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...
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| Veröffentlicht in: | Food and bioprocess technology 2014-02, Vol.7 (2), p.598-604 |
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| 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 |
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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 & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Agricultural 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> |
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| source | SpringerNature Journals |
| 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 |
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