Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods

Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration ( n  = 113) and prediction ( n  = 56). Six different pretreatment methods were compared to determine...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Food and bioprocess technology 2014-02, Vol.7 (2), p.598-604
Hauptverfasser: Zhang, Chu, Xu, Ning, Luo, Liubin, Liu, Fei, Kong, Wenwen, Feng, Lei, He, Yong
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 604
container_issue 2
container_start_page 598
container_title Food and bioprocess technology
container_volume 7
creator Zhang, Chu
Xu, Ning
Luo, Liubin
Liu, Fei
Kong, Wenwen
Feng, Lei
He, Yong
description Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration ( n  = 113) and prediction ( n  = 56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm–partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r pre  = 0.9223, root mean square of prediction RMSEP = 0.0751 g/100 g, and coefficient of variation CV = 5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.
doi_str_mv 10.1007/s11947-013-1149-x
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1534822160</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2410777756</sourcerecordid><originalsourceid>FETCH-LOGICAL-c419t-5cff34a02a84df6eb43a3696660c7858c71e4fe9e9d00c36be59d6d8a020ab333</originalsourceid><addsrcrecordid>eNp1kd1u3CAQha0qkZomfYDeIfWmN27AYGxfbrf5k_InpektwjDeEHnBHdg0-0B5z7DZqJEqZW6YQd85gzhF8YXR74zS5jAy1ommpIyXjImufPxQ7LGO12Wdp51_Pacfi08x3lMqqWB8r3j6CQlMcsGTMJBZnDQmZ8jMOEucJ8eAS_AJLJkHtGsDUyTX4a8FJLfR-QW5BI3kzA-oMUM3U_bCEE2Y1uSHjvkqG__W6HQ_ArmB8XXXbFwEdOluGYn2llysxuQeNlgCMtej61G_cBeQ7oKNB8XuoMcIn1_P_eL2-OjX_LQ8vzo5m8_OSyNYl8raDAMXmla6FXaQ0AuuueyklNQ0bd2ahoEYoIPOUmq47KHurLRtVlDdc873i29b3wnDnxXEpJYuGhhH7SGsomI1F21VMUkz-vU_9D6s0OfXqUow2uSqZabYljL5VyLCoCZ0S41rxajaBKe2wakcnNoEpx6zptpqYmb9AvDN-X3RM7SHnlw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2410777756</pqid></control><display><type>article</type><title>Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods</title><source>SpringerLink</source><creator>Zhang, Chu ; Xu, Ning ; Luo, Liubin ; Liu, Fei ; Kong, Wenwen ; Feng, Lei ; He, Yong</creator><creatorcontrib>Zhang, Chu ; Xu, Ning ; Luo, Liubin ; Liu, Fei ; Kong, Wenwen ; Feng, Lei ; He, Yong</creatorcontrib><description>Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration ( n  = 113) and prediction ( n  = 56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm–partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r pre  = 0.9223, root mean square of prediction RMSEP = 0.0751 g/100 g, and coefficient of variation CV = 5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.</description><identifier>ISSN: 1935-5130</identifier><identifier>EISSN: 1935-5149</identifier><identifier>DOI: 10.1007/s11947-013-1149-x</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Acids ; Agriculture ; Algorithms ; Aspartic acid ; Biotechnology ; Calibration ; Chemistry ; Chemistry and Materials Science ; Chemistry/Food Science ; Chemometrics ; Coefficient of variation ; Communication ; Cordyceps ; Correlation coefficient ; Correlation coefficients ; Food Science ; Genetic algorithms ; Genetic analysis ; Infrared spectroscopy ; Least squares ; Near infrared radiation ; Powder ; Predictions ; Pretreatment ; Regression analysis ; Regression coefficients ; Spectrum analysis ; Support vector machines</subject><ispartof>Food and bioprocess technology, 2014-02, Vol.7 (2), p.598-604</ispartof><rights>Springer Science+Business Media New York 2013</rights><rights>Springer Science+Business Media New York 2013.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c419t-5cff34a02a84df6eb43a3696660c7858c71e4fe9e9d00c36be59d6d8a020ab333</citedby><cites>FETCH-LOGICAL-c419t-5cff34a02a84df6eb43a3696660c7858c71e4fe9e9d00c36be59d6d8a020ab333</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/s11947-013-1149-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11947-013-1149-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,782,786,27931,27932,41495,42564,51326</link.rule.ids></links><search><creatorcontrib>Zhang, Chu</creatorcontrib><creatorcontrib>Xu, Ning</creatorcontrib><creatorcontrib>Luo, Liubin</creatorcontrib><creatorcontrib>Liu, Fei</creatorcontrib><creatorcontrib>Kong, Wenwen</creatorcontrib><creatorcontrib>Feng, Lei</creatorcontrib><creatorcontrib>He, Yong</creatorcontrib><title>Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods</title><title>Food and bioprocess technology</title><addtitle>Food Bioprocess Technol</addtitle><description>Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration ( n  = 113) and prediction ( n  = 56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm–partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r pre  = 0.9223, root mean square of prediction RMSEP = 0.0751 g/100 g, and coefficient of variation CV = 5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.