A Novel Nanoscaled Chemo Dye–Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat
This work presents a novel colorimetric sensor based on nanoscaled chemo dyes which can detect inert volatile organic compounds (VOCs) during the mildewing process of stored wheat. 1-Octen-3-ol and 3-octanone were selected as the marked compounds by gas chromatography mass spectrometry (GC-MS) analy...
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Veröffentlicht in: | Food analytical methods 2019-12, Vol.12 (12), p.2895-2907 |
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creator | Lin, Hao Kang, Wencui Kutsanedzie, Felix Y. H. Chen, Quansheng |
description | This work presents a novel colorimetric sensor based on nanoscaled chemo dyes which can detect inert volatile organic compounds (VOCs) during the mildewing process of stored wheat. 1-Octen-3-ol and 3-octanone were selected as the marked compounds by gas chromatography mass spectrometry (GC-MS) analysis. In this work, poly(styrene-
co
-acrylic acid) microbeads were prepared by soap-free emulsion copolymerisation. Boron-dipyrromethene dyes with PSA were fabricated as a novel sensor to obtain digital data before and after exposure to VOCs, and the correlation coefficients (
R
2
) between the digital data and the concentration of VOCs were 0.8078 and 0.8324, respectively. And root mean square errors (RMSEs) were 3.05 g L
−1
and 1.65 g L
−1
, respectively. The data based on the identification of mouldy wheat samples were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained for the LDA model was 83.33% in the prediction set and 90% in the calibration set. |
doi_str_mv | 10.1007/s12161-019-01617-1 |
format | Article |
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co
-acrylic acid) microbeads were prepared by soap-free emulsion copolymerisation. Boron-dipyrromethene dyes with PSA were fabricated as a novel sensor to obtain digital data before and after exposure to VOCs, and the correlation coefficients (
R
2
) between the digital data and the concentration of VOCs were 0.8078 and 0.8324, respectively. And root mean square errors (RMSEs) were 3.05 g L
−1
and 1.65 g L
−1
, respectively. The data based on the identification of mouldy wheat samples were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained for the LDA model was 83.33% in the prediction set and 90% in the calibration set.</description><identifier>ISSN: 1936-9751</identifier><identifier>EISSN: 1936-976X</identifier><identifier>DOI: 10.1007/s12161-019-01617-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Acrylic acid ; Analytical Chemistry ; Boron ; Calibration ; Chemistry ; Chemistry and Materials Science ; Chemistry/Food Science ; Colorimetry ; Copolymerization ; Correlation coefficients ; Digital data ; Discriminant analysis ; Dyes ; Food Science ; Gas chromatography ; Mass spectrometry ; Mass spectroscopy ; Microbiology ; Microspheres ; Nanoparticles ; Organic compounds ; Pollutant identification ; Polystyrene resins ; Principal components analysis ; Sensors ; Styrene ; VOCs ; Volatile organic compounds ; Wheat</subject><ispartof>Food analytical methods, 2019-12, Vol.12 (12), p.2895-2907</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Copyright Springer Nature B.V. 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-63bba939f19ea8ab16de6fb66aaac33d3f90e7b7208f70c138ffa43964a58c0b3</citedby><cites>FETCH-LOGICAL-c319t-63bba939f19ea8ab16de6fb66aaac33d3f90e7b7208f70c138ffa43964a58c0b3</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/s12161-019-01617-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12161-019-01617-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Lin, Hao</creatorcontrib><creatorcontrib>Kang, Wencui</creatorcontrib><creatorcontrib>Kutsanedzie, Felix Y. H.</creatorcontrib><creatorcontrib>Chen, Quansheng</creatorcontrib><title>A Novel Nanoscaled Chemo Dye–Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat</title><title>Food analytical methods</title><addtitle>Food Anal. Methods</addtitle><description>This work presents a novel colorimetric sensor based on nanoscaled chemo dyes which can detect inert volatile organic compounds (VOCs) during the mildewing process of stored wheat. 1-Octen-3-ol and 3-octanone were selected as the marked compounds by gas chromatography mass spectrometry (GC-MS) analysis. In this work, poly(styrene-
co
-acrylic acid) microbeads were prepared by soap-free emulsion copolymerisation. Boron-dipyrromethene dyes with PSA were fabricated as a novel sensor to obtain digital data before and after exposure to VOCs, and the correlation coefficients (
R
2
) between the digital data and the concentration of VOCs were 0.8078 and 0.8324, respectively. And root mean square errors (RMSEs) were 3.05 g L
−1
and 1.65 g L
−1
