Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves
Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection...
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| Veröffentlicht in: | Analytical chemistry (Washington) 2017-07, Vol.89 (14), p.7593-7600 |
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| creator | Peng, Jiyu He, Yong Ye, Lanhan Shen, Tingting Liu, Fei Kong, Wenwen Liu, Xiaodan Zhao, Yun |
| description | Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work. |
| doi_str_mv | 10.1021/acs.analchem.7b01441 |
| format | Article |
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However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.</description><identifier>ISSN: 0003-2700</identifier><identifier>EISSN: 1520-6882</identifier><identifier>DOI: 10.1021/acs.analchem.7b01441</identifier><identifier>PMID: 28625048</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Calibration ; Case studies ; Chemistry ; Chromium ; Correlation coefficient ; Correlation coefficients ; Drying ; Electron density ; Flowers & plants ; Food plants ; Food safety ; Heavy metals ; Laser induced breakdown spectroscopy ; Lasers ; Least-Squares Analysis ; Leaves ; Mathematical models ; Metals, Heavy - analysis ; Moisture ; Moisture content ; Oryza - chemistry ; Oxidation-Reduction ; Plant Leaves - chemistry ; Regression analysis ; Rice ; Spectroscopy ; Spectrum Analysis ; Wettability</subject><ispartof>Analytical chemistry (Washington), 2017-07, Vol.89 (14), p.7593-7600</ispartof><rights>Copyright © 2017 American Chemical Society</rights><rights>Copyright American Chemical Society Jul 18, 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a376t-bbe4075aeebe95f1a1d9061dc0a9a9dcdc1952c0e5febd648476471676582bd63</citedby><cites>FETCH-LOGICAL-a376t-bbe4075aeebe95f1a1d9061dc0a9a9dcdc1952c0e5febd648476471676582bd63</cites><orcidid>0000-0003-0266-6896</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.analchem.7b01441$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.analchem.7b01441$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,776,780,2752,27053,27901,27902,56713,56763</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28625048$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Peng, Jiyu</creatorcontrib><creatorcontrib>He, Yong</creatorcontrib><creatorcontrib>Ye, Lanhan</creatorcontrib><creatorcontrib>Shen, Tingting</creatorcontrib><creatorcontrib>Liu, Fei</creatorcontrib><creatorcontrib>Kong, Wenwen</creatorcontrib><creatorcontrib>Liu, Xiaodan</creatorcontrib><creatorcontrib>Zhao, Yun</creatorcontrib><title>Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves</title><title>Analytical chemistry (Washington)</title><addtitle>Anal. Chem</addtitle><description>Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.</description><subject>Calibration</subject><subject>Case studies</subject><subject>Chemistry</subject><subject>Chromium</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Drying</subject><subject>Electron density</subject><subject>Flowers & plants</subject><subject>Food plants</subject><subject>Food safety</subject><subject>Heavy metals</subject><subject>Laser induced breakdown spectroscopy</subject><subject>Lasers</subject><subject>Least-Squares Analysis</subject><subject>Leaves</subject><subject>Mathematical models</subject><subject>Metals, Heavy - analysis</subject><subject>Moisture</subject><subject>Moisture content</subject><subject>Oryza - chemistry</subject><subject>Oxidation-Reduction</subject><subject>Plant Leaves - chemistry</subject><subject>Regression analysis</subject><subject>Rice</subject><subject>Spectroscopy</subject><subject>Spectrum