</description><subject>Acids</subject><subject>Agriculture</subject><subject>Algorithms</subject><subject>Aspartic acid</subject><subject>Biotechnology</subject><subject>Calibration</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Chemometrics</subject><subject>Coefficient of variation</subject><subject>Communication</subject><subject>Cordyceps</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Food Science</subject><subject>Genetic algorithms</subject><subject>Genetic analysis</subject><subject>Infrared spectroscopy</subject><subject>Least squares</subject><subject>Near infrared radiation</subject><subject>Powder</subject><subject>Predictions</subject><subject>Pretreatment</subject><subject>Regression analysis</subject><subject>Regression coefficients</subject><subject>Spectrum analysis</subject><subject>Support vector machines</subject><issn>1935-5130</issn><issn>1935-5149</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kd1u3CAQha0qkZomfYDeIfWmN27AYGxfbrf5k_InpektwjDeEHnBHdg0-0B5z7DZqJEqZW6YQd85gzhF8YXR74zS5jAy1ommpIyXjImufPxQ7LGO12Wdp51_Pacfi08x3lMqqWB8r3j6CQlMcsGTMJBZnDQmZ8jMOEucJ8eAS_AJLJkHtGsDUyTX4a8FJLfR-QW5BI3kzA-oMUM3U_bCEE2Y1uSHjvkqG__W6HQ_ArmB8XXXbFwEdOluGYn2llysxuQeNlgCMtej61G_cBeQ7oKNB8XuoMcIn1_P_eL2-OjX_LQ8vzo5m8_OSyNYl8raDAMXmla6FXaQ0AuuueyklNQ0bd2ahoEYoIPOUmq47KHurLRtVlDdc873i29b3wnDnxXEpJYuGhhH7SGsomI1F21VMUkz-vU_9D6s0OfXqUow2uSqZabYljL5VyLCoCZ0S41rxajaBKe2wakcnNoEpx6zptpqYmb9AvDN-X3RM7SHnlw</recordid><startdate>20140201</startdate><enddate>20140201</enddate><creator>Zhang, Chu</creator><creator>Xu, Ning</creator><creator>Luo, Liubin</creator><creator>Liu, Fei</creator><creator>Kong, Wenwen</creator><creator>Feng, Lei</creator><creator>He, Yong</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X2</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M0K</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20140201</creationdate><title>Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods</title><author>Zhang, Chu ; Xu, Ning ; Luo, Liubin ; Liu, Fei ; Kong, Wenwen ; Feng, Lei ; He, Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c419t-5cff34a02a84df6eb43a3696660c7858c71e4fe9e9d00c36be59d6d8a020ab333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Acids</topic><topic>Agriculture</topic><topic>Algorithms</topic><topic>Aspartic acid</topic><topic>Biotechnology</topic><topic>Calibration</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Chemometrics</topic><topic>Coefficient of variation</topic><topic>Communication</topic><topic>Cordyceps</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Food Science</topic><topic>Genetic algorithms</topic><topic>Genetic analysis</topic><topic>Infrared spectroscopy</topic><topic>Least squares</topic><topic>Near infrared radiation</topic><topic>Powder</topic><topic>Predictions</topic><topic>Pretreatment</topic><topic>Regression analysis</topic><topic>Regression coefficients</topic><topic>Spectrum analysis</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Chu</creatorcontrib><creatorcontrib>Xu, Ning</creatorcontrib><creatorcontrib>Luo, Liubin</creatorcontrib><creatorcontrib>Liu, Fei</creatorcontrib><creatorcontrib>Kong, Wenwen</creatorcontrib><creatorcontrib>Feng, Lei</creatorcontrib><creatorcontrib>He, Yong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Engineering Collection</collection><collection>Agriculture Science Database</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Food and bioprocess technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Chu</au><au>Xu, Ning</au><au>Luo, Liubin</au><au>Liu, Fei</au><au>Kong, Wenwen</au><au>Feng, Lei</au><au>He, Yong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods</atitle><jtitle>Food and bioprocess technology</jtitle><stitle>Food Bioprocess Technol</stitle><date>2014-02-01</date><risdate>2014</risdate><volume>7</volume><issue>2</issue><spage>598</spage><epage>604</epage><pages>598-604</pages><issn>1935-5130</issn><eissn>1935-5149</eissn><abstract>Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration ( n  = 113) and prediction ( n  = 56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm–partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r pre  = 0.9223, root mean square of prediction RMSEP = 0.0751 g/100 g, and coefficient of variation CV = 5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s11947-013-1149-x</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1935-5130
ispartof Food and bioprocess technology, 2014-02, Vol.7 (2), p.598-604
issn 1935-5130
1935-5149
language eng
recordid cdi_proquest_miscellaneous_1534822160
source SpringerLink
subjects Acids
Agriculture
Algorithms
Aspartic acid
Biotechnology
Calibration
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Chemometrics
Coefficient of variation
Communication
Cordyceps
Correlation coefficient
Correlation coefficients
Food Science
Genetic algorithms
Genetic analysis
Infrared spectroscopy
Least squares
Near infrared radiation
Powder
Predictions
Pretreatment
Regression analysis
Regression coefficients
Spectrum analysis
Support vector machines
title Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-06T14%3A14%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Detection%20of%20Aspartic%20Acid%20in%20Fermented%20Cordyceps%20Powder%20Using%20Near%20Infrared%20Spectroscopy%20Based%20on%20Variable%20Selection%20Algorithms%20and%20Multivariate%20Calibration%20Methods&rft.jtitle=Food%20and%20bioprocess%20technology&rft.au=Zhang,%20Chu&rft.date=2014-02-01&rft.volume=7&rft.issue=2&rft.spage=598&rft.epage=604&rft.pages=598-604&rft.issn=1935-5130&rft.eissn=1935-5149&rft_id=info:doi/10.1007/s11947-013-1149-x&rft_dat=%3Cproquest_cross%3E2410777756%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2410777756&rft_id=info:pmid/&rfr_iscdi=true