, respectively. The data based on the identification of mouldy wheat samples were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained for the LDA model was 83.33% in the prediction set and 90% in the calibration set.</description><subject>Acrylic acid</subject><subject>Analytical Chemistry</subject><subject>Boron</subject><subject>Calibration</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Colorimetry</subject><subject>Copolymerization</subject><subject>Correlation coefficients</subject><subject>Digital data</subject><subject>Discriminant analysis</subject><subject>Dyes</subject><subject>Food Science</subject><subject>Gas chromatography</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Microbiology</subject><subject>Microspheres</subject><subject>Nanoparticles</subject><subject>Organic compounds</subject><subject>Pollutant identification</subject><subject>Polystyrene resins</subject><subject>Principal components analysis</subject><subject>Sensors</subject><subject>Styrene</subject><subject>VOCs</subject><subject>Volatile organic compounds</subject><subject>Wheat</subject><issn>1936-9751</issn><issn>1936-976X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OAjEQxzdGExF9AU9NPK92ttClRwQ_SBBM_Lw13e4UlixbbBcNN1_Ak2_ok1jA6M3DZD4yv_9k_lF0DPQUKE3PPCTAIaYgQnBIY9iJGiAYj0XKn3d_6zbsRwfezyjltAVJI_rokpF9xZKMVGW9ViXmpDfFuSX9FX69f54rHyZ3WHnriAlRT5EMcqzqwhRa1YWtiDXk0ZahLpGM3URVhSY9O1_YZZV70l-6oppsuJuizPFt3d06q9H7NXpXWxdOPE1R1YfRnlGlx6Of3IweLi_ue9fxcHw16HWHsWYg6pizLFOCCQMCVUdlwHPkJuNcKaUZy5kRFNMsTWjHpFQD6xijWkzwlmp3NM1YMzrZ6i6cfVmir-XMLl0VTsqEQSsVvC1o2Eq2W9pZ7x0auXDFXLmVBCrXtsut7TLYLje2SwgQ20J-sf4b3Z_0P9Q3d_GIMA</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Lin, Hao</creator><creator>Kang, Wencui</creator><creator>Kutsanedzie, Felix Y. H.</creator><creator>Chen, Quansheng</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20191201</creationdate><title>A Novel Nanoscaled Chemo Dye–Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat</title><author>Lin, Hao ; Kang, Wencui ; Kutsanedzie, Felix Y. H. ; Chen, Quansheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-63bba939f19ea8ab16de6fb66aaac33d3f90e7b7208f70c138ffa43964a58c0b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acrylic acid</topic><topic>Analytical Chemistry</topic><topic>Boron</topic><topic>Calibration</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Colorimetry</topic><topic>Copolymerization</topic><topic>Correlation coefficients</topic><topic>Digital data</topic><topic>Discriminant analysis</topic><topic>Dyes</topic><topic>Food Science</topic><topic>Gas chromatography</topic><topic>Mass spectrometry</topic><topic>Mass spectroscopy</topic><topic>Microbiology</topic><topic>Microspheres</topic><topic>Nanoparticles</topic><topic>Organic compounds</topic><topic>Pollutant identification</topic><topic>Polystyrene resins</topic><topic>Principal components analysis</topic><topic>Sensors</topic><topic>Styrene</topic><topic>VOCs</topic><topic>Volatile organic compounds</topic><topic>Wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Hao</creatorcontrib><creatorcontrib>Kang, Wencui</creatorcontrib><creatorcontrib>Kutsanedzie, Felix Y. H.</creatorcontrib><creatorcontrib>Chen, Quansheng</creatorcontrib><collection>CrossRef</collection><jtitle>Food analytical methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Hao</au><au>Kang, Wencui</au><au>Kutsanedzie, Felix Y. H.</au><au>Chen, Quansheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Nanoscaled Chemo Dye–Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat</atitle><jtitle>Food analytical methods</jtitle><stitle>Food Anal. Methods</stitle><date>2019-12-01</date><risdate>2019</risdate><volume>12</volume><issue>12</issue><spage>2895</spage><epage>2907</epage><pages>2895-2907</pages><issn>1936-9751</issn><eissn>1936-976X</eissn><abstract>This work presents a novel colorimetric sensor based on nanoscaled chemo dyes which can detect inert volatile organic compounds (VOCs) during the mildewing process of stored wheat. 1-Octen-3-ol and 3-octanone were selected as the marked compounds by gas chromatography mass spectrometry (GC-MS) analysis. In this work, poly(styrene-
co
-acrylic acid) microbeads were prepared by soap-free emulsion copolymerisation. Boron-dipyrromethene dyes with PSA were fabricated as a novel sensor to obtain digital data before and after exposure to VOCs, and the correlation coefficients (
R
2
) between the digital data and the concentration of VOCs were 0.8078 and 0.8324, respectively. And root mean square errors (RMSEs) were 3.05 g L
−1
and 1.65 g L
−1
, respectively. The data based on the identification of mouldy wheat samples were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained for the LDA model was 83.33% in the prediction set and 90% in the calibration set.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s12161-019-01617-1</doi><tpages>13</tpages></addata></record> |
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subjects | Acrylic acid Analytical Chemistry Boron Calibration Chemistry Chemistry and Materials Science Chemistry/Food Science Colorimetry Copolymerization Correlation coefficients Digital data Discriminant analysis Dyes Food Science Gas chromatography Mass spectrometry Mass spectroscopy Microbiology Microspheres Nanoparticles Organic compounds Pollutant identification Polystyrene resins Principal components analysis Sensors Styrene VOCs Volatile organic compounds Wheat |
title | A Novel Nanoscaled Chemo Dye–Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat |
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