Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin
Honey is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of...
Gespeichert in:
| Veröffentlicht in: | Analytical and bioanalytical chemistry 2020-01, Vol.412 (2), p.463-472 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 472 |
|---|---|
| container_issue | 2 |
| container_start_page | 463 |
| container_title | Analytical and bioanalytical chemistry |
| container_volume | 412 |
| creator | Fiamegos, Yiannis Dumitrascu, Catalina Ghidotti, Michele de la Calle Guntiñas, Maria Beatriz |
| description | Honey is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.
Graphical abstract |
| doi_str_mv | 10.1007/s00216-019-02255-6 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6992546</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A612850821</galeid><sourcerecordid>A612850821</sourcerecordid><originalsourceid>FETCH-LOGICAL-c583t-86c1bcef5bdd69b15c5fb988da2551a92571004e35450f2956f0eb6c83aa8b603</originalsourceid><addsrcrecordid>eNqFUk2L1TAULaI44-gfcCEBN246JmmTphthGPyCATcOuAtpetNmSJNn0j7pP_Hnmjd9Pj8WShYJOeeee-_hFMVzgi8Jxs3rhDElvMSkLTGljJX8QXFOOBEl5Qw_PL1relY8SekOY8IE4Y-Ls4o0XLC2Oi--3yZAwSDwEIe17G3aQUx2D-hLGdWKjFtChKTBa0A6TJ310KNvdh6RHmEKE8zRajSFHpyzfkBzQNqplKxZ0Rg8rEhpHWJ_xLowK2-1cmivooU5w75HA4Qhqt14D4RoB-ufFo-McgmeHe-L4vbd28_XH8qbT-8_Xl_dlJqJai4F16TTYFjX97ztCNPMdK0QvcqGENVS1mSvaqhYzbChLeMGQ8e1qJQSHcfVRfFm090t3QR9XnSOysldtJOKqwzKyj8Rb0c5hL3kbRaveRZ4dRSI4esCaZaTzX45pzyEJUla5_68qer2_9SKiIayihzGevkX9S4s0WcnMqsWLWkIF5l1ubEG5UBab0IeUefTw2R1dt_Y_H_FCRUMC0pyAd0KdAwpRTCnRQmWh1DJLVQyh0reh0oeNnzxu0Wnkp8pyoRqI6QM-QHir2H_IfsDNVLa4w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2348917168</pqid></control><display><type>article</type><title>Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Fiamegos, Yiannis ; Dumitrascu, Catalina ; Ghidotti, Michele ; de la Calle Guntiñas, Maria Beatriz</creator><creatorcontrib>Fiamegos, Yiannis ; Dumitrascu, Catalina ; Ghidotti, Michele ; de la Calle Guntiñas, Maria Beatriz</creatorcontrib><description>Honey is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.
Graphical abstract</description><identifier>ISSN: 1618-2642</identifier><identifier>EISSN: 1618-2650</identifier><identifier>DOI: 10.1007/s00216-019-02255-6</identifier><identifier>PMID: 31768593</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Analytical Chemistry ; Biochemistry ; Castanea ; Characterization and Evaluation of Materials ; Chemistry ; Chemistry and Materials Science ; chemometrics ; Chestnut ; Classification ; Commodities ; Discriminant Analysis ; Dispersion ; Energy consumption ; energy-dispersive X-ray analysis ; Eucalyptus ; Europe ; Fluorescence ; Food Science ; France ; Fraud ; Geographical distribution ; Geography ; Honey ; Honey - analysis ; Honey - classification ; Hungary ; Laboratory Medicine ; Lavandula ; Limit of Detection ; Masking ; Models, Chemical ; Monitoring/Environmental Analysis ; Multivariate Analysis ; New Zealand ; Portugal ; principal component analysis ; Principal components analysis ; provenance ; Reproducibility of Results ; Research Paper ; Robinia ; Romania ; Rosemary ; Spain ; Spectrometry, X-Ray Emission - methods ; Sugar ; sugars ; Thyme ; Trace elements ; X-ray fluorescence ; X-ray fluorescence spectroscopy ; X-ray spectroscopy</subject><ispartof>Analytical and bioanalytical chemistry, 2020-01, Vol.412 (2), p.463-472</ispartof><rights>The Author(s) 2019</rights><rights>COPYRIGHT 2020 Springer</rights><rights>Analytical and Bioanalytical Chemistry is a copyright of Springer, (2019). All Rights Reserved. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c583t-86c1bcef5bdd69b15c5fb988da2551a92571004e35450f2956f0eb6c83aa8b603</citedby><cites>FETCH-LOGICAL-c583t-86c1bcef5bdd69b15c5fb988da2551a92571004e35450f2956f0eb6c83aa8b603</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/s00216-019-02255-6$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00216-019-02255-6$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31768593$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fiamegos, Yiannis</creatorcontrib><creatorcontrib>Dumitrascu, Catalina</creatorcontrib><creatorcontrib>Ghidotti, Michele</creatorcontrib><creatorcontrib>de la Calle Guntiñas, Maria Beatriz</creatorcontrib><title>Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin</title><title>Analytical and bioanalytical chemistry</title><addtitle>Anal Bioanal Chem</addtitle><addtitle>Anal Bioanal Chem</addtitle><description>Honey is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.
