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Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy
The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty ( n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator i...
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| Published in: | Food science & technology 2005-12, Vol.38 (8), p.821-828 |
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| cites | cdi_FETCH-LOGICAL-c448t-6a467e9d6e0dc7860cbb35a5540cce4459f52a5aed87545ba0f7360bf8916b853 |
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| container_title | Food science & technology |
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| creator | Cozzolino, D. Murray, I. Chree, A. Scaife, J.R. |
| description | The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (
n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500
nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (
R
2) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (
r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (
R
2 |
| doi_str_mv | 10.1016/j.lwt.2004.10.007 |
| format | article |
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n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500
nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (
R
2) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (
r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (
R
2<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.</description><identifier>ISSN: 0023-6438</identifier><identifier>EISSN: 1096-1127</identifier><identifier>DOI: 10.1016/j.lwt.2004.10.007</identifier><identifier>CODEN: LBWTAP</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>anisidine value ; Biological and medical sciences ; chemical analysis ; Fat industries ; fatty acid composition ; Fish oil ; fish oils ; food analysis ; Food industries ; Free fatty acids ; Fundamental and applied biological sciences. Psychology ; least squares ; Lipid oxidation ; lipid peroxidation ; lipids ; model validation ; Moisture ; multivariate analysis ; Near-infrared spectroscopy ; NIR ; oxidation ; Partial least squares ; peroxide value ; Peroxides ; Pisces ; rancidity ; water content</subject><ispartof>Food science & technology, 2005-12, Vol.38 (8), p.821-828</ispartof><rights>2004 Swiss Society of Food Science and Technology</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-6a467e9d6e0dc7860cbb35a5540cce4459f52a5aed87545ba0f7360bf8916b853</citedby><cites>FETCH-LOGICAL-c448t-6a467e9d6e0dc7860cbb35a5540cce4459f52a5aed87545ba0f7360bf8916b853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17056084$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Cozzolino, D.</creatorcontrib><creatorcontrib>Murray, I.</creatorcontrib><creatorcontrib>Chree, A.</creatorcontrib><creatorcontrib>Scaife, J.R.</creatorcontrib><title>Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy</title><title>Food science & technology</title><description>The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (
n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500
nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (
R
2) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (
r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (
R
2<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.</description><subject>anisidine value</subject><subject>Biological and medical sciences</subject><subject>chemical analysis</subject><subject>Fat industries</subject><subject>fatty acid composition</subject><subject>Fish oil</subject><subject>fish oils</subject><subject>food analysis</subject><subject>Food industries</subject><subject>Free fatty acids</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>least squares</subject><subject>Lipid oxidation</subject><subject>lipid peroxidation</subject><subject>lipids</subject><subject>model validation</subject><subject>Moisture</subject><subject>multivariate analysis</subject><subject>Near-infrared spectroscopy</subject><subject>NIR</subject><subject>oxidation</subject><subject>Partial least squares</subject><subject>peroxide value</subject><subject>Peroxides</subject><subject>Pisces</subject><subject>rancidity</subject><subject>water content</subject><issn>0023-6438</issn><issn>1096-1127</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNp9kM1u1TAQhSMEEpfCA7DCG9jlMk78k4gVqvipVMQCurYmzrj4KjdObafoPgMvjcOtxK6zGc34O0fjU1WvOew5cPX-sJ9-530DIMq8B9BPqh2HXtWcN_pptQNo2lqJtntevUjpAKVE0-2qP9_WKft7jB4zsZEyxaOfMfsws-CYi0TMYc4nhtaPieE8smPwKa-RmJ-Z8-kXC35KbDixBWP2OLGJMOU63a0YKbFIt6WlzXFTz4Sx9rOL5XFkaSGbY0g2LKeX1TOHU6JXD_2iuvn86efl1_r6-5ery4_XtRWiy7VCoTT1oyIYre4U2GFoJUopwFoSQvZONiiRxk5LIQcEp1sFg-t6roZOthfVu7PvEsPdSimbo0-WpglnCmsyXLeac9AF5GfQlgtTJGeW6I8YT4aD2WI3B1NiN1vs2wr-ad4-mGOyOJVvztan_0INUkEnCvfmzDkMBm9jYW5-NMBb4NAo0XeF-HAmqGRx7ymaZD3NlkYfS2hmDP6RO_4CNfqkjw</recordid><startdate>200512</startdate><enddate>200512</enddate><creator>Cozzolino, D.</creator><creator>Murray, I.</creator><creator>Chree, A.</creator><creator>Scaife, J.R.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>F1W</scope><scope>H95</scope><scope>L.G</scope></search><sort><creationdate>200512</creationdate><title>Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy</title><author>Cozzolino, D. ; Murray, I. ; Chree, A. ; Scaife, J.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-6a467e9d6e0dc7860cbb35a5540cce4459f52a5aed87545ba0f7360bf8916b853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>anisidine value</topic><topic>Biological and medical sciences</topic><topic>chemical analysis</topic><topic>Fat industries</topic><topic>fatty acid composition</topic><topic>Fish oil</topic><topic>fish oils</topic><topic>food analysis</topic><topic>Food industries</topic><topic>Free fatty acids</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>least squares</topic><topic>Lipid oxidation</topic><topic>lipid peroxidation</topic><topic>lipids</topic><topic>model validation</topic><topic>Moisture</topic><topic>multivariate analysis</topic><topic>Near-infrared spectroscopy</topic><topic>NIR</topic><topic>oxidation</topic><topic>Partial least squares</topic><topic>peroxide value</topic><topic>Peroxides</topic><topic>Pisces</topic><topic>rancidity</topic><topic>water content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cozzolino, D.</creatorcontrib><creatorcontrib>Murray, I.</creatorcontrib><creatorcontrib>Chree, A.</creatorcontrib><creatorcontrib>Scaife, J.R.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Food science & technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cozzolino, D.</au><au>Murray, I.</au><au>Chree, A.</au><au>Scaife, J.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy</atitle><jtitle>Food science & technology</jtitle><date>2005-12</date><risdate>2005</risdate><volume>38</volume><issue>8</issue><spage>821</spage><epage>828</epage><pages>821-828</pages><issn>0023-6438</issn><eissn>1096-1127</eissn><coden>LBWTAP</coden><abstract>The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (
n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500
nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (
R
2) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (
r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (
R
2<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.lwt.2004.10.007</doi><tpages>8</tpages></addata></record> |
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| ispartof | Food science & technology, 2005-12, Vol.38 (8), p.821-828 |
| issn | 0023-6438 1096-1127 |
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| source | ScienceDirect Journals |
| subjects | anisidine value Biological and medical sciences chemical analysis Fat industries fatty acid composition Fish oil fish oils food analysis Food industries Free fatty acids Fundamental and applied biological sciences. Psychology least squares Lipid oxidation lipid peroxidation lipids model validation Moisture multivariate analysis Near-infrared spectroscopy NIR oxidation Partial least squares peroxide value Peroxides Pisces rancidity water content |
| title | Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy |
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