Analysis</subject><subject>Wettability</subject><issn>0003-2700</issn><issn>1520-6882</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc2O0zAUhS0EYsrAGyBkiQ2blHvT_LIbws9UagWaYdaRY99QD4ldbAfUJ-P1cGgHBAtWlq6_c861D2NPEZYIKb4U0i-FEYPc0bgsO8Asw3tsgXkKSVFV6X22AIBVkpYAZ-yR97cAiIDFQ3aWVkWaQ1Yt2I-t1T5Mjvja9MNERhK_IjVJbT7zLYWdVby3jl-S-HaYB2Lw_A0FkkFbw7XhHwdhAt-KQE7Plzd-lm6EJ5esTXQixV87El-U_W749T4qnfXS7g-v-AVvIsevw6QOv2KanbOjnkbeWBMo-v4VdaXjdpu4CfnH7EEf0-jJ6TxnN-_efmouk82H9-vmYpOIVVmEpOsogzIXRB3VeY8CVQ0FKgmiFrWSSmKdpxIo76lTRVZlZZGVWJRFXqVxsDpnL46-e2e_TuRDO2ovaYiPJjv5FmtErOuqwog-_we9tZOLDc1UBnU0zleRyo6UjL_gHfXt3ulRuEOL0M7FtrHY9q7Y9lRslD07mU_dSOq36K7JCMARmOV_gv_n-ROW4LTl</recordid><startdate>20170718</startdate><enddate>20170718</enddate><creator>Peng, Jiyu</creator><creator>He, Yong</creator><creator>Ye, Lanhan</creator><creator>Shen, Tingting</creator><creator>Liu, Fei</creator><creator>Kong, Wenwen</creator><creator>Liu, Xiaodan</creator><creator>Zhao, Yun</creator><general>American Chemical Society</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>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U7</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0266-6896</orcidid></search><sort><creationdate>20170718</creationdate><title>Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves</title><author>Peng, Jiyu ; He, Yong ; Ye, Lanhan ; Shen, Tingting ; Liu, Fei ; Kong, Wenwen ; Liu, Xiaodan ; Zhao, Yun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a376t-bbe4075aeebe95f1a1d9061dc0a9a9dcdc1952c0e5febd648476471676582bd63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Calibration</topic><topic>Case studies</topic><topic>Chemistry</topic><topic>Chromium</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Drying</topic><topic>Electron density</topic><topic>Flowers & plants</topic><topic>Food plants</topic><topic>Food safety</topic><topic>Heavy metals</topic><topic>Laser induced breakdown spectroscopy</topic><topic>Lasers</topic><topic>Least-Squares Analysis</topic><topic>Leaves</topic><topic>Mathematical models</topic><topic>Metals, Heavy - analysis</topic><topic>Moisture</topic><topic>Moisture content</topic><topic>Oryza - chemistry</topic><topic>Oxidation-Reduction</topic><topic>Plant Leaves - chemistry</topic><topic>Regression analysis</topic><topic>Rice</topic><topic>Spectroscopy</topic><topic>Spectrum Analysis</topic><topic>Wettability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, Jiyu</creatorcontrib><creatorcontrib>He, Yong</creatorcontrib><creatorcontrib>Ye, Lanhan</creatorcontrib><creatorcontrib>Shen, Tingting</creatorcontrib><creatorcontrib>Liu, Fei</creatorcontrib><creatorcontrib>Kong, Wenwen</creatorcontrib><creatorcontrib>Liu, Xiaodan</creatorcontrib><creatorcontrib>Zhao, Yun</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>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>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Analytical chemistry (Washington)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peng, Jiyu</au><au>He, Yong</au><au>Ye, Lanhan</au><au>Shen, Tingting</au><au>Liu, Fei</au><au>Kong, Wenwen</au><au>Liu, Xiaodan</au><au>Zhao, Yun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves</atitle><jtitle>Analytical chemistry (Washington)</jtitle><addtitle>Anal. Chem</addtitle><date>2017-07-18</date><risdate>2017</risdate><volume>89</volume><issue>14</issue><spage>7593</spage><epage>7600</epage><pages>7593-7600</pages><issn>0003-2700</issn><eissn>1520-6882</eissn><abstract>Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>28625048</pmid><doi>10.1021/acs.analchem.7b01441</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-0266-6896</orcidid></addata></record> |
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| subjects | Calibration Case studies Chemistry Chromium Correlation coefficient Correlation coefficients Drying Electron density Flowers & plants Food plants Food safety Heavy metals Laser induced breakdown spectroscopy Lasers Least-Squares Analysis Leaves Mathematical models Metals, Heavy - analysis Moisture Moisture content Oryza - chemistry Oxidation-Reduction Plant Leaves - chemistry Regression analysis Rice Spectroscopy Spectrum Analysis Wettability |
| title | Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves |
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