Data model for the elimination of matrix effects in enzyme-based flow-injection systems
This contribution presents a new conceptional enzyme‐based flow injection analysis (FIA) system for the process and quality control of food processing and biotechnological systems. It provides the determination of different analytes in distinct process media on the base of a common experimental set‐...
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| Veröffentlicht in: | Biotechnology and bioengineering 2000-08, Vol.69 (4), p.377-384 |
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| creator | Becker, Thomas Matthias Schmidt, Hanns-Ludwig |
| description | This contribution presents a new conceptional enzyme‐based flow injection analysis (FIA) system for the process and quality control of food processing and biotechnological systems. It provides the determination of different analytes in distinct process media on the base of a common experimental set‐up. In contrast to known comparable systems, analysis is performed without the commonly used sample preparation and dilution steps. Instead, the adaptation to the necessary measurement range is realized by optimization of intrinsic system parameters. The central principle of the work presented is the elimination of occurring interferences by the heterogenous matrix of the process sample. Based on a particular injection mode, the application of dehydrogenases as indicator enzymes and a specially developed data model using cognitive methods, cross sensitivities of the detector as well as disturbed reaction rates of the enzymes could be almost completely compensated. Two applications are presented, the analysis of ethanol in non‐alcoholic beer and the online determination of D‐/L‐lactate during a lactic acid fermentation, which reveal the advantage of the developed system. © 2000 John Wiley & Sons, Inc. Biotechnol Bioeng 69: 377–384, 2000. |
| doi_str_mv | 10.1002/1097-0290(20000820)69:4<377::AID-BIT4>3.0.CO;2-O |
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It provides the determination of different analytes in distinct process media on the base of a common experimental set‐up. In contrast to known comparable systems, analysis is performed without the commonly used sample preparation and dilution steps. Instead, the adaptation to the necessary measurement range is realized by optimization of intrinsic system parameters. The central principle of the work presented is the elimination of occurring interferences by the heterogenous matrix of the process sample. Based on a particular injection mode, the application of dehydrogenases as indicator enzymes and a specially developed data model using cognitive methods, cross sensitivities of the detector as well as disturbed reaction rates of the enzymes could be almost completely compensated. Two applications are presented, the analysis of ethanol in non‐alcoholic beer and the online determination of D‐/L‐lactate during a lactic acid fermentation, which reveal the advantage of the developed system. © 2000 John Wiley & Sons, Inc. Biotechnol Bioeng 69: 377–384, 2000.</description><identifier>ISSN: 0006-3592</identifier><identifier>EISSN: 1097-0290</identifier><identifier>DOI: 10.1002/1097-0290(20000820)69:4<377::AID-BIT4>3.0.CO;2-O</identifier><identifier>PMID: 10862676</identifier><identifier>CODEN: BIBIAU</identifier><language>eng</language><publisher>New York: John Wiley & Sons, Inc</publisher><subject>Alanine Transaminase - chemistry ; Alcohol Dehydrogenase - chemistry ; Algorithms ; Animals ; artificial neural network ; Automatic Data Processing ; Beer - analysis ; Biological and medical sciences ; Bioreactors ; Biotechnology ; Cognitive systems ; dehydrogenase ; Enzymes ; Enzymes - chemistry ; enzymology ; Ethanol ; Ethanol - analysis ; Fermentation ; flow-injection analysis ; Food Analysis ; Fundamental and applied biological sciences. Psychology ; Fuzzy Logic ; Fuzzy sets ; Horses ; In Vitro Techniques ; L-Lactate Dehydrogenase - chemistry ; lactic acid ; Lactic Acid - metabolism ; Leuconostoc - chemistry ; Methods. Procedures. 