Estimation of Oil Content and Fatty Acid Composition in Cottonseed Kernel Powder Using Near Infrared Reflectance Spectroscopy
Oil content and fatty acid composition in 444 ground cottonseed kernel samples were analyzed using near infrared reflectance spectroscopy (NIRS). Calibration equations were developed for oil and fatty acid contents with the modified partial least squares (MPLS) regression method. The correlations be...
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| Veröffentlicht in: | Journal of the American Oil Chemists' Society 2012-04, Vol.89 (4), p.567-575 |
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| creator | Quampah, Alfred Huang, Zhuang Rong Wu, Jian Guo Liu, Hai Ying Li, Jin Rong Zhu, Shui Jin Shi, Chun Hai |
| description | Oil content and fatty acid composition in 444 ground cottonseed kernel samples were analyzed using near infrared reflectance spectroscopy (NIRS). Calibration equations were developed for oil and fatty acid contents with the modified partial least squares (MPLS) regression method. The correlations between NIRS and reference values in external validation were in agreement with the predictions in calibration. Each equation was assessed based on the relative prediction determinant for external validation (RPDv). Equations corresponding to total oil content (RPDv = 11.495) and linoleic acid (RPDv = 5.026) showed high accuracy. For palmitic acid (RPDv = 1.914), myristic acid (RPDv = 1.724) and oleic acid (RPDv = 1.999), the equations were predicted with relatively high accuracy while those for palmitoleic acid (RPDv = 0.686), stearic acid (RPDv = 0.792), linolenic acid (RPDv = 0.475) and 1-eicosenoic acid (RPDv = 0.619) were poorly predicted. The equations for traits with RPDv > 1.5 could be reliably used in screening samples for breeding programs. |
| doi_str_mv | 10.1007/s11746-011-1945-2 |
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Calibration equations were developed for oil and fatty acid contents with the modified partial least squares (MPLS) regression method. The correlations between NIRS and reference values in external validation were in agreement with the predictions in calibration. Each equation was assessed based on the relative prediction determinant for external validation (RPDv). Equations corresponding to total oil content (RPDv = 11.495) and linoleic acid (RPDv = 5.026) showed high accuracy. For palmitic acid (RPDv = 1.914), myristic acid (RPDv = 1.724) and oleic acid (RPDv = 1.999), the equations were predicted with relatively high accuracy while those for palmitoleic acid (RPDv = 0.686), stearic acid (RPDv = 0.792), linolenic acid (RPDv = 0.475) and 1-eicosenoic acid (RPDv = 0.619) were poorly predicted. The equations for traits with RPDv > 1.5 could be reliably used in screening samples for breeding programs.</description><identifier>ISSN: 0003-021X</identifier><identifier>EISSN: 1558-9331</identifier><identifier>DOI: 10.1007/s11746-011-1945-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Agriculture ; Biological and medical sciences ; Biomaterials ; Biotechnology ; Calibration ; Chemistry ; Chemistry and Materials Science ; cottonseed ; Cottonseed kernel ; equations ; Estimating techniques ; Fat industries ; fatty acid composition ; Fatty acids ; Food industries ; Food Science ; Fundamental and applied biological sciences. Psychology ; Industrial Chemistry/Chemical Engineering ; Inverse multiple scatter correction (I‐MSC) ; least squares ; linoleic acid ; linolenic acid ; lipid content ; myristic acid ; Near infrared spectroscopy (NIRS) ; near-infrared reflectance spectroscopy ; normal values ; Oil content ; Oils & fats ; oleic acid ; Original Paper ; palmitic acid ; palmitoleic acid ; prediction ; Reflectance ; seeds ; Spectroscopy ; Spectrum analysis ; stearic acid</subject><ispartof>Journal of the American Oil Chemists' Society, 2012-04, Vol.89 (4), p.567-575</ispartof><rights>AOCS 2011</rights><rights>2012 American Oil Chemists' Society (AOCS)</rights><rights>2015 INIST-CNRS</rights><rights>AOCS 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4417-6ae637ab03a110df06ef5f89b5efad9d52e6c0d6d8531570ed32ba9b5ed544043</citedby><cites>FETCH-LOGICAL-c4417-6ae637ab03a110df06ef5f89b5efad9d52e6c0d6d8531570ed32ba9b5ed544043</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11746-011-1945-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11746-011-1945-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,41488,42557,45574,45575,51319</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25773231$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Quampah, Alfred</creatorcontrib><creatorcontrib>Huang, Zhuang