Graphical abstract</description><subject>Analytical Chemistry</subject><subject>Biochemistry</subject><subject>Castanea</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>chemometrics</subject><subject>Chestnut</subject><subject>Classification</subject><subject>Commodities</subject><subject>Discriminant Analysis</subject><subject>Dispersion</subject><subject>Energy consumption</subject><subject>energy-dispersive X-ray analysis</subject><subject>Eucalyptus</subject><subject>Europe</subject><subject>Fluorescence</subject><subject>Food Science</subject><subject>France</subject><subject>Fraud</subject><subject>Geographical distribution</subject><subject>Geography</subject><subject>Honey</subject><subject>Honey - analysis</subject><subject>Honey - classification</subject><subject>Hungary</subject><subject>Laboratory Medicine</subject><subject>Lavandula</subject><subject>Limit of Detection</subject><subject>Masking</subject><subject>Models, Chemical</subject><subject>Monitoring/Environmental Analysis</subject><subject>Multivariate Analysis</subject><subject>New Zealand</subject><subject>Portugal</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>provenance</subject><subject>Reproducibility of Results</subject><subject>Research Paper</subject><subject>Robinia</subject><subject>Romania</subject><subject>Rosemary</subject><subject>Spain</subject><subject>Spectrometry, X-Ray Emission - methods</subject><subject>Sugar</subject><subject>sugars</subject><subject>Thyme</subject><subject>Trace elements</subject><subject>X-ray fluorescence</subject><subject>X-ray fluorescence spectroscopy</subject><subject>X-ray spectroscopy</subject><issn>1618-2642</issn><issn>1618-2650</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFUk2L1TAULaI44-gfcCEBN246JmmTphthGPyCATcOuAtpetNmSJNn0j7pP_Hnmjd9Pj8WShYJOeeee-_hFMVzgi8Jxs3rhDElvMSkLTGljJX8QXFOOBEl5Qw_PL1relY8SekOY8IE4Y-Ls4o0XLC2Oi--3yZAwSDwEIe17G3aQUx2D-hLGdWKjFtChKTBa0A6TJ310KNvdh6RHmEKE8zRajSFHpyzfkBzQNqplKxZ0Rg8rEhpHWJ_xLowK2-1cmivooU5w75HA4Qhqt14D4RoB-ufFo-McgmeHe-L4vbd28_XH8qbT-8_Xl_dlJqJai4F16TTYFjX97ztCNPMdK0QvcqGENVS1mSvaqhYzbChLeMGQ8e1qJQSHcfVRfFm090t3QR9XnSOysldtJOKqwzKyj8Rb0c5hL3kbRaveRZ4dRSI4esCaZaTzX45pzyEJUla5_68qer2_9SKiIayihzGevkX9S4s0WcnMqsWLWkIF5l1ubEG5UBab0IeUefTw2R1dt_Y_H_FCRUMC0pyAd0KdAwpRTCnRQmWh1DJLVQyh0reh0oeNnzxu0Wnkp8pyoRqI6QM-QHir2H_IfsDNVLa4w</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Fiamegos, Yiannis</creator><creator>Dumitrascu, Catalina</creator><creator>Ghidotti, Michele</creator><creator>de la Calle Guntiñas, Maria Beatriz</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><scope>C6C</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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8BQ</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>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KB.</scope><scope>KR7</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P64</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope></search><sort><creationdate>20200101</creationdate><title>Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin</title><author>Fiamegos, Yiannis ; Dumitrascu, Catalina ; Ghidotti, Michele ; de la Calle Guntiñas, Maria Beatriz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c583t-86c1bcef5bdd69b15c5fb988da2551a92571004e35450f2956f0eb6c83aa8b603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Analytical Chemistry</topic><topic>Biochemistry</topic><topic>Castanea</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>chemometrics</topic><topic>Chestnut</topic><topic>Classification</topic><topic>Commodities</topic><topic>Discriminant Analysis</topic><topic>Dispersion</topic><topic>Energy consumption</topic><topic>energy-dispersive X-ray analysis</topic><topic>Eucalyptus</topic><topic>Europe</topic><topic>Fluorescence</topic><topic>Food Science</topic><topic>France</topic><topic>Fraud</topic><topic>Geographical distribution</topic><topic>Geography</topic><topic>Honey</topic><topic>Honey - analysis</topic><topic>Honey - classification</topic><topic>Hungary</topic><topic>Laboratory