Classification of Potato Varieties Using Isoelectrophoretic Focusing Patterns, Neural Nets, and Statistical Methods
Automatic potato variety classification by a multivariate statistical classification method and a combined neural network model is described. Both classification methods were based on digitized isoelectrophoretic patterns of the soluble tuber proteins. The statistical classification algorithm was ba...
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| Veröffentlicht in: | Journal of agricultural and food chemistry 1997-01, Vol.45 (1), p.158-161 |
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| container_title | Journal of agricultural and food chemistry |
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| creator | Jensen, Kirsten Tygesen, Thomas K Keşmir, Can Skovgaard, Ib M Søndergaard, Ib |
| description | Automatic potato variety classification by a multivariate statistical classification method and a combined neural network model is described. Both classification methods were based on digitized isoelectrophoretic patterns of the soluble tuber proteins. The statistical classification algorithm was based on a model that allows for individual stretching as well as displacement along the pH axis. The neural network architecture consisted of two layers: a self-organizing feature map and a feed-forward classifier. Twelve potato varieties were classified. The mean value of the recognition rates were 84.5 and 87.5% obtained by the statistical classification method and the neural network model, respectively. The results confirm the theory stated in earlier classification studies, that the automatic classification systems are well-established, independent of the origin of the samples, and unaffected by pattern deformations and variations in the background level of the electrophoretic gels. Keywords: Potato; isoelectric focusing; image processing; classification; neural networks |
| doi_str_mv | 10.1021/jf9602737 |
| format | Article |
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Both classification methods were based on digitized isoelectrophoretic patterns of the soluble tuber proteins. The statistical classification algorithm was based on a model that allows for individual stretching as well as displacement along the pH axis. The neural network architecture consisted of two layers: a self-organizing feature map and a feed-forward classifier. Twelve potato varieties were classified. The mean value of the recognition rates were 84.5 and 87.5% obtained by the statistical classification method and the neural network model, respectively. The results confirm the theory stated in earlier classification studies, that the automatic classification systems are well-established, independent of the origin of the samples, and unaffected by pattern deformations and variations in the background level of the electrophoretic gels. Keywords: Potato; isoelectric focusing; image processing; classification; neural networks</description><identifier>ISSN: 0021-8561</identifier><identifier>EISSN: 1520-5118</identifier><identifier>DOI: 10.1021/jf9602737</identifier><identifier>CODEN: JAFCAU</identifier><language>eng</language><publisher>Washington, DC: American Chemical Society</publisher><subject>Agronomy. Soil science and plant productions ; Biological and medical sciences ; CLASIFICACION ; CLASSIFICATION ; COMPARISONS ; COMPUTER SOFTWARE ; CULTIVARS ; Economic plant physiology ; ELECTROFORESIS ; ELECTROPHORESE ; ELECTROPHORESIS ; Food industries ; Fruit and vegetable industries ; Fundamental and applied biological sciences. Psychology ; Generalities and techniques ; ISOELECTRIC FOCUSING ; ISOELECTROFOCALISATION ; ISOELECTROFOQUE ; LOGICIEL ; METHODE STATISTIQUE ; METODOS ESTADISTICOS ; MULTIVARIATE ANALYSIS ; NEURAL NETWORKS ; PLANT PROTEINS ; PROGRAMAS DE ORDENADOR ; PROTEINAS ; PROTEINE ; PROTEINS ; SOLANUM TUBEROSUM ; STATISTICAL METHODS ; TUBERCULE ; TUBERCULO ; TUBERS ; VARIEDADES ; VARIETE ; VARIETIES</subject><ispartof>Journal of agricultural and food chemistry, 1997-01, Vol.45 (1), p.158-161</ispartof><rights>Copyright © 1997 American Chemical Society</rights><rights>1997 