Predictive microbiology: providing a knowledge-based framework for change management
This contribution considers predictive microbiology in the context of the Food Micro 2002 theme, “Microbial adaptation to changing environments”. To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demograph...
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description | This contribution considers predictive microbiology in the context of the Food Micro 2002 theme, “Microbial adaptation to changing environments”. To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of predictive microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the “clean, green” aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. The challenge is to demonstrate the validity of such models and to develop applications of benefit to the food industry and consumers as was achieved with growth models to predict shelf life and the hygienic equivalence of food processing operations. |
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To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of predictive microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the “clean, green” aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. 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To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of predictive microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the “clean, green” aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. The challenge is to demonstrate the validity of such models and to develop applications of benefit to the food industry and consumers as was achieved with growth models to predict shelf life and the hygienic equivalence of food processing operations.</description><subject>Bacteria - growth & development</subject><subject>Biological and medical sciences</subject><subject>Consumer Product Safety</subject><subject>Decision Trees</subject><subject>Food industries</subject><subject>Food Microbiology</subject><subject>Food Preservation</subject><subject>Forecasting</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Hazard analysis critical control point</subject><subject>Humans</subject><subject>Models, Biological</subject><subject>Models, Theoretical</subject><subject>Predictive microbiology</subject><subject>Risk Assessment</subject><issn>0168-1605</issn><issn>1879-3460</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU9P3DAQxa2qqCzQj9AqlyJ6CIz_JQ4XhFBbkJBaqfRsOfYkdUlisLOL-PZ42RUc6Rw8l98bP71HyCcKxxRodfI7P6qkFcgjYF8BGKclf0cWVNVNyUUF78niBdkleyn9AwDJOXwgu5TlqTgsyM2viM7b2a-wGL2NofVhCP3jaXEXw8o7P_WFKW6n8DCg67FsTUJXdNGM-BDibdGFWNi_Zuqz3EymxxGn-YDsdGZI-HG798mf799uLi7L658_ri7Or0srlJhLYZQDJ61wHcNWdJ1TKB2aWhgEi7zirELH104pgqTCOctsI7LIVrQ1fJ8cbu5mr_dLTLMefbI4DGbCsEyaNbWqQdb_AUohhGreBKmSqoEGMig3YI4spYidvot-NPFRU9DrgvRzQXqdvgamnwvSPOs-bz9YtiO6V9W2kQx82QImWTPkpCfr0yvHlayYWB8623CYA155jDpZj5PNbUa0s3bBv2HlCex8rcg</recordid><startdate>20020915</startdate><enddate>20020915</enddate><creator>McMeekin, T.A</creator><creator>Ross, T</creator><general>Elsevier B.V</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>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>7TB</scope><scope>F28</scope></search><sort><creationdate>20020915</creationdate><title>Predictive microbiology: providing a knowledge-based framework for change management</title><author>McMeekin, T.A ; Ross, T</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c484t-4a8d0d5c4df2eb4ffd8e5dea74ae0ce36326ed322631e0514ddc2c94d0dc61ba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Bacteria - growth & development</topic><topic>Biological and medical sciences</topic><topic>Consumer Product Safety</topic><topic>Decision Trees</topic><topic>Food industries</topic><topic>Food Microbiology</topic><topic>Food Preservation</topic><topic>Forecasting</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Hazard analysis critical control point</topic><topic>Humans</topic><topic>Models, Biological</topic><topic>Models, Theoretical</topic><topic>Predictive microbiology</topic><topic>Risk Assessment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McMeekin, T.A</creatorcontrib><creatorcontrib>Ross, T</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>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>International journal of food microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McMeekin, T.A</au><au>Ross, T</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive microbiology: providing a knowledge-based framework for change management</atitle><jtitle>International journal of food microbiology</jtitle><addtitle>Int J Food Microbiol</addtitle><date>2002-09-15</date><risdate>2002</risdate><volume>78</volume><issue>1</issue><spage>133</spage><epage>153</epage><pages>133-153</pages><issn>0168-1605</issn><eissn>1879-3460</eissn><coden>IJFMDD</coden><abstract>This contribution considers predictive microbiology in the context of the Food Micro 2002 theme, “Microbial adaptation to changing environments”. To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of predictive microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the “clean, green” aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. The challenge is to demonstrate the validity of such models and to develop applications of benefit to the food industry and consumers as was achieved with growth models to predict shelf life and the hygienic equivalence of food processing operations.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>12222630</pmid><doi>10.1016/S0168-1605(02)00231-3</doi><tpages>21</tpages></addata></record> |
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subjects | Bacteria - growth & development Biological and medical sciences Consumer Product Safety Decision Trees Food industries Food Microbiology Food Preservation Forecasting Fundamental and applied biological sciences. Psychology Hazard analysis critical control point Humans Models, Biological Models, Theoretical Predictive microbiology Risk Assessment |
title | Predictive microbiology: providing a knowledge-based framework for change management |
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