Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine
Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liqu...
Gespeichert in:
| Veröffentlicht in: | Analytical methods 2020-06, Vol.12 (23), p.325-331 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 331 |
|---|---|
| container_issue | 23 |
| container_start_page | 325 |
| container_title | Analytical methods |
| container_volume | 12 |
| creator | Bao, Qiwen Zhao, Hang Han, Siqingaowa Zhang, Chen Hasi, Wuliji |
| description | Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 μg mL
−1
for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.
Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. |
| doi_str_mv | 10.1039/d0ay00741b |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_2442848499</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2414405737</sourcerecordid><originalsourceid>FETCH-LOGICAL-c374t-fbaa0725ad144f1893d7c89b8393c50a9b47f5dc7678f37a3b3d72545904265c3</originalsourceid><addsrcrecordid>eNpd0VtLBCEUB3CJosvWS--F0EsEUzo64_jYvSAIujz0NJzxQtauM6szxH773La2CARFf54j_hHapeSYEiZPNIEZIYLTZgVtUlHITJZCri7XJdlAWzG-EVJKVtJ1tMFyyQgt8000exyCBWUy41_BK6PxA0zA49gZ1Yc2qrabYdsGHKBzGjttfO-sU9C71mPwGk8H-LvVWnw1dg34aILzOA3trDUh3cPvzus4Fx_Om220ZmEczc73PELPV5dP5zfZ3f317fnpXaaY4H1mGwAi8gI05dzSSjItVCWbikmmCgKy4cIWWolSVJYJYE0CecELSXheFoqN0OGibhfa6WBiX09cVGY8Bm_aIdY553nFKy5logf_6Fs7BJ9el1RqTwrBRFJHC6XS_8RgbN0FN4Ewqymp54HUF-T05SuQs4T3v0sOzcToJf1JIIG9BQhRLU9_E2Wf_DmQew</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2414405737</pqid></control><display><type>article</type><title>Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine</title><source>MEDLINE</source><source>Royal Society Of Chemistry Journals 2008-</source><creator>Bao, Qiwen ; Zhao, Hang ; Han, Siqingaowa ; Zhang, Chen ; Hasi, Wuliji</creator><creatorcontrib>Bao, Qiwen ; Zhao, Hang ; Han, Siqingaowa ; Zhang, Chen ; Hasi, Wuliji</creatorcontrib><description>Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 μg mL
−1
for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.
Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior.</description><identifier>ISSN: 1759-9660</identifier><identifier>EISSN: 1759-9679</identifier><identifier>DOI: 10.1039/d0ay00741b</identifier><identifier>PMID: 32930162</identifier><language>eng</language><publisher>England: Royal Society of Chemistry</publisher><subject>Algorithms ; Beer ; Benzimidazoles ; Classification ; Correlation coefficient ; Correlation coefficients ; Drugs ; Grapes ; Learning algorithms ; Least squares method ; Liquor ; Machine learning ; Principal components analysis ; Psychedelic drugs ; Raman spectroscopy ; Spectroscopy ; Spectrum analysis ; Spectrum Analysis, Raman ; Support Vector Machine ; Support vector machines ; Wine ; Wine - analysis ; Wines ; Workload</subject><ispartof>Analytical methods, 2020-06, Vol.12 (23), p.325-331</ispartof><rights>Copyright Royal Society of Chemistry 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c374t-fbaa0725ad144f1893d7c89b8393c50a9b47f5dc7678f37a3b3d72545904265c3</citedby><cites>FETCH-LOGICAL-c374t-fbaa0725ad144f1893d7c89b8393c50a9b47f5dc7678f37a3b3d72545904265c3</cites><orcidid>0000-0003-3248-1075 ; 0000-0002-9903-0183 ; 0000-0003-2424-9225</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32930162$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bao, Qiwen</creatorcontrib><creatorcontrib>Zhao, Hang</creatorcontrib><creatorcontrib>Han, Siqingaowa</creatorcontrib><creatorcontrib>Zhang, Chen</creatorcontrib><creatorcontrib>Hasi, Wuliji</creatorcontrib><title>Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine</title><title>Analytical methods</title><addtitle>Anal Methods</addtitle><description>Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 μg mL
−1
for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.
Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior.</description><subject>Algorithms</subject><subject>Beer</subject><subject>Benzimidazoles</subject><subject>Classification</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Drugs</subject><subject>Grapes</subject><subject>Learning algorithms</subject><subject>Least squares method</subject><subject>Liquor</subject><subject>Machine learning</subject><subject>Principal components analysis</subject><subject>Psychedelic drugs</subject><subject>Raman spectroscopy</subject><subject>Spectroscopy</subject><subject>Spectrum analysis</subject><subject>Spectrum Analysis, Raman</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Wine</subject><subject>Wine - analysis</subject><subject>Wines</subject><subject>Workload</subject><issn>1759-9660</issn><issn>1759-9679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpd0VtLBCEUB3CJosvWS--F0EsEUzo64_jYvSAIujz0NJzxQtauM6szxH773La2CARFf54j_hHapeSYEiZPNIEZIYLTZgVtUlHITJZCri7XJdlAWzG-EVJKVtJ1tMFyyQgt8000exyCBWUy41_BK6PxA0zA49gZ1Yc2qrabYdsGHKBzGjttfO-sU9C71mPwGk8H-LvVWnw1dg34aILzOA3trDUh3cPvzus4Fx_Om220ZmEczc73PELPV5dP5zfZ3f317fnpXaaY4H1mGwAi8gI05dzSSjItVCWbikmmCgKy4cIWWolSVJYJYE0CecELSXheFoqN0OGibhfa6WBiX09cVGY8Bm_aIdY553nFKy5logf_6Fs7BJ9el1RqTwrBRFJHC6XS_8RgbN0FN4Ewqymp54HUF-T05SuQs4T3v0sOzcToJf1JIIG9BQhRLU9_E2Wf_DmQew</recordid><startdate>20200618</startdate><enddate>20200618</enddate><creator>Bao, Qiwen</creator><creator>Zhao, Hang</creator><creator>Han, Siqingaowa</creator><creator>Zhang, Chen</creator><creator>Hasi, Wuliji</creator><general>Royal Society of Chemistry</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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SE</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>H8G</scope><scope>JG9</scope><scope>L7M</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-3248-1075</orcidid><orcidid>https://orcid.org/0000-0002-9903-0183</orcidid><orcidid>https://orcid.org/0000-0003-2424-9225</orcidid></search><sort><creationdate>20200618</creationdate><title>Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine</title><author>Bao, Qiwen ; Zhao, Hang ; Han, Siqingaowa ; Zhang, Chen ; Hasi, Wuliji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c374t-fbaa0725ad144f1893d7c89b8393c50a9b47f5dc7678f37a3b3d72545904265c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Beer</topic><topic>Benzimidazoles</topic><topic>Classification</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Drugs</topic><topic>Grapes</topic><topic>Learning algorithms</topic><topic>Least squares method</topic><topic>Liquor</topic><topic>Machine learning</topic><topic>Principal components analysis</topic><topic>Psychedelic drugs</topic><topic>Raman spectroscopy</topic><topic>Spectroscopy</topic><topic>Spectrum analysis</topic><topic>Spectrum Analysis, Raman</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>Wine</topic><topic>Wine - analysis</topic><topic>Wines</topic><topic>Workload</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bao, Qiwen</creatorcontrib><creatorcontrib>Zhao, Hang</creatorcontrib><creatorcontrib>Han, Siqingaowa</creatorcontrib><creatorcontrib>Zhang, Chen</creatorcontrib><creatorcontrib>Hasi, Wuliji</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Analytical methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bao, Qiwen</au><au>Zhao, Hang</au><au>Han, Siqingaowa</au><au>Zhang, Chen</au><au>Hasi, Wuliji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine</atitle><jtitle>Analytical methods</jtitle><addtitle>Anal Methods</addtitle><date>2020-06-18</date><risdate>2020</risdate><volume>12</volume><issue>23</issue><spage>325</spage><epage>331</epage><pages>325-331</pages><issn>1759-9660</issn><eissn>1759-9679</eissn><abstract>Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 μg mL
−1
for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.
Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior.</abstract><cop>England</cop><pub>Royal Society of Chemistry</pub><pmid>32930162</pmid><doi>10.1039/d0ay00741b</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-3248-1075</orcidid><orcidid>https://orcid.org/0000-0002-9903-0183</orcidid><orcidid>https://orcid.org/0000-0003-2424-9225</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1759-9660 |
| ispartof | Analytical methods, 2020-06, Vol.12 (23), p.325-331 |
| issn | 1759-9660 1759-9679 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2442848499 |
| source | MEDLINE; Royal Society Of Chemistry Journals 2008- |
| subjects | Algorithms Beer Benzimidazoles Classification Correlation coefficient Correlation coefficients Drugs Grapes Learning algorithms Least squares method Liquor Machine learning Principal components analysis Psychedelic drugs Raman spectroscopy Spectroscopy Spectrum analysis Spectrum Analysis, Raman Support Vector Machine Support vector machines Wine Wine - analysis Wines Workload |
| title | Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T12%3A50%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Surface-enhanced%20Raman%20spectroscopy%20for%20rapid%20identification%20and%20quantification%20of%20Flibanserin%20in%20different%20kinds%20of%20wine&rft.jtitle=Analytical%20methods&rft.au=Bao,%20Qiwen&rft.date=2020-06-18&rft.volume=12&rft.issue=23&rft.spage=325&rft.epage=331&rft.pages=325-331&rft.issn=1759-9660&rft.eissn=1759-9679&rft_id=info:doi/10.1039/d0ay00741b&rft_dat=%3Cproquest_pubme%3E2414405737%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2414405737&rft_id=info:pmid/32930162&rfr_iscdi=true |