Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology
Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal comp...
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| Veröffentlicht in: | Applied optics (2004) 2019-11, Vol.58 (31), p.8396-8403 |
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| container_title | Applied optics (2004) |
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| creator | Gong, Aiping Gu, Weimin Zhao, Zhenyu Shao, Yongni |
| description | Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal component-linear discriminant analysis, back propagation-artificial neural network (BP-ANN), and sensitive wavelengths-linear discriminant analysis were used to build models to identify different copper concentrations using the spectral data after pretreatment. The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4. |
| doi_str_mv | 10.1364/AO.58.008396 |
| format | Article |
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The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal component-linear discriminant analysis, back propagation-artificial neural network (BP-ANN), and sensitive wavelengths-linear discriminant analysis were used to build models to identify different copper concentrations using the spectral data after pretreatment. The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4.</description><identifier>ISSN: 1559-128X</identifier><identifier>EISSN: 2155-3165</identifier><identifier>EISSN: 1539-4522</identifier><identifier>DOI: 10.1364/AO.58.008396</identifier><identifier>PMID: 31873321</identifier><language>eng</language><publisher>United States: Optical Society of America</publisher><subject>Algae ; Artificial neural networks ; Biomass ; Carotenoids ; Carotenoids - analysis ; Chlorophyll ; Chlorophyll - analysis ; Copper ; Copper - analysis ; Data acquisition ; Discriminant Analysis ; Heavy metals ; Mapping ; Microalgae - chemistry ; Neural Networks, Computer ; Pretreatment ; Principal Component Analysis ; Spectra ; Spectrum Analysis, Raman - methods ; Wave propagation</subject><ispartof>Applied optics (2004), 2019-11, Vol.58 (31), p.8396-8403</ispartof><rights>Copyright Optical Society of America Nov 1, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-cd6ba73b880044ad63138dbffd067999d882f6d6f9c73bfa9125c205acc895e83</citedby><cites>FETCH-LOGICAL-c319t-cd6ba73b880044ad63138dbffd067999d882f6d6f9c73bfa9125c205acc895e83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,3258,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31873321$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gong, Aiping</creatorcontrib><creatorcontrib>Gu, Weimin</creatorcontrib><creatorcontrib>Zhao, Zhenyu</creatorcontrib><creatorcontrib>Shao, Yongni</creatorcontrib><title>Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology</title><title>Applied optics (2004)</title><addtitle>Appl Opt</addtitle><description>Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. 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The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4.</description><subject>Algae</subject><subject>Artificial neural networks</subject><subject>Biomass</subject><subject>Carotenoids</subject><subject>Carotenoids - analysis</subject><subject>Chlorophyll</subject><subject>Chlorophyll - analysis</subject><subject>Copper</subject><subject>Copper - analysis</subject><subject>Data acquisition</subject><subject>Discriminant Analysis</subject><subject>Heavy metals</subject><subject>Mapping</subject><subject>Microalgae - chemistry</subject><subject>Neural Networks, Computer</subject><subject>Pretreatment</subject><subject>Principal Component Analysis</subject><subject>Spectra</subject><subject>Spectrum Analysis, Raman - methods</subject><subject>Wave propagation</subject><issn>1559-128X</issn><issn>2155-3165</issn><issn>1539-4522</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpd0M9LwzAUB_Agips_bp6l4MWDnUlfkybHMfwxGAxEwVtJ02TraJvatEL_e1M6PXh6Ifnk8d4XoRuCFwRY_LjcLihfYMxBsBM0jwilIRBGT9HcH0VIIv45QxfOHTAGGovkHM2A8AQgInOk17muu8IUSnaFrQNrgr2W30NQ6U6WQTYEnXZdUe-CqlCtleVO6qB344WytbHKozdZyXp6d41WnS_KNuNPta9taXfDFTozsnT6-lgv0cfz0_vqNdxsX9ar5SZUQEQXqpxlMoGMc4zjWOYMCPA8MybHLBFC5JxHhuXMCOWVkYJEVEWYSqW4oJrDJbqf-jat_er94GlVOKXLUtba9i6NADBAghn19O4fPdi-rf10XhGfkwAKXj1MatzNtdqkTVtUsh1SgtMx_nS5TSlPp_g9vz027bNK53_4N2_4AVAQgQc</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Gong, Aiping</creator><creator>Gu, Weimin</creator><creator>Zhao, Zhenyu</creator><creator>Shao, Yongni</creator><general>Optical Society of America</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>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope></search><sort><creationdate>20191101</creationdate><title>Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology</title><author>Gong, Aiping ; Gu, Weimin ; Zhao, Zhenyu ; Shao, Yongni</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-cd6ba73b880044ad63138dbffd067999d882f6d6f9c73bfa9125c205acc895e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algae</topic><topic>Artificial neural networks</topic><topic>Biomass</topic><topic>Carotenoids</topic><topic>Carotenoids - analysis</topic><topic>Chlorophyll</topic><topic>Chlorophyll - analysis</topic><topic>Copper</topic><topic>Copper - analysis</topic><topic>Data acquisition</topic><topic>Discriminant Analysis</topic><topic>Heavy metals</topic><topic>Mapping</topic><topic>Microalgae - chemistry</topic><topic>Neural Networks, Computer</topic><topic>Pretreatment</topic><topic>Principal Component Analysis</topic><topic>Spectra</topic><topic>Spectrum Analysis, Raman - methods</topic><topic>Wave propagation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gong, Aiping</creatorcontrib><creatorcontrib>Gu, Weimin</creatorcontrib><creatorcontrib>Zhao, Zhenyu</creatorcontrib><creatorcontrib>Shao, Yongni</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Applied optics (2004)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gong, Aiping</au><au>Gu, Weimin</au><au>Zhao, Zhenyu</au><au>Shao, Yongni</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology</atitle><jtitle>Applied optics (2004)</jtitle><addtitle>Appl Opt</addtitle><date>2019-11-01</date><risdate>2019</risdate><volume>58</volume><issue>31</issue><spage>8396</spage><epage>8403</epage><pages>8396-8403</pages><issn>1559-128X</issn><eissn>2155-3165</eissn><eissn>1539-4522</eissn><abstract>Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal component-linear discriminant analysis, back propagation-artificial neural network (BP-ANN), and sensitive wavelengths-linear discriminant analysis were used to build models to identify different copper concentrations using the spectral data after pretreatment. The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4.</abstract><cop>United States</cop><pub>Optical Society of America</pub><pmid>31873321</pmid><doi>10.1364/AO.58.008396</doi><tpages>8</tpages></addata></record> |
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| ispartof | Applied optics (2004), 2019-11, Vol.58 (31), p.8396-8403 |
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| source | MEDLINE; Alma/SFX Local Collection; Optica Publishing Group Journals |
| subjects | Algae Artificial neural networks Biomass Carotenoids Carotenoids - analysis Chlorophyll Chlorophyll - analysis Copper Copper - analysis Data acquisition Discriminant Analysis Heavy metals Mapping Microalgae - chemistry Neural Networks, Computer Pretreatment Principal Component Analysis Spectra Spectrum Analysis, Raman - methods Wave propagation |
| title | Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology |
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