Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods
This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The cur...
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Veröffentlicht in: | Journal of dairy research 2017-05, Vol.84 (2), p.214-219 |
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description | This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400–550 nm excitation range with Δλ of 10–100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk. |
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Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400–550 nm excitation range with Δλ of 10–100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.</description><identifier>ISSN: 0022-0299</identifier><identifier>ISSN: 1469-7629</identifier><identifier>EISSN: 1469-7629</identifier><identifier>DOI: 10.1017/S0022029917000073</identifier><identifier>PMID: 28325170</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>adulterated products ; Animals ; Authenticity ; Buffalo ; buffalo milk ; Buffaloes ; CAD ; calibration ; Cattle ; Chemistry ; Classification ; Computer aided design ; Cow's milk ; cows ; Dairy industry ; Dairy products ; Design of experiments ; detection limit ; Discriminant analysis ; Female ; Fluorescence ; fluorescence emission spectroscopy ; Fluorescence spectroscopy ; Food ; Food Contamination - analysis ; foods ; least squares ; Least-Squares Analysis ; Limit of Detection ; Mathematical models ; Mean square values ; Methods ; Milk ; Milk - chemistry ; Milk - classification ; Multivariate analysis ; Nitrogen ; prediction ; Principal Component Analysis ; Principal components analysis ; raw materials ; Reproducibility of Results ; Root-mean-square errors ; Scientific papers ; Spectrometry, Fluorescence - methods ; Spectroscopy</subject><ispartof>Journal of dairy research, 2017-05, Vol.84 (2), p.214-219</ispartof><rights>Copyright © Proprietors of Journal of Dairy Research 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-2b628f20e49cdfc8ad92f779a52983a19ae2efd38cc982da3eb60ddfd45b467a3</citedby><cites>FETCH-LOGICAL-c406t-2b628f20e49cdfc8ad92f779a52983a19ae2efd38cc982da3eb60ddfd45b467a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0022029917000073/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,776,780,27901,27902,55603</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28325170$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Durakli Velioglu, Serap</creatorcontrib><creatorcontrib>Ercioglu, Elif</creatorcontrib><creatorcontrib>Boyaci, Ismail Hakki</creatorcontrib><title>Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods</title><title>Journal of dairy research</title><addtitle>Journal of Dairy Research</addtitle><description>This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400–550 nm excitation range with Δλ of 10–100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.</description><subject>adulterated products</subject><subject>Animals</subject><subject>Authenticity</subject><subject>Buffalo</subject><subject>buffalo milk</subject><subject>Buffaloes</subject><subject>CAD</subject><subject>calibration</subject><subject>Cattle</subject><subject>Chemistry</subject><subject>Classification</subject><subject>Computer aided design</subject><subject>Cow's milk</subject><subject>cows</subject><subject>Dairy industry</subject><subject>Dairy products</subject><subject>Design of experiments</subject><subject>detection limit</subject><subject>Discriminant analysis</subject><subject>Female</subject><subject>Fluorescence</subject><subject>fluorescence emission spectroscopy</subject><subject>Fluorescence spectroscopy</subject><subject>Food</subject><subject>Food Contamination - analysis</subject><subject>foods</subject><subject>least squares</subject><subject>Least-Squares Analysis</subject><subject>Limit of Detection</subject><subject>Mathematical models</subject><subject>Mean square values</subject><subject>Methods</subject><subject>Milk</subject><subject>Milk - chemistry</subject><subject>Milk - classification</subject><subject>Multivariate analysis</subject><subject>Nitrogen</subject><subject>prediction</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>raw materials</subject><subject>Reproducibility of Results</subject><subject>Root-mean-square errors</subject><subject>Scientific papers</subject><subject>Spectrometry, Fluorescence - methods</subject><subject>Spectroscopy</subject><issn>0022-0299</issn><issn>1469-7629</issn><issn>1469-7629</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFUk1v1DAQtRCILoUfwAVZ4tJLwB9JHB9RRVukSkgUzpFjj7suiR1sh9X-PX4Z3u0uIBCqL6PRvPfGM28QeknJG0qoeHtDCGOESUkFKU_wR2hF61ZWomXyMVrtytWufoKepXRHCOVEtk_RCes4awpphX58UrMz2Liko5ucV9kFjwfIG4ASF2vVGLDyBuuwwZMbv-4TAxn0HhosVmYZM0R1zI-sPXrj8vo3d0nO3-K09Xodgw9LwnZcQoSkwWvAaS6qMSQd5i12vvCm4finvdBUOrnvKjqVAU-Q18Gk5-hJaZfgxSGeoi8X7z-fX1XXHy8_nL-7rnRN2lyxoWWdZQRqqY3VnTKSWSGkapjsuKJSAQNreKe17JhRHIaWGGNN3Qx1KxQ_RWf3unMM3xZIuZ_K1mAclYcySc84aRilXDYPQmnXFbukZLxAX_8FvQtL9GWQnspiKheCyIKi9yhdtpMi2H4udqm47Snpd7fQ_3MLhfPqoLwME5hfjKP5BcAPomoaojO38Efv_8r-BCEhw4o</recordid><startdate>201705</startdate><enddate>201705</enddate><creator>Durakli Velioglu, Serap</creator><creator>Ercioglu, Elif</creator><creator>Boyaci, Ismail Hakki</creator><general>Cambridge University Press</general><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>7QL</scope><scope>7QO</scope><scope>7QR</scope><scope>7T5</scope><scope>7T7</scope><scope>7TM</scope><scope>7U7</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L6V</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7S</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>201705</creationdate><title>Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods</title><author>Durakli Velioglu, Serap ; Ercioglu, Elif ; Boyaci, Ismail Hakki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-2b628f20e49cdfc8ad92f779a52983a19ae2efd38cc982da3eb60ddfd45b467a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>adulterated products</topic><topic>Animals</topic><topic>Authenticity</topic><topic>Buffalo</topic><topic>buffalo milk</topic><topic>Buffaloes</topic><topic>CAD</topic><topic>calibration</topic><topic>Cattle</topic><topic>Chemistry</topic><topic>Classification</topic><topic>Computer aided design</topic><topic>Cow's milk</topic><topic>cows</topic><topic>Dairy industry</topic><topic>Dairy products</topic><topic>Design of experiments</topic><topic>detection limit</topic><topic>Discriminant analysis</topic><topic>Female</topic><topic>Fluorescence</topic><topic>fluorescence emission spectroscopy</topic><topic>Fluorescence spectroscopy</topic><topic>Food</topic><topic>Food Contamination - analysis</topic><topic>foods</topic><topic>least squares</topic><topic>Least-Squares Analysis</topic><topic>Limit of Detection</topic><topic>Mathematical models</topic><topic>Mean square values</topic><topic>Methods</topic><topic>Milk</topic><topic>Milk - chemistry</topic><topic>Milk - classification</topic><topic>Multivariate analysis</topic><topic>Nitrogen</topic><topic>prediction</topic><topic>Principal Component Analysis</topic><topic>Principal components analysis</topic><topic>raw materials</topic><topic>Reproducibility of Results</topic><topic>Root-mean-square errors</topic><topic>Scientific papers</topic><topic>Spectrometry, Fluorescence - methods</topic><topic>Spectroscopy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Durakli Velioglu, Serap</creatorcontrib><creatorcontrib>Ercioglu, Elif</creatorcontrib><creatorcontrib>Boyaci, Ismail Hakki</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Engineering Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science 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>Environmental Science Collection</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Journal of dairy research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Durakli Velioglu, Serap</au><au>Ercioglu, Elif</au><au>Boyaci, Ismail Hakki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods</atitle><jtitle>Journal of dairy research</jtitle><addtitle>Journal of Dairy Research</addtitle><date>2017-05</date><risdate>2017</risdate><volume>84</volume><issue>2</issue><spage>214</spage><epage>219</epage><pages>214-219</pages><issn>0022-0299</issn><issn>1469-7629</issn><eissn>1469-7629</eissn><abstract>This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400–550 nm excitation range with Δλ of 10–100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><pmid>28325170</pmid><doi>10.1017/S0022029917000073</doi><tpages>6</tpages></addata></record> |
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subjects | adulterated products Animals Authenticity Buffalo buffalo milk Buffaloes CAD calibration Cattle Chemistry Classification Computer aided design Cow's milk cows Dairy industry Dairy products Design of experiments detection limit Discriminant analysis Female Fluorescence fluorescence emission spectroscopy Fluorescence spectroscopy Food Food Contamination - analysis foods least squares Least-Squares Analysis Limit of Detection Mathematical models Mean square values Methods Milk Milk - chemistry Milk - classification Multivariate analysis Nitrogen prediction Principal Component Analysis Principal components analysis raw materials Reproducibility of Results Root-mean-square errors Scientific papers Spectrometry, Fluorescence - methods Spectroscopy |
title | Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods |
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