SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee
•SPME-GC-MS for detection of fraud in roasted and ground coffee.•Potential chemical markers of the most common adulterants in coffee fraud.•Chemometrics analysis indicate volatile compounds as possible markers for food fraud. Roasted ground coffee has been intentionally adulterated for economic reve...
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Veröffentlicht in: | Food chemistry 2024-07, Vol.446, p.138862-138862, Article 138862 |
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creator | Couto, Cinthia de Carvalho Chávez, Davy William Hidalgo Oliveira, Edna Maria Morais Freitas-Silva, Otniel Casal, Susana |
description | •SPME-GC-MS for detection of fraud in roasted and ground coffee.•Potential chemical markers of the most common adulterants in coffee fraud.•Chemometrics analysis indicate volatile compounds as possible markers for food fraud.
Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool. |
doi_str_mv | 10.1016/j.foodchem.2024.138862 |
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Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool.</description><identifier>ISSN: 0308-8146</identifier><identifier>EISSN: 1873-7072</identifier><identifier>DOI: 10.1016/j.foodchem.2024.138862</identifier><identifier>PMID: 38430775</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>adulterants ; Adulteration ; Authenticity ; barley ; Cereal ; chemometrics ; Chromatography ; Coffea ; Coffea arabica ; Coffea canephora ; corn ; discriminant analysis ; food chemistry ; fraud ; Gas Chromatography-Mass Spectrometry ; Glycine max ; heat ; income ; least squares ; Least-Squares Analysis ; metabolomics ; principal component analysis ; rice ; Seeds ; Solid Phase Microextraction ; soybeans</subject><ispartof>Food chemistry, 2024-07, Vol.446, p.138862-138862, Article 138862</ispartof><rights>2024 Elsevier Ltd</rights><rights>Copyright © 2024 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c401t-4d954c33c3d69323e0e0cd56ec9cd30893ad19d267c8f8746bddbcf1d6f852f73</citedby><cites>FETCH-LOGICAL-c401t-4d954c33c3d69323e0e0cd56ec9cd30893ad19d267c8f8746bddbcf1d6f852f73</cites><orcidid>0000-0002-7658-8010</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0308814624005119$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38430775$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Couto, Cinthia de Carvalho</creatorcontrib><creatorcontrib>Chávez, Davy William Hidalgo</creatorcontrib><creatorcontrib>Oliveira, Edna Maria Morais</creatorcontrib><creatorcontrib>Freitas-Silva, Otniel</creatorcontrib><creatorcontrib>Casal, Susana</creatorcontrib><title>SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee</title><title>Food chemistry</title><addtitle>Food Chem</addtitle><description>•SPME-GC-MS for detection of fraud in roasted and ground coffee.•Potential chemical markers of the most common adulterants in coffee fraud.•Chemometrics analysis indicate volatile compounds as possible markers for food fraud.
Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool.</description><subject>adulterants</subject><subject>Adulteration</subject><subject>Authenticity</subject><subject>barley</subject><subject>Cereal</subject><subject>chemometrics</subject><subject>Chromatography</subject><subject>Coffea</subject><subject>Coffea arabica</subject><subject>Coffea canephora</subject><subject>corn</subject><subject>discriminant analysis</subject><subject>food chemistry</subject><subject>fraud</subject><subject>Gas Chromatography-Mass Spectrometry</subject><subject>Glycine max</subject><subject>heat</subject><subject>income</subject><subject>least squares</subject><subject>Least-Squares Analysis</subject><subject>metabolomics</subject><subject>principal component analysis</subject><subject>rice</subject><subject>Seeds</subject><subject>Solid Phase