Exploratory Analysis of South American Wines Using Artificial Intelligence
In this work, microwave-induced plasma optical emission spectrometry was applied for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentinian wines. Then, logistic regression, support vector machine, and d...
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| Veröffentlicht in: | Biological trace element research 2023-09, Vol.201 (9), p.4590-4599 |
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| creator | Carneiro, Candice N. Gomez, Federico J. V. Spisso, Adrian Silva, Maria Fernanda Santos, Jorge L. O. Dias, Fabio de S. |
| description | In this work, microwave-induced plasma optical emission spectrometry was applied for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentinian wines. Then, logistic regression, support vector machine, and decision tree for exploratory analysis and comparison of these algorithms in differentiating red wine samples by region of origin were carried out. All wine samples were classified according to their geographical origin. The quantification limits (mg L
−1
) were P: 0.06, B: 0.08, K: 0.17, Mn: 0.002, Cr: 0.002, and Al: 0.02. The accuracy of the method was evaluated by analyzing the wine samples by ICP OES for results’ comparison. The concentrations in mg L
−1
found for each element in wine samples were as follows: Al ( |
| doi_str_mv | 10.1007/s12011-022-03529-4 |
| format | Article |
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−1
) were P: 0.06, B: 0.08, K: 0.17, Mn: 0.002, Cr: 0.002, and Al: 0.02. The accuracy of the method was evaluated by analyzing the wine samples by ICP OES for results’ comparison. The concentrations in mg L
−1
found for each element in wine samples were as follows: Al (< 0.02–1.82), Cr (0.15–0.50), Mn (< 0.002–0.8), P (97–277), B (1.7–11.6), Pb (< 0.06–0.3), Na (8.84–41.57), and K (604–1701), in mg L
−1
.</description><identifier>ISSN: 0163-4984</identifier><identifier>EISSN: 1559-0720</identifier><identifier>DOI: 10.1007/s12011-022-03529-4</identifier><identifier>PMID: 36550265</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Acid digestion ; Algorithms ; Aluminum ; Analytical chemistry ; Artificial intelligence ; Biochemistry ; Biomedical and Life Sciences ; Biotechnology ; chemical species ; Cluster analysis ; Decision analysis ; decision support systems ; Decision trees ; Ions ; Life Sciences ; Manganese ; microwave treatment ; Nutrition ; Oncology ; Optical emission spectroscopy ; provenance ; red wines ; regression analysis ; Spectrometry ; spectroscopy ; Support vector machines ; wet digestion method ; Wine ; Wines</subject><ispartof>Biological trace element research, 2023-09, Vol.201 (9), p.4590-4599</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-ff680f826b563608c6429d1a00eb2a88f4d9287422d6404bdd3c838cba17ad543</citedby><cites>FETCH-LOGICAL-c408t-ff680f826b563608c6429d1a00eb2a88f4d9287422d6404bdd3c838cba17ad543</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/s12011-022-03529-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12011-022-03529-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36550265$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Carneiro, Candice N.</creatorcontrib><creatorcontrib>Gomez, Federico J. V.</creatorcontrib><creatorcontrib>Spisso, Adrian</creatorcontrib><creatorcontrib>Silva, Maria Fernanda</creatorcontrib><creatorcontrib>Santos, Jorge L. O.</creatorcontrib><creatorcontrib>Dias, Fabio de S.</creatorcontrib><title>Exploratory Analysis of South American Wines Using Artificial Intelligence</title><title>Biological trace element research</title><addtitle>Biol Trace Elem Res</addtitle><addtitle>Biol Trace Elem Res</addtitle><description>In this work, microwave-induced plasma optical emission spectrometry was applied for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentinian wines. Then, logistic regression, support vector machine, and decision tree for exploratory analysis and comparison of these algorithms in differentiating red wine samples by region of origin were carried out. All wine samples were classified according to their geographical origin. The quantification limits (mg L
−1
) were P: 0.06, B: 0.08, K: 0.17, Mn: 0.002, Cr: 0.002, and Al: 0.02. The accuracy of the method was evaluated by analyzing the wine samples by ICP OES for results’ comparison. The concentrations in mg L
−1
found for each element in wine samples were as follows: Al (< 0.02–1.82), Cr (0.15–0.50), Mn (< 0.002–0.8), P (97–277), B (1.7–11.6), Pb (< 0.06–0.3), Na (8.84–41.57), and K (604–1701), in mg L
−1
