Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition
This study examined the ability of near-infrared reflectance spectroscopy method (FT-NIRS) and multivariate calibration to estimate the concentration of moisture, protein, lipid, ash and carbohydrate of Brazilian soybeans. The spectra obtained in the range of 4000 to 10,000cm−1 were preprocessed by...
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| Veröffentlicht in: | Food research international 2013-04, Vol.51 (1), p.53-58 |
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| creator | Ferreira, Daniela Souza Pallone, Juliana Azevedo Lima Poppi, Ronei Jesus |
| description | This study examined the ability of near-infrared reflectance spectroscopy method (FT-NIRS) and multivariate calibration to estimate the concentration of moisture, protein, lipid, ash and carbohydrate of Brazilian soybeans. The spectra obtained in the range of 4000 to 10,000cm−1 were preprocessed by several combinations of mathematical treatments: MSC (multiplicative scatter correction), SNV (standard normal variate) or first and second derivative and all data were mean centered before the calibration, for which was used the PLS method (partial least squares). The best calibration models found in this study were the ones used to determine protein and moisture contents (R2=0.81, RMSEP=1.61% and R2=0.80, RMSEC=1.55%, respectively). However, the technique shows high predictability for all parameters, including lipids, ashes and carbohydrates, with RMSECV of 0.40 to 2.30% and RMSEP of 0.38 to 3.71%. This result shows the viability of using NIR in controlling the quality parameters of soybeans.
► We examined the ability of NIRS to estimate the composition of Brazilian soybean. ► PLS models were developed for moisture, protein, lipid, ash and carbohydrate. ► It was possible to predict the parameters in a range of soybean varieties. |
| doi_str_mv | 10.1016/j.foodres.2012.09.015 |
| format | Article |
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► We examined the ability of NIRS to estimate the composition of Brazilian soybean. ► PLS models were developed for moisture, protein, lipid, ash and carbohydrate. ► It was possible to predict the parameters in a range of soybean varieties.</description><identifier>ISSN: 0963-9969</identifier><identifier>EISSN: 1873-7145</identifier><identifier>DOI: 10.1016/j.foodres.2012.09.015</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Ashes ; Biological and medical sciences ; Brazil ; Calibration ; carbohydrates ; Composition analysis ; Estimates ; Food industries ; Fruit and vegetable industries ; Fundamental and applied biological sciences. Psychology ; Glycine max ; least squares ; Lipids ; Mathematical analysis ; Mathematical models ; Multivariate data analysis ; Near-infrared reflectance spectroscopy ; near-infrared spectroscopy ; Soybean ; Soybeans ; viability</subject><ispartof>Food research international, 2013-04, Vol.51 (1), p.53-58</ispartof><rights>2012 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-460e4627951089bcb8c5bf8beb7521f84d90d6e669d0b1e2ea9b8813f43493403</citedby><cites>FETCH-LOGICAL-c476t-460e4627951089bcb8c5bf8beb7521f84d90d6e669d0b1e2ea9b8813f43493403</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.foodres.2012.09.015$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27135936$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ferreira, Daniela Souza</creatorcontrib><creatorcontrib>Pallone, Juliana Azevedo Lima</creatorcontrib><creatorcontrib>Poppi, Ronei Jesus</creatorcontrib><title>Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition</title><title>Food research international</title><description>This study examined the ability of near-infrared reflectance spectroscopy method (FT-NIRS) and multivariate calibration to estimate the concentration of moisture, protein, lipid, ash and carbohydrate of Brazilian soybeans. The spectra obtained in the range of 4000 to 10,000cm−1 were preprocessed by several combinations of mathematical treatments: MSC (multiplicative scatter correction), SNV (standard normal variate) or first and second derivative and all data were mean centered before the calibration, for which was used the PLS method (partial least squares). The best calibration models found in this study were the ones used to determine protein and moisture contents (R2=0.81, RMSEP=1.61% and R2=0.80, RMSEC=1.55%, respectively). However, the technique shows high predictability for all parameters, including lipids, ashes and carbohydrates, with RMSECV of 0.40 to 2.30% and RMSEP of 0.38 to 3.71%. This result shows the viability of using NIR in controlling the quality parameters of soybeans.