Microextraction</subject><subject>soybeans</subject><issn>0308-8146</issn><issn>1873-7072</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkctu1DAUhi0EokPhFSov2WTqS2I7O9CoLZVagVRYWx77uPWQxMF2KvUteGQcTcu2K3vxX3T-D6EzSraUUHF-2PoYnX2AccsIa7eUKyXYG7ShSvJGEsneog3hRDWKtuIEfcj5QAhhhKr36ISrlhMpuw36e_fj9qK52jW3d3iZikn3UMDhEYrZxyGOwWZs5jlFYx9wiTg4mErwT3iOZf2ZAT_GwZQwALZxnOMyuerIeDTpN6SMfUzYJ7M47GqyLSFOOEy4Bua1yEwO36fVVe3eA3xE77wZMnx6fk_Rr8uLn7tvzc33q-vd15vGtoSWpnV911rOLXei54wDAWJdJ8D21tWze24c7R0T0iqvZCv2zu2tp0541TEv-Sn6fMytt_1ZIBc9hmxhGMwEccma045LSpkSr0pZzyXnXUVQpeIotSnmnMDrOYU6xZOmRK_g9EG_gNMrOH0EV41nzx3LfgT33_ZCqgq-HAVQR3kMkHS2ASYLLqQ6q3YxvNbxD4Xhr2Q</recordid><startdate>20240715</startdate><enddate>20240715</enddate><creator>Couto, Cinthia de Carvalho</creator><creator>Chávez, Davy William Hidalgo</creator><creator>Oliveira, Edna Maria Morais</creator><creator>Freitas-Silva, Otniel</creator><creator>Casal, Susana</creator><general>Elsevier Ltd</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>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-7658-8010</orcidid></search><sort><creationdate>20240715</creationdate><title>SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee</title><author>Couto, Cinthia de Carvalho ; Chávez, Davy William Hidalgo ; Oliveira, Edna Maria Morais ; Freitas-Silva, Otniel ; Casal, Susana</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-4d954c33c3d69323e0e0cd56ec9cd30893ad19d267c8f8746bddbcf1d6f852f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>adulterants</topic><topic>Adulteration</topic><topic>Authenticity</topic><topic>barley</topic><topic>Cereal</topic><topic>chemometrics</topic><topic>Chromatography</topic><topic>Coffea</topic><topic>Coffea arabica</topic><topic>Coffea canephora</topic><topic>corn</topic><topic>discriminant analysis</topic><topic>food chemistry</topic><topic>fraud</topic><topic>Gas Chromatography-Mass Spectrometry</topic><topic>Glycine max</topic><topic>heat</topic><topic>income</topic><topic>least squares</topic><topic>Least-Squares Analysis</topic><topic>metabolomics</topic><topic>principal component analysis</topic><topic>rice</topic><topic>Seeds</topic><topic>Solid Phase Microextraction</topic><topic>soybeans</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Couto, Cinthia de Carvalho</creatorcontrib><creatorcontrib>Chávez, Davy William Hidalgo</creatorcontrib><creatorcontrib>Oliveira, Edna Maria Morais</creatorcontrib><creatorcontrib>Freitas-Silva, Otniel</creatorcontrib><creatorcontrib>Casal, Susana</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Couto, Cinthia de Carvalho</au><au>Chávez, Davy William Hidalgo</au><au>Oliveira, Edna Maria Morais</au><au>Freitas-Silva, Otniel</au><au>Casal, Susana</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee</atitle><jtitle>Food chemistry</jtitle><addtitle>Food Chem</addtitle><date>2024-07-15</date><risdate>2024</risdate><volume>446</volume><spage>138862</spage><epage>138862</epage><pages>138862-138862</pages><artnum>138862</artnum><issn>0308-8146</issn><eissn>1873-7072</eissn><abstract>•SPME-GC-MS for detection of fraud in roasted and ground coffee.•Potential chemical markers of the most common adulterants in coffee fraud.•Chemometrics analysis indicate volatile compounds as possible markers for food fraud.
Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38430775</pmid><doi>10.1016/j.foodchem.2024.138862</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-7658-8010</orcidid></addata></record> |
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subjects | adulterants Adulteration Authenticity barley Cereal chemometrics Chromatography Coffea Coffea arabica Coffea canephora corn discriminant analysis food chemistry fraud Gas Chromatography-Mass Spectrometry Glycine max heat income least squares Least-Squares Analysis metabolomics principal component analysis rice Seeds Solid Phase Microextraction soybeans |
title | SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee |
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