Medicine</topic><topic>Lavandula</topic><topic>Limit of Detection</topic><topic>Masking</topic><topic>Models, Chemical</topic><topic>Monitoring/Environmental Analysis</topic><topic>Multivariate Analysis</topic><topic>New Zealand</topic><topic>Portugal</topic><topic>principal component analysis</topic><topic>Principal components analysis</topic><topic>provenance</topic><topic>Reproducibility of Results</topic><topic>Research Paper</topic><topic>Robinia</topic><topic>Romania</topic><topic>Rosemary</topic><topic>Spain</topic><topic>Spectrometry, X-Ray Emission - methods</topic><topic>Sugar</topic><topic>sugars</topic><topic>Thyme</topic><topic>Trace elements</topic><topic>X-ray fluorescence</topic><topic>X-ray fluorescence spectroscopy</topic><topic>X-ray spectroscopy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fiamegos, Yiannis</creatorcontrib><creatorcontrib>Dumitrascu, Catalina</creatorcontrib><creatorcontrib>Ghidotti, Michele</creatorcontrib><creatorcontrib>de la Calle Guntiñas, Maria Beatriz</creatorcontrib><collection>Springer Nature OA Free Journals</collection><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>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>METADEX</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>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</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>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Biological Science Collection</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Analytical and bioanalytical chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fiamegos, Yiannis</au><au>Dumitrascu, Catalina</au><au>Ghidotti, Michele</au><au>de la Calle Guntiñas, Maria Beatriz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin</atitle><jtitle>Analytical and bioanalytical chemistry</jtitle><stitle>Anal Bioanal Chem</stitle><addtitle>Anal Bioanal Chem</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>412</volume><issue>2</issue><spage>463</spage><epage>472</epage><pages>463-472</pages><issn>1618-2642</issn><eissn>1618-2650</eissn><abstract>Honey is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.
Graphical abstract</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31768593</pmid><doi>10.1007/s00216-019-02255-6</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1618-2642 |
| ispartof | Analytical and bioanalytical chemistry, 2020-01, Vol.412 (2), p.463-472 |
| issn | 1618-2642 1618-2650 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6992546 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Analytical Chemistry Biochemistry Castanea Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science chemometrics Chestnut Classification Commodities Discriminant Analysis Dispersion Energy consumption energy-dispersive X-ray analysis Eucalyptus Europe Fluorescence Food Science France Fraud Geographical distribution Geography Honey Honey - analysis Honey - classification Hungary Laboratory Medicine Lavandula Limit of Detection Masking Models, Chemical Monitoring/Environmental Analysis Multivariate Analysis New Zealand Portugal principal component analysis Principal components analysis provenance Reproducibility of Results Research Paper Robinia Romania Rosemary Spain Spectrometry, X-Ray Emission - methods Sugar sugars Thyme Trace elements X-ray fluorescence X-ray fluorescence spectroscopy X-ray spectroscopy |
| title | Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T02%3A49%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Use%20of%20energy-dispersive%20X-ray%20fluorescence%20combined%20with%20chemometric%20modelling%20to%20classify%20honey%20according%20to%20botanical%20variety%20and%20geographical%20origin&rft.jtitle=Analytical%20and%20bioanalytical%20chemistry&rft.au=Fiamegos,%20Yiannis&rft.date=2020-01-01&rft.volume=412&rft.issue=2&rft.spage=463&rft.epage=472&rft.pages=463-472&rft.issn=1618-2642&rft.eissn=1618-2650&rft_id=info:doi/10.1007/s00216-019-02255-6&rft_dat=%3Cgale_pubme%3EA612850821%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2348917168&rft_id=info:pmid/31768593&rft_galeid=A612850821&rfr_iscdi=true |