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Psychology</subject><subject>Glycine max</subject><subject>least squares</subject><subject>Lipids</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Multivariate data analysis</subject><subject>Near-infrared reflectance spectroscopy</subject><subject>near-infrared spectroscopy</subject><subject>Soybean</subject><subject>Soybeans</subject><subject>viability</subject><issn>0963-9969</issn><issn>1873-7145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkcFu1DAQhiMEEkvhERC-IG0PScdx7MQnBBVbKi2tRNsTQpbjjJFXSRzsLGJ5AJ4bR7vqtae5fPPP6P-y7C2FggIVF7vCet8FjEUJtCxAFkD5s2xFm5rlNa3482wFUrBcSiFfZq9i3AGA4LVcZf82fh8cBjIHPUbrw0BG1CF3ow06YEfihGYOPho_Hch6c5_fXH-7Oyd6mnpn9Oz8SGZPMM5u0DOST0H_db3TI4n-0GKa36_6g3EjkkH_IettcU6-Ygiu_0GMHyYf3ZLxOnthdR_xzWmeZQ-bz_eXX_Lt7dX15cdtbqpazHklACtR1pJTaGRr2sbw1jYttjUvqW2qTkInUAjZQUuxRC3bpqHMVqySrAJ2lq2PuVPwv_bpazW4aLDv9Yh-HxWtGVBaVg17GuU0hZYN1AnlR9SkomJAq6aQ6ggHRUEtitROnRSpRZECqZKitPf-dEJHo_vU-GhcfFwua8q4ZCJx746c1V7pnyExD3cpiC8aQbAl6cORwFTe7-RTReNwNNi5kPypzrsnfvkP0mOzhA</recordid><startdate>20130401</startdate><enddate>20130401</enddate><creator>Ferreira, Daniela Souza</creator><creator>Pallone, Juliana Azevedo Lima</creator><creator>Poppi, Ronei Jesus</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20130401</creationdate><title>Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition</title><author>Ferreira, Daniela Souza ; Pallone, Juliana Azevedo Lima ; Poppi, Ronei Jesus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-460e4627951089bcb8c5bf8beb7521f84d90d6e669d0b1e2ea9b8813f43493403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Ashes</topic><topic>Biological and medical sciences</topic><topic>Brazil</topic><topic>Calibration</topic><topic>carbohydrates</topic><topic>Composition analysis</topic><topic>Estimates</topic><topic>Food industries</topic><topic>Fruit and vegetable industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Glycine max</topic><topic>least squares</topic><topic>Lipids</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Multivariate data analysis</topic><topic>Near-infrared reflectance spectroscopy</topic><topic>near-infrared spectroscopy</topic><topic>Soybean</topic><topic>Soybeans</topic><topic>viability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ferreira, Daniela Souza</creatorcontrib><creatorcontrib>Pallone, Juliana Azevedo Lima</creatorcontrib><creatorcontrib>Poppi, Ronei Jesus</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>Food research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ferreira, Daniela Souza</au><au>Pallone, Juliana Azevedo Lima</au><au>Poppi, Ronei Jesus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition</atitle><jtitle>Food research international</jtitle><date>2013-04-01</date><risdate>2013</risdate><volume>51</volume><issue>1</issue><spage>53</spage><epage>58</epage><pages>53-58</pages><issn>0963-9969</issn><eissn>1873-7145</eissn><abstract>This study examined the ability of near-infrared reflectance spectroscopy method (FT-NIRS) and multivariate calibration to estimate the concentration of moisture, protein, lipid, ash and carbohydrate of Brazilian soybeans. The spectra obtained in the range of 4000 to 10,000cm−1 were preprocessed by several combinations of mathematical treatments: MSC (multiplicative scatter correction), SNV (standard normal variate) or first and second derivative and all data were mean centered before the calibration, for which was used the PLS method (partial least squares). The best calibration models found in this study were the ones used to determine protein and moisture contents (R2=0.81, RMSEP=1.61% and R2=0.80, RMSEC=1.55%, respectively). However, the technique shows high predictability for all parameters, including lipids, ashes and carbohydrates, with RMSECV of 0.40 to 2.30% and RMSEP of 0.38 to 3.71%. This result shows the viability of using NIR in controlling the quality parameters of soybeans.
► We examined the ability of NIRS to estimate the composition of Brazilian soybean. ► PLS models were developed for moisture, protein, lipid, ash and carbohydrate. ► It was possible to predict the parameters in a range of soybean varieties.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.foodres.2012.09.015</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Ashes Biological and medical sciences Brazil Calibration carbohydrates Composition analysis Estimates Food industries Fruit and vegetable industries Fundamental and applied biological sciences. Psychology Glycine max least squares Lipids Mathematical analysis Mathematical models Multivariate data analysis Near-infrared reflectance spectroscopy near-infrared spectroscopy Soybean Soybeans viability |
| title | Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition |
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