Rong</creatorcontrib><creatorcontrib>Wu, Jian Guo</creatorcontrib><creatorcontrib>Liu, Hai Ying</creatorcontrib><creatorcontrib>Li, Jin Rong</creatorcontrib><creatorcontrib>Zhu, Shui Jin</creatorcontrib><creatorcontrib>Shi, Chun Hai</creatorcontrib><title>Estimation of Oil Content and Fatty Acid Composition in Cottonseed Kernel Powder Using Near Infrared Reflectance Spectroscopy</title><title>Journal of the American Oil Chemists' Society</title><addtitle>J Am Oil Chem Soc</addtitle><description>Oil content and fatty acid composition in 444 ground cottonseed kernel samples were analyzed using near infrared reflectance spectroscopy (NIRS). Calibration equations were developed for oil and fatty acid contents with the modified partial least squares (MPLS) regression method. The correlations between NIRS and reference values in external validation were in agreement with the predictions in calibration. Each equation was assessed based on the relative prediction determinant for external validation (RPDv). Equations corresponding to total oil content (RPDv = 11.495) and linoleic acid (RPDv = 5.026) showed high accuracy. For palmitic acid (RPDv = 1.914), myristic acid (RPDv = 1.724) and oleic acid (RPDv = 1.999), the equations were predicted with relatively high accuracy while those for palmitoleic acid (RPDv = 0.686), stearic acid (RPDv = 0.792), linolenic acid (RPDv = 0.475) and 1-eicosenoic acid (RPDv = 0.619) were poorly predicted. The equations for traits with RPDv > 1.5 could be reliably used in screening samples for breeding programs.</description><subject>Agriculture</subject><subject>Biological and medical sciences</subject><subject>Biomaterials</subject><subject>Biotechnology</subject><subject>Calibration</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>cottonseed</subject><subject>Cottonseed kernel</subject><subject>equations</subject><subject>Estimating techniques</subject><subject>Fat industries</subject><subject>fatty acid composition</subject><subject>Fatty acids</subject><subject>Food industries</subject><subject>Food Science</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Industrial Chemistry/Chemical Engineering</subject><subject>Inverse multiple scatter correction (I‐MSC)</subject><subject>least squares</subject><subject>linoleic acid</subject><subject>linolenic acid</subject><subject>lipid content</subject><subject>myristic acid</subject><subject>Near infrared spectroscopy (NIRS)</subject><subject>near-infrared reflectance spectroscopy</subject><subject>normal values</subject><subject>Oil content</subject><subject>Oils & fats</subject><subject>oleic acid</subject><subject>Original Paper</subject><subject>palmitic acid</subject><subject>palmitoleic acid</subject><subject>prediction</subject><subject>Reflectance</subject><subject>seeds</subject><subject>Spectroscopy</subject><subject>Spectrum analysis</subject><subject>stearic acid</subject><issn>0003-021X</issn><issn>1558-9331</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkk9vFCEYxidGE9fqB_AkMfE4yn9mjptNq02bbtN1E2-EhZfNNFMYgabZg99d1mnqrT3BC7_n5eGBpvlI8FeCsfqWCVFctpiQlvRctPRVsyBCdG3PGHndLDDGrMWU_HrbvMv5tpYdo2LR_DnNZbgzZYgBRY_Ww4hWMRQIBZng0Jkp5YCWdnB1-W6KefhHDqGWpcSQARy6gBRgRNfxwUFC2zyEPboCk9B58MmkStyAH8EWEyygzVRnKWYbp8P75o03Y4YPj-NJsz07_bn60V6uv5-vlpet5ZyoVhqQTJkdZoYQ7DyW4IXv-p0Ab1zvBAVpsZOuE4wIhcExujPHbSc4x5ydNJ_nvlOKv-8hF30b71OoR-qedZJ1PZUVIjNkq7ucwOsp1WjSQROsjyHrOWRdQ9bHkDWtmi-PjU22ZqzXDXbIT0IqlGKUkcqpmXsYRji83Fgv16sNFlJVJZ2VuYrCHtJ_68_Z-jSLvIna7FO1tN1QTHh9d047jp8lKJf1t_wFpxOv0A</recordid><startdate>201204</startdate><enddate>201204</enddate><creator>Quampah, Alfred</creator><creator>Huang, Zhuang Rong</creator><creator>Wu, Jian Guo</creator><creator>Liu, Hai Ying</creator><creator>Li, Jin Rong</creator><creator>Zhu, Shui Jin</creator><creator>Shi, Chun Hai</creator><general>Springer-Verlag</general><general>Springer</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>PCBAR</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>201204</creationdate><title>Estimation of Oil Content and Fatty Acid Composition in Cottonseed Kernel Powder Using Near Infrared Reflectance Spectroscopy</title><author>Quampah, Alfred ; Huang, Zhuang Rong ; Wu, Jian Guo ; Liu, Hai Ying ; Li, Jin Rong ; Zhu, Shui Jin ; Shi, Chun Hai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4417-6ae637ab03a110df06ef5f89b5efad9d52e6c0d6d8531570ed32ba9b5ed544043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Agriculture</topic><topic>Biological