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Bioeng</addtitle><description>This contribution presents a new conceptional enzyme‐based flow injection analysis (FIA) system for the process and quality control of food processing and biotechnological systems. It provides the determination of different analytes in distinct process media on the base of a common experimental set‐up. In contrast to known comparable systems, analysis is performed without the commonly used sample preparation and dilution steps. Instead, the adaptation to the necessary measurement range is realized by optimization of intrinsic system parameters. The central principle of the work presented is the elimination of occurring interferences by the heterogenous matrix of the process sample. Based on a particular injection mode, the application of dehydrogenases as indicator enzymes and a specially developed data model using cognitive methods, cross sensitivities of the detector as well as disturbed reaction rates of the enzymes could be almost completely compensated. Two applications are presented, the analysis of ethanol in non‐alcoholic beer and the online determination of D‐/L‐lactate during a lactic acid fermentation, which reveal the advantage of the developed system. © 2000 John Wiley & Sons, Inc. Biotechnol Bioeng 69: 377–384, 2000.</description><subject>Alanine Transaminase - chemistry</subject><subject>Alcohol Dehydrogenase - chemistry</subject><subject>Algorithms</subject><subject>Animals</subject><subject>artificial neural network</subject><subject>Automatic Data Processing</subject><subject>Beer - analysis</subject><subject>Biological and medical sciences</subject><subject>Bioreactors</subject><subject>Biotechnology</subject><subject>Cognitive systems</subject><subject>dehydrogenase</subject><subject>Enzymes</subject><subject>Enzymes - chemistry</subject><subject>enzymology</subject><subject>Ethanol</subject><subject>Ethanol - analysis</subject><subject>Fermentation</subject><subject>flow-injection analysis</subject><subject>Food Analysis</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Fuzzy Logic</subject><subject>Fuzzy sets</subject><subject>Horses</subject><subject>In Vitro Techniques</subject><subject>L-Lactate Dehydrogenase - chemistry</subject><subject>lactic acid</subject><subject>Lactic Acid - metabolism</subject><subject>Leuconostoc - chemistry</subject><subject>Methods. Procedures. Technologies</subject><subject>Models, Biological</subject><subject>Models, Theoretical</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Others</subject><subject>Parameter estimation</subject><subject>process control</subject><subject>Pseudomonas putida - chemistry</subject><subject>Rabbits</subject><subject>Systems analysis</subject><subject>Various methods and equipments</subject><issn>0006-3592</issn><issn>1097-0290</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqVkV1v0zAUhiMEYmXwF1AuEIKLFH_FdjqENDpWKlULSEOVenN0mtjCIx8jTrWVX49DyuACacI31pGf855jPVGkKZlSQtgbSjKVEJaRV4yEoxl5LbOZeMuVms1Ol2fJ--WleMenZDrPT1iSP4gmdy0Po0lokQlPM3YUPfH-KpRKS_k4OqJESyaVnETrM-wxrtvSVLFtu7j_amJTudo12Lu2iVsb19h37jY21pqi97FrYtP82Ncm2aI3ZWyr9iZxzVV4HBr83vem9k-jRxYrb54d7uPoy_mHy_nHZJUvlvPTVVIILkXCGS1QqS1uUxSUG8GFttoKZWlRppRKTFGhkJlMS4F8W5Ys1YiotFEF1ciPo5dj7nXXft8Z30PtfGGqChvT7jwoyggjXN0LMipSTjS5F6RKypRoGsBPI1h0rfedsXDduRq7PVACgz4YXMDgAn7rA5mBgKAPIOiDQR9wIDDPgUEeIp8fZu-2tSn_Chx9BeDFAUBfYGU7bArn_3BCDn8N2OcRu3GV2f_XXv9Y61cdMpMx0wXBt3eZ2H0DqbhKYX2xgGyx2WwuVms45z8B4NjODw</recordid><startdate>20000820</startdate><enddate>20000820</enddate><creator>Becker, Thomas Matthias</creator><creator>Schmidt, Hanns-Ludwig</creator><general>John Wiley & Sons, Inc</general><general>Wiley</general><scope>BSCLL</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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20000820</creationdate><title>Data model for the elimination of matrix effects in enzyme-based flow-injection systems</title><author>Becker, Thomas Matthias ; Schmidt, Hanns-Ludwig</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4364-321ca77bab5a413e4348f8f47f1cd5116a5a7a46965d4a3bdd258aaa78e7c18a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Alanine Transaminase - chemistry</topic><topic>Alcohol Dehydrogenase - chemistry</topic><topic>Algorithms</topic><topic>Animals</topic><topic>artificial neural network</topic><topic>Automatic Data Processing</topic><topic>Beer - analysis</topic><topic>Biological and medical sciences</topic><topic>Bioreactors</topic><topic>Biotechnology</topic><topic>Cognitive