An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea

The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, an...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiaoyan Tang [verfasserIn] Wenmin Xiao [verfasserIn] Tao Shang [verfasserIn] Shanyan Zhang [verfasserIn] Xiaoyang Han [verfasserIn] Yuliang Wang [verfasserIn] Haiwei Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	electronic nose tea pyrethroid pesticide BP neural network technique

Übergeordnetes Werk:	In: Chemosensors - MDPI AG, 2013, 8(2020), 2, p 30
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:2, p 30

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/chemosensors8020030

Katalog-ID:	DOAJ047444436

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520			\|a The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry.
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allfields	10.3390/chemosensors8020030 doi (DE-627)DOAJ047444436 (DE-599)DOAJ18462877b5994285b32e07fff02faa12 DE-627 ger DE-627 rakwb eng QD415-436 Xiaoyan Tang verfasserin aut An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry. electronic nose tea pyrethroid pesticide BP neural network technique Biochemistry Wenmin Xiao verfasserin aut Tao Shang verfasserin aut Shanyan Zhang verfasserin aut Xiaoyang Han verfasserin aut Yuliang Wang verfasserin aut Haiwei Sun verfasserin aut In Chemosensors MDPI AG, 2013 8(2020), 2, p 30 (DE-627)737287594 (DE-600)2704218-2 22279040 nnns volume:8 year:2020 number:2, p 30 https://doi.org/10.3390/chemosensors8020030 kostenfrei https://doaj.org/article/18462877b5994285b32e07fff02faa12 kostenfrei https://www.mdpi.com/2227-9040/8/2/30 kostenfrei https://doaj.org/toc/2227-9040 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2, p 30
spelling	10.3390/chemosensors8020030 doi (DE-627)DOAJ047444436 (DE-599)DOAJ18462877b5994285b32e07fff02faa12 DE-627 ger DE-627 rakwb eng QD415-436 Xiaoyan Tang verfasserin aut An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry. electronic nose tea pyrethroid pesticide BP neural network technique Biochemistry Wenmin Xiao verfasserin aut Tao Shang verfasserin aut Shanyan Zhang verfasserin aut Xiaoyang Han verfasserin aut Yuliang Wang verfasserin aut Haiwei Sun verfasserin aut In Chemosensors MDPI AG, 2013