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a346t-7a3fb55e65faf79335e7f3f35f1cc6089ec232628f44f3af7eb312269f8cc3333</citedby><cites>FETCH-LOGICAL-a346t-7a3fb55e65faf79335e7f3f35f1cc6089ec232628f44f3af7eb312269f8cc3333</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/jf9602737$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/jf9602737$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,780,784,2765,27076,27924,27925,56738,56788</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2547206$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Jensen, Kirsten</creatorcontrib><creatorcontrib>Tygesen, Thomas K</creatorcontrib><creatorcontrib>Keşmir, Can</creatorcontrib><creatorcontrib>Skovgaard, Ib M</creatorcontrib><creatorcontrib>Søndergaard, Ib</creatorcontrib><title>Classification of Potato Varieties Using Isoelectrophoretic Focusing Patterns, Neural Nets, and Statistical Methods</title><title>Journal of agricultural and food chemistry</title><addtitle>J. Agric. Food Chem</addtitle><description>Automatic potato variety classification by a multivariate statistical classification method and a combined neural network model is described. Both classification methods were based on digitized isoelectrophoretic patterns of the soluble tuber proteins. The statistical classification algorithm was based on a model that allows for individual stretching as well as displacement along the pH axis. The neural network architecture consisted of two layers: a self-organizing feature map and a feed-forward classifier. Twelve potato varieties were classified. The mean value of the recognition rates were 84.5 and 87.5% obtained by the statistical classification method and the neural network model, respectively. The results confirm the theory stated in earlier classification studies, that the automatic classification systems are well-established, independent of the origin of the samples, and unaffected by pattern deformations and variations in the background level of the electrophoretic gels. Keywords: Potato; isoelectric focusing; image processing; classification; neural networks</description><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>CLASIFICACION</subject><subject>CLASSIFICATION</subject><subject>COMPARISONS</subject><subject>COMPUTER SOFTWARE</subject><subject>CULTIVARS</subject><subject>Economic plant physiology</subject><subject>ELECTROFORESIS</subject><subject>ELECTROPHORESE</subject><subject>ELECTROPHORESIS</subject><subject>Food industries</subject><subject>Fruit and vegetable industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Generalities and techniques</subject><subject>ISOELECTRIC FOCUSING</subject><subject>ISOELECTROFOCALISATION</subject><subject>ISOELECTROFOQUE</subject><subject>LOGICIEL</subject><subject>METHODE STATISTIQUE</subject><subject>METODOS ESTADISTICOS</subject><subject>MULTIVARIATE ANALYSIS</subject><subject>NEURAL NETWORKS</subject><subject>PLANT PROTEINS</subject><subject>PROGRAMAS DE ORDENADOR</subject><subject>PROTEINAS</subject><subject>PROTEINE</subject><subject>PROTEINS</subject><subject>SOLANUM TUBEROSUM</subject><subject>STATISTICAL METHODS</subject><subject>TUBERCULE</subject><subject>TUBERCULO</subject><subject>TUBERS</subject><subject>VARIEDADES</subject><subject>VARIETE</subject><subject>VARIETIES</subject><issn>0021-8561</issn><issn>1520-5118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNptkM1OGzEUhS3USqTQRR8AaRawqMRQ_4w9M0sUlRQptJFCqu6si7HBIYwjX0eCt-fSqbKqN1f2-Xx8fBj7IviF4FJ8W4fecNmq9oBNhJa81kJ0H9iEk1h32ohD9glxzTnvdMsnDKcbQIwhOigxDVUK1SIVKKn6DTn6Ej1WK4zDQ3WNyW-8KzltH1MmxVVXye3-agsoxecBz6uffpdhQ6PQBob7aklmEYmm0xtfHtM9HrOPATboP_-bR2x19f12-qOe_5pdTy_nNajGlLoFFe609kYHCG2vlPZtUEHpIJwzvOu9k0oa2YWmCYoQf6eElKYPnXOK1hH7Ovq6nBCzD3ab4zPkVyu4fW_L7tsi9nRkt4AUNWQYXMT9BambVnJDWD1i9CX_spchP1lDNtreLpb25k-vm5me2TnxJyMfIFl4yGS5Woq-bznllu_Pno06OLTrtMsDFfKfeG_aiY6a</recordid><startdate>19970101</startdate><enddate>19970101</enddate><creator>Jensen, Kirsten</creator><creator>Tygesen, Thomas K</creator><creator>Keşmir, Can</creator><creator>Skovgaard, Ib M</creator><creator>Søndergaard, Ib</creator><general>American Chemical Society</general><scope>FBQ</scope><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19970101</creationdate><title>Classification of Potato Varieties Using Isoelectrophoretic Focusing Patterns, Neural Nets, and Statistical Methods</title><author>Jensen, Kirsten ; Tygesen, Thomas K ; Keşmir, Can ; Skovgaard, Ib M ; Søndergaard, Ib</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a346t-7a3fb55e65faf79335e7f3f35f1cc6089ec232628f44f3af7eb312269f8cc3333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>CLASIFICACION</topic><topic>CLASSIFICATION</topic><topic>COMPARISONS</topic><topic>COMPUTER SOFTWARE</topic><topic>CULTIVARS</topic><topic>Economic plant physiology</topic><topic>ELECTROFORESIS</topic><topic>ELECTROPHORESE</topic><topic>ELECTROPHORESIS</topic><topic>Food industries</topic><topic>Fruit and vegetable industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Generalities and techniques</topic><topic>ISOELECTRIC FOCUSING</topic><topic>ISOELECTROFOCALISATION</topic><topic>ISOELECTROFOQUE</topic><topic>LOGICIEL</topic><topic>METHODE STATISTIQUE</topic><topic>METODOS ESTADISTICOS</topic><topic>MULTIVARIATE ANALYSIS</topic><topic>NEURAL NETWORKS</topic><topic>PLANT PROTEINS</topic><topic>PROGRAMAS DE ORDENADOR</topic><topic>PROTEINAS</topic><topic>PROTEINE</topic><topic>PROTEINS</topic><topic>SOLANUM TUBEROSUM</topic><topic>STATISTICAL METHODS</topic><topic>TUBERCULE</topic><topic>TUBERCULO</topic><topic>TUBERS</topic><topic>VARIEDADES</topic><topic>VARIETE</topic><topic>VARIETIES</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jensen, Kirsten</creatorcontrib><creatorcontrib>Tygesen, Thomas K</creatorcontrib><creatorcontrib>Keşmir, Can</creatorcontrib><creatorcontrib>Skovgaard, Ib M</creatorcontrib><creatorcontrib>Søndergaard, Ib</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>Journal of agricultural and food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jensen, Kirsten</au><au>Tygesen, Thomas K</au><au>Keşmir, Can</au><au>Skovgaard, Ib M</au><au>Søndergaard, Ib</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classification of Potato Varieties Using Isoelectrophoretic Focusing Patterns, Neural Nets, and Statistical Methods</atitle><jtitle>Journal of agricultural and food chemistry</jtitle><addtitle>J. Agric. Food Chem</addtitle><date>1997-01-01</date><risdate>1997</risdate><volume>45</volume><issue>1</issue><spage>158</spage><epage>161</epage><pages>158-161</pages><issn>0021-8561</issn><eissn>1520-5118</eissn><coden>JAFCAU</coden><abstract>Automatic potato variety classification by a multivariate statistical classification method and a combined neural network model is described. Both classification methods were based on digitized isoelectrophoretic patterns of the soluble tuber proteins. The statistical classification algorithm was based on a model that allows for individual stretching as well as displacement along the pH axis. The neural network architecture consisted of two layers: a self-organizing feature map and a feed-forward classifier. Twelve potato varieties were classified. The mean value of the recognition rates were 84.5 and 87.5% obtained by the statistical classification method and the neural network model, respectively. The results confirm the theory stated in earlier classification studies, that the automatic classification systems are well-established, independent of the origin of the samples, and unaffected by pattern deformations and variations in the background level of the electrophoretic gels. Keywords: Potato; isoelectric focusing; image processing; classification; neural networks</abstract><cop>Washington, DC</cop><pub>American Chemical Society</pub><doi>10.1021/jf9602737</doi><tpages>4</tpages></addata></record> |
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| source | American Chemical Society Journals |
| subjects | Agronomy. Soil science and plant productions Biological and medical sciences CLASIFICACION CLASSIFICATION COMPARISONS COMPUTER SOFTWARE CULTIVARS Economic plant physiology ELECTROFORESIS ELECTROPHORESE ELECTROPHORESIS Food industries Fruit and vegetable industries Fundamental and applied biological sciences. Psychology Generalities and techniques ISOELECTRIC FOCUSING ISOELECTROFOCALISATION ISOELECTROFOQUE LOGICIEL METHODE STATISTIQUE METODOS ESTADISTICOS MULTIVARIATE ANALYSIS NEURAL NETWORKS PLANT PROTEINS PROGRAMAS DE ORDENADOR PROTEINAS PROTEINE PROTEINS SOLANUM TUBEROSUM STATISTICAL METHODS TUBERCULE TUBERCULO TUBERS VARIEDADES VARIETE VARIETIES |
| title | Classification of Potato Varieties Using Isoelectrophoretic Focusing Patterns, Neural Nets, and Statistical Methods |
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