-028X-72.6.1190</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | accuracy Animals Bacteria bacterial contamination Beef Biological and medical sciences Colony Count, Microbial cured meats E coli equations Escherichia coli Escherichia coli O157 - growth & development Escherichia coli O157:H7 food contamination Food Contamination - analysis Food industries Food microbiology food pathogens Food safety food storage Fundamental and applied biological sciences. Psychology ground beef Growth models Humans Kinetics Laboratories Linear Models Mathematical functions mathematical models Meat - microbiology Meat and meat product industries Meat industry Meat processing Meat Products - microbiology microbial growth Models, Biological Pathogens pork Poultry Poultry - microbiology poultry meat predictive microbiology Predictive Value of Tests processed foods quantitative analysis raw meat Regression analysis Salmonella Salmonella - growth & development sausages sodium chloride sodium nitrite Staphylococcus aureus Staphylococcus aureus - growth & development Staphylococcus infections Temperature Time Factors |
| title | Mathematical Approaches To Estimating Lag-Phase Duration and Growth Rate for Predicting Growth of Salmonella Serovars, Escherichia coli O157:H7, and Staphylococcus aureus in Raw Beef, Bratwurst, and Poultry |
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