and medical sciences</topic><topic>Biomaterials</topic><topic>Biotechnology</topic><topic>Calibration</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>cottonseed</topic><topic>Cottonseed kernel</topic><topic>equations</topic><topic>Estimating techniques</topic><topic>Fat industries</topic><topic>fatty acid composition</topic><topic>Fatty acids</topic><topic>Food industries</topic><topic>Food Science</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Industrial Chemistry/Chemical Engineering</topic><topic>Inverse multiple scatter correction (I‐MSC)</topic><topic>least squares</topic><topic>linoleic acid</topic><topic>linolenic acid</topic><topic>lipid content</topic><topic>myristic acid</topic><topic>Near infrared spectroscopy (NIRS)</topic><topic>near-infrared reflectance spectroscopy</topic><topic>normal values</topic><topic>Oil content</topic><topic>Oils & fats</topic><topic>oleic acid</topic><topic>Original Paper</topic><topic>palmitic acid</topic><topic>palmitoleic acid</topic><topic>prediction</topic><topic>Reflectance</topic><topic>seeds</topic><topic>Spectroscopy</topic><topic>Spectrum analysis</topic><topic>stearic acid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Quampah, Alfred</creatorcontrib><creatorcontrib>Huang, Zhuang Rong</creatorcontrib><creatorcontrib>Wu, Jian Guo</creatorcontrib><creatorcontrib>Liu, Hai Ying</creatorcontrib><creatorcontrib>Li, Jin Rong</creatorcontrib><creatorcontrib>Zhu, Shui Jin</creatorcontrib><creatorcontrib>Shi, Chun Hai</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Research Library (Corporate)</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of the American Oil Chemists' Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Quampah, Alfred</au><au>Huang, Zhuang Rong</au><au>Wu, Jian Guo</au><au>Liu, Hai Ying</au><au>Li, Jin Rong</au><au>Zhu, Shui Jin</au><au>Shi, Chun Hai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of Oil Content and Fatty Acid Composition in Cottonseed Kernel Powder Using Near Infrared Reflectance Spectroscopy</atitle><jtitle>Journal of the American Oil Chemists' Society</jtitle><stitle>J Am Oil Chem Soc</stitle><date>2012-04</date><risdate>2012</risdate><volume>89</volume><issue>4</issue><spage>567</spage><epage>575</epage><pages>567-575</pages><issn>0003-021X</issn><eissn>1558-9331</eissn><abstract>Oil content and fatty acid composition in 444 ground cottonseed kernel samples were analyzed using near infrared reflectance spectroscopy (NIRS). Calibration equations were developed for oil and fatty acid contents with the modified partial least squares (MPLS) regression method. The correlations between NIRS and reference values in external validation were in agreement with the predictions in calibration. Each equation was assessed based on the relative prediction determinant for external validation (RPDv). Equations corresponding to total oil content (RPDv = 11.495) and linoleic acid (RPDv = 5.026) showed high accuracy. For palmitic acid (RPDv = 1.914), myristic acid (RPDv = 1.724) and oleic acid (RPDv = 1.999), the equations were predicted with relatively high accuracy while those for palmitoleic acid (RPDv = 0.686), stearic acid (RPDv = 0.792), linolenic acid (RPDv = 0.475) and 1-eicosenoic acid (RPDv = 0.619) were poorly predicted. The equations for traits with RPDv > 1.5 could be reliably used in screening samples for breeding programs.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s11746-011-1945-2</doi><tpages>9</tpages></addata></record> |
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| subjects | Agriculture Biological and medical sciences Biomaterials Biotechnology Calibration Chemistry Chemistry and Materials Science cottonseed Cottonseed kernel equations Estimating techniques Fat industries fatty acid composition Fatty acids Food industries Food Science Fundamental and applied biological sciences. Psychology Industrial Chemistry/Chemical Engineering Inverse multiple scatter correction (I‐MSC) least squares linoleic acid linolenic acid lipid content myristic acid Near infrared spectroscopy (NIRS) near-infrared reflectance spectroscopy normal values Oil content Oils & fats oleic acid Original Paper palmitic acid palmitoleic acid prediction Reflectance seeds Spectroscopy Spectrum analysis stearic acid |
| title | Estimation of Oil Content and Fatty Acid Composition in Cottonseed Kernel Powder Using Near Infrared Reflectance Spectroscopy |
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