systems</topic><topic>dehydrogenase</topic><topic>Enzymes</topic><topic>Enzymes - chemistry</topic><topic>enzymology</topic><topic>Ethanol</topic><topic>Ethanol - analysis</topic><topic>Fermentation</topic><topic>flow-injection analysis</topic><topic>Food Analysis</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Fuzzy Logic</topic><topic>Fuzzy sets</topic><topic>Horses</topic><topic>In Vitro Techniques</topic><topic>L-Lactate Dehydrogenase - chemistry</topic><topic>lactic acid</topic><topic>Lactic Acid - metabolism</topic><topic>Leuconostoc - chemistry</topic><topic>Methods. Procedures. Technologies</topic><topic>Models, Biological</topic><topic>Models, Theoretical</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Others</topic><topic>Parameter estimation</topic><topic>process control</topic><topic>Pseudomonas putida - chemistry</topic><topic>Rabbits</topic><topic>Systems analysis</topic><topic>Various methods and equipments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Becker, Thomas Matthias</creatorcontrib><creatorcontrib>Schmidt, Hanns-Ludwig</creatorcontrib><collection>Istex</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>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Biotechnology and bioengineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Becker, Thomas Matthias</au><au>Schmidt, Hanns-Ludwig</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data model for the elimination of matrix effects in enzyme-based flow-injection systems</atitle><jtitle>Biotechnology and bioengineering</jtitle><addtitle>Biotechnol. Bioeng</addtitle><date>2000-08-20</date><risdate>2000</risdate><volume>69</volume><issue>4</issue><spage>377</spage><epage>384</epage><pages>377-384</pages><issn>0006-3592</issn><eissn>1097-0290</eissn><coden>BIBIAU</coden><abstract>This contribution presents a new conceptional enzyme‐based flow injection analysis (FIA) system for the process and quality control of food processing and biotechnological systems. It provides the determination of different analytes in distinct process media on the base of a common experimental set‐up. In contrast to known comparable systems, analysis is performed without the commonly used sample preparation and dilution steps. Instead, the adaptation to the necessary measurement range is realized by optimization of intrinsic system parameters. The central principle of the work presented is the elimination of occurring interferences by the heterogenous matrix of the process sample. Based on a particular injection mode, the application of dehydrogenases as indicator enzymes and a specially developed data model using cognitive methods, cross sensitivities of the detector as well as disturbed reaction rates of the enzymes could be almost completely compensated. Two applications are presented, the analysis of ethanol in non‐alcoholic beer and the online determination of D‐/L‐lactate during a lactic acid fermentation, which reveal the advantage of the developed system. © 2000 John Wiley & Sons, Inc. Biotechnol Bioeng 69: 377–384, 2000.</abstract><cop>New York</cop><pub>John Wiley & Sons, Inc</pub><pmid>10862676</pmid><doi>10.1002/1097-0290(20000820)69:4<377::AID-BIT4>3.0.CO;2-O</doi><tpages>8</tpages></addata></record> |
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| subjects | Alanine Transaminase - chemistry Alcohol Dehydrogenase - chemistry Algorithms Animals artificial neural network Automatic Data Processing Beer - analysis Biological and medical sciences Bioreactors Biotechnology Cognitive systems dehydrogenase Enzymes Enzymes - chemistry enzymology Ethanol Ethanol - analysis Fermentation flow-injection analysis Food Analysis Fundamental and applied biological sciences. Psychology Fuzzy Logic Fuzzy sets Horses In Vitro Techniques L-Lactate Dehydrogenase - chemistry lactic acid Lactic Acid - metabolism Leuconostoc - chemistry Methods. Procedures. Technologies Models, Biological Models, Theoretical Neural networks Optimization Others Parameter estimation process control Pseudomonas putida - chemistry Rabbits Systems analysis Various methods and equipments |
| title | Data model for the elimination of matrix effects in enzyme-based flow-injection systems |
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