Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover
We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariat...
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| Veröffentlicht in: | Cellulose 2009, Vol.16 (4), p.567-576 |
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| creator | Wolfrum, Edward J Sluiter, Amie D |
| description | We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models. |
| doi_str_mv | 10.1007/s10570-009-9320-2 |
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(NREL), Golden, CO (United States)</creatorcontrib><description>We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-9993548ecc56fce185ba4c6192eea2c0f74ea0b6ea3dbabf1673c45d245a24bd3</citedby><cites>FETCH-LOGICAL-c407t-9993548ecc56fce185ba4c6192eea2c0f74ea0b6ea3dbabf1673c45d245a24bd3</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/s10570-009-9320-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10570-009-9320-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/968449$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Wolfrum, Edward J</creatorcontrib><creatorcontrib>Sluiter, Amie D</creatorcontrib><creatorcontrib>National Renewable Energy Lab. (NREL), Golden, CO (United States)</creatorcontrib><title>Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover</title><title>Cellulose</title><addtitle>Cellulose</addtitle><description>We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.</description><subject>09 BIOMASS FUELS</subject><subject>ACCURACY</subject><subject>AGRICULTURAL WASTES</subject><subject>ALGORITHMS</subject><subject>Bioenergy</subject><subject>BIOMASS</subject><subject>Bioorganic Chemistry</subject><subject>CALIBRATION</subject><subject>Ceramics</subject><subject>CHEMICAL ANALYSIS</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Composites</subject><subject>Corn</subject><subject>Dilution</subject><subject>Glass</subject><subject>Glucan</subject><subject>Glucose</subject><subject>INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY</subject><subject>LIGNIN</subject><subject>MAIZE</subject><subject>Multivariate analysis</subject><subject>Multivariate statistical analysis</subject><subject>Natural Materials</subject><subject>Near infrared radiation</subject><subject>Organic Chemistry</subject><subject>Physical Chemistry</subject><subject>Polymer Sciences</subject><subject>Raw materials</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical significance</subject><subject>Sustainable Development</subject><issn>0969-0239</issn><issn>1572-882X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kMFKHTEUhoMo9Gp9gK6a0nX0JJNMJssitRUEF1ZwFzLJGY2dO7lNcgXfvpEp2JWrcxbf93POT8gnDmccQJ8XDkoDAzDMdAKYOCAbrrRgwyDuD8kGTG8YiM58IMelPEEDteAbEq-2u5yeMdDtfq7x2eXoKlLv5jhmV2Na6DYFnAudUqY-5YWW2vhMJ8TQVv-buiXQEOd9ReZ8DHSXsWZsMeF_4SM5mtxc8PTfPCF3l99_Xfxk1zc_ri6-XTMvQVdmjOmUHNB71U8e-aBGJ33PjUB0wsOkJToYe3RdGN048V53XqogpHJCjqE7IV_W3FRqtMXHiv7Rp2VBX63pBylNY76uTPv9zx5LtU9pn5d2lhVCGcO1ULpRfKV8TqVknOwux63LL5aDfW3drq3bVqZ9bd2K5ojVKY1dHjC_Jb8nfV6lySXrHnIs9u5WAO-A96ob2il_AdCqkE4</recordid><startdate>2009</startdate><enddate>2009</enddate><creator>Wolfrum, Edward J</creator><creator>Sluiter, Amie D</creator><general>Dordrecht : Springer Netherlands</general><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>OTOTI</scope></search><sort><creationdate>2009</creationdate><title>Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover</title><author>Wolfrum, Edward J ; Sluiter, Amie D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-9993548ecc56fce185ba4c6192eea2c0f74ea0b6ea3dbabf1673c45d245a24bd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>09 BIOMASS FUELS</topic><topic>ACCURACY</topic><topic>AGRICULTURAL WASTES</topic><topic>ALGORITHMS</topic><topic>Bioenergy</topic><topic>BIOMASS</topic><topic>Bioorganic Chemistry</topic><topic>CALIBRATION</topic><topic>Ceramics</topic><topic>CHEMICAL ANALYSIS</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Composites</topic><topic>Corn</topic><topic>Dilution</topic><topic>Glass</topic><topic>Glucan</topic><topic>Glucose</topic><topic>INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY</topic><topic>LIGNIN</topic><topic>MAIZE</topic><topic>Multivariate analysis</topic><topic>Multivariate statistical analysis</topic><topic>Natural Materials</topic><topic>Near infrared radiation</topic><topic>Organic Chemistry</topic><topic>Physical Chemistry</topic><topic>Polymer Sciences</topic><topic>Raw materials</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical significance</topic><topic>Sustainable Development</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wolfrum, Edward J</creatorcontrib><creatorcontrib>Sluiter, Amie D</creatorcontrib><creatorcontrib>National Renewable Energy Lab. 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(NREL), Golden, CO (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover</atitle><jtitle>Cellulose</jtitle><stitle>Cellulose</stitle><date>2009</date><risdate>2009</risdate><volume>16</volume><issue>4</issue><spage>567</spage><epage>576</epage><pages>567-576</pages><issn>0969-0239</issn><eissn>1572-882X</eissn><abstract>We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.</abstract><cop>Dordrecht</cop><pub>Dordrecht : Springer Netherlands</pub><doi>10.1007/s10570-009-9320-2</doi><tpages>10</tpages></addata></record> |
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| subjects | 09 BIOMASS FUELS ACCURACY AGRICULTURAL WASTES ALGORITHMS Bioenergy BIOMASS Bioorganic Chemistry CALIBRATION Ceramics CHEMICAL ANALYSIS Chemistry Chemistry and Materials Science Composites Corn Dilution Glass Glucan Glucose INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY LIGNIN MAIZE Multivariate analysis Multivariate statistical analysis Natural Materials Near infrared radiation Organic Chemistry Physical Chemistry Polymer Sciences Raw materials Statistical analysis Statistical methods Statistical significance Sustainable Development |
| title | Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover |
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