Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits
Nondestructive method of measuring soluble solids content （SSC） of citrus fruits was developed using Fourier transform near infrared reflectance （FT-NIR） measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were develope...
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| Veröffentlicht in: | Journal of Zhejiang University. B. Science 2006-10, Vol.7 (10), p.794-799 |
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| creator | Lu, Hui-shan Xu, Hui-rong Ying, Yi-bin Fu, Xia-ping Yu, Hai-yan Tian, Hai-qing |
| description | Nondestructive method of measuring soluble solids content （SSC） of citrus fruits was developed using Fourier transform near infrared reflectance （FT-NIR） measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms （standard normal variate （SNV）, multiplicative signal correction （MSC）） were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression （PCR） and partial least squares （PLS） regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79°Brix. |
| doi_str_mv | 10.1631/jzus.2006.B0794 |
| format | Article |
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The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms （standard normal variate （SNV）, multiplicative signal correction （MSC）） were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression （PCR） and partial least squares （PLS） regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79°Brix.</description><identifier>ISSN: 1673-1581</identifier><identifier>EISSN: 1862-1783</identifier><identifier>DOI: 10.1631/jzus.2006.B0794</identifier><identifier>PMID: 16972321</identifier><language>eng</language><publisher>China: Springer Nature B.V</publisher><subject>Biochemistry - methods ; Biotechnology ; Calibration ; Citrus - metabolism ; Citrus fruits ; Fourier transforms ; FT-NIR光谱 ; Least-Squares Analysis ; Light ; Models, Statistical ; Regression Analysis ; Reproducibility of Results ; Scattering, Radiation ; Spectroscopy, Fourier Transform Infrared - methods ; Spectroscopy, Near-Infrared ; Spectrum analysis ; 反射比 ; 可溶固体含量</subject><ispartof>Journal of Zhejiang University. B. Science, 2006-10, Vol.7 (10), p.794-799</ispartof><rights>Zhejiang University 2006</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><rights>Copyright © 2006, Journal of Zhejiang University Science</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3953-c08cbd9fc3f5c7530a05f95e7495dffe0fc8db2e00208c2768439754b0f2d8e33</citedby><cites>FETCH-LOGICAL-c3953-c08cbd9fc3f5c7530a05f95e7495dffe0fc8db2e00208c2768439754b0f2d8e33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/86281A/86281A.jpg</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1599807/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1599807/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16972321$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lu, Hui-shan</creatorcontrib><creatorcontrib>Xu, Hui-rong</creatorcontrib><creatorcontrib>Ying, Yi-bin</creatorcontrib><creatorcontrib>Fu, Xia-ping</creatorcontrib><creatorcontrib>Yu, Hai-yan</creatorcontrib><creatorcontrib>Tian, Hai-qing</creatorcontrib><title>Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits</title><title>Journal of Zhejiang University. B. Science</title><addtitle>Journal of Zhejiang University Science</addtitle><description>Nondestructive method of measuring soluble solids content （SSC） of citrus fruits was developed using Fourier transform near infrared reflectance （FT-NIR） measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms （standard normal variate （SNV）, multiplicative signal correction （MSC）） were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression （PCR） and partial least squares （PLS） regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79°Brix.</description><subject>Biochemistry - methods</subject><subject>Biotechnology</subject><subject>Calibration</subject><subject>Citrus - metabolism</subject><subject>Citrus fruits</subject><subject>Fourier transforms</subject><subject>FT-NIR光谱</subject><subject>Least-Squares Analysis</subject><subject>Light</subject><subject>Models, Statistical</subject><subject>Regression Analysis</subject><subject>Reproducibility of Results</subject><subject>Scattering, Radiation</subject><subject>Spectroscopy, Fourier Transform Infrared - methods</subject><subject>Spectroscopy, Near-Infrared</subject><subject>Spectrum analysis</subject><subject>反射比</subject><subject>可溶固体含量</subject><issn>1673-1581</issn><issn>1862-1783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNpdUk1v1DAQjRCIlsKZG7I4cEDarT_i2L4glYoCUiUucLYcZ7z1krVT2ymlB347DrtqgdOMPM_vvflompcEr0nHyOn2bs5rinG3fo-Fah81x0R2dEWEZI9r3gm2IlySo-ZZzluM2xaL7mlzRDolKKPkuPl1Nk2jt6b4GNBFnJOHhEoyIbuYdiiAScgHl0yCAeUJbElxBwWWV5TM5AcEufjdniA6lOM49yMs0Q8Z2RgKhLJUfCjGFmR9SXNGLs2-5OfNE2fGDC8O8aT5dvHh6_mn1eWXj5_Pzy5XlinOVhZL2w_KWea4FZxhg7lTHESr-OAcYGfl0FPAmFYkFZ1smRK87bGjgwTGTpp3e95p7ncw2GopmVFPqTpPP3U0Xv9bCf5Kb-KNJlwpiUUleLsn-GGCM2Gjt3VWoVrWd9vh9rbvNSxrIBjjRe3NQS3F67nOR-98tjCOJkCcs-6k5JizBfj6P-A9rZSMydoHraDTPcimmHMCd2-bYL1cgV6uQC_y-s8V1B-v_u72AX9Y-4OuvYphc-1rP72x350fQVOqhGItZ78B7-q9qA</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Lu, Hui-shan</creator><creator>Xu, Hui-rong</creator><creator>Ying, Yi-bin</creator><creator>Fu, Xia-ping</creator><creator>Yu, Hai-yan</creator><creator>Tian, Hai-qing</creator><general>Springer Nature B.V</general><general>School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China</general><general>Zhejiang University Press</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W95</scope><scope>~WA</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>7X8</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope><scope>5PM</scope></search><sort><creationdate>200610</creationdate><title>Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits</title><author>Lu, Hui-shan ; Xu, Hui-rong ; Ying, Yi-bin ; Fu, Xia-ping ; Yu, Hai-yan ; Tian, Hai-qing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3953-c08cbd9fc3f5c7530a05f95e7495dffe0fc8db2e00208c2768439754b0f2d8e33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Biochemistry - 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B. Science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lu, Hui-shan</au><au>Xu, Hui-rong</au><au>Ying, Yi-bin</au><au>Fu, Xia-ping</au><au>Yu, Hai-yan</au><au>Tian, Hai-qing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits</atitle><jtitle>Journal of Zhejiang University. B. Science</jtitle><addtitle>Journal of Zhejiang University Science</addtitle><date>2006-10</date><risdate>2006</risdate><volume>7</volume><issue>10</issue><spage>794</spage><epage>799</epage><pages>794-799</pages><issn>1673-1581</issn><eissn>1862-1783</eissn><abstract>Nondestructive method of measuring soluble solids content （SSC） of citrus fruits was developed using Fourier transform near infrared reflectance （FT-NIR） measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms （standard normal variate （SNV）, multiplicative signal correction （MSC）） were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression （PCR） and partial least squares （PLS） regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79°Brix.</abstract><cop>China</cop><pub>Springer Nature B.V</pub><pmid>16972321</pmid><doi>10.1631/jzus.2006.B0794</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; SpringerLink Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Biochemistry - methods Biotechnology Calibration Citrus - metabolism Citrus fruits Fourier transforms FT-NIR光谱 Least-Squares Analysis Light Models, Statistical Regression Analysis Reproducibility of Results Scattering, Radiation Spectroscopy, Fourier Transform Infrared - methods Spectroscopy, Near-Infrared Spectrum analysis 反射比 可溶固体含量 |
| title | Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits |
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