.</description><subject>Acid digestion</subject><subject>Algorithms</subject><subject>Aluminum</subject><subject>Analytical chemistry</subject><subject>Artificial intelligence</subject><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biotechnology</subject><subject>chemical species</subject><subject>Cluster analysis</subject><subject>Decision analysis</subject><subject>decision support systems</subject><subject>Decision trees</subject><subject>Ions</subject><subject>Life Sciences</subject><subject>Manganese</subject><subject>microwave treatment</subject><subject>Nutrition</subject><subject>Oncology</subject><subject>Optical emission spectroscopy</subject><subject>provenance</subject><subject>red wines</subject><subject>regression analysis</subject><subject>Spectrometry</subject><subject>spectroscopy</subject><subject>Support vector machines</subject><subject>wet digestion method</subject><subject>Wine</subject><subject>Wines</subject><issn>0163-4984</issn><issn>1559-0720</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqFkUtLxDAQx4Mo7vr4Ah6k4MVLdfJsclxkfSF4UPEY0jRds3TbNWnB_fZmXR_gQSEwh_zmP8P8EDrCcIYBivOICWCcAyE5UE5UzrbQGHOucigIbKMxYEFzpiQbob0Y5wC4IIruohEVnAMRfIxup2_Lpgum78Iqm7SmWUUfs67OHrqhf8kmCxe8NW327FsXs6fo21k2Cb2vvfWmyW7a3jWNn7nWugO0U5smusPPuo-eLqePF9f53f3VzcXkLrcMZJ_XtZBQSyJKLqgAaQUjqsIGwJXESFmzShFZMEIqwYCVVUWtpNKWBhem4ozuo9NN7jJ0r4OLvV74aNMapnXdEDXFfP0Ekf-ipOASg1LAE3ryC513Q0gHSVQaj0EoKBJFNpQNXYzB1XoZ_MKElcag11L0RopOUvSHFL1e-PgzeigXrvpu-bKQALoBYvpqZy78zP4j9h1YtZUt</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Carneiro, Candice N.</creator><creator>Gomez, Federico J. 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O. ; Dias, Fabio de S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-ff680f826b563608c6429d1a00eb2a88f4d9287422d6404bdd3c838cba17ad543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Acid digestion</topic><topic>Algorithms</topic><topic>Aluminum</topic><topic>Analytical chemistry</topic><topic>Artificial intelligence</topic><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biotechnology</topic><topic>chemical species</topic><topic>Cluster analysis</topic><topic>Decision analysis</topic><topic>decision support systems</topic><topic>Decision trees</topic><topic>Ions</topic><topic>Life Sciences</topic><topic>Manganese</topic><topic>microwave treatment</topic><topic>Nutrition</topic><topic>Oncology</topic><topic>Optical emission spectroscopy</topic><topic>provenance</topic><topic>red wines</topic><topic>regression analysis</topic><topic>Spectrometry</topic><topic>spectroscopy</topic><topic>Support vector machines</topic><topic>wet digestion method</topic><topic>Wine</topic><topic>Wines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Carneiro, Candice N.</creatorcontrib><creatorcontrib>Gomez, Federico J. 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V.</au><au>Spisso, Adrian</au><au>Silva, Maria Fernanda</au><au>Santos, Jorge L. O.</au><au>Dias, Fabio de S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploratory Analysis of South American Wines Using Artificial Intelligence</atitle><jtitle>Biological trace element research</jtitle><stitle>Biol Trace Elem Res</stitle><addtitle>Biol Trace Elem Res</addtitle><date>2023-09-01</date><risdate>2023</risdate><volume>201</volume><issue>9</issue><spage>4590</spage><epage>4599</epage><pages>4590-4599</pages><issn>0163-4984</issn><eissn>1559-0720</eissn><abstract>In this work, microwave-induced plasma optical emission spectrometry was applied for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentinian wines. Then, logistic regression, support vector machine, and decision tree for exploratory analysis and comparison of these algorithms in differentiating red wine samples by region of origin were carried out. All wine samples were classified according to their geographical origin. The quantification limits (mg L
−1
) were P: 0.06, B: 0.08, K: 0.17, Mn: 0.002, Cr: 0.002, and Al: 0.02. The accuracy of the method was evaluated by analyzing the wine samples by ICP OES for results’ comparison. The concentrations in mg L
−1
found for each element in wine samples were as follows: Al (< 0.02–1.82), Cr (0.15–0.50), Mn (< 0.002–0.8), P (97–277), B (1.7–11.6), Pb (< 0.06–0.3), Na (8.84–41.57), and K (604–1701), in mg L
−1
.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>36550265</pmid><doi>10.1007/s12011-022-03529-4</doi><tpages>10</tpages></addata></record> |
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| subjects | Acid digestion Algorithms Aluminum Analytical chemistry Artificial intelligence Biochemistry Biomedical and Life Sciences Biotechnology chemical species Cluster analysis Decision analysis decision support systems Decision trees Ions Life Sciences Manganese microwave treatment Nutrition Oncology Optical emission spectroscopy provenance red wines regression analysis Spectrometry spectroscopy Support vector machines wet digestion method Wine Wines |
| title | Exploratory Analysis of South American Wines Using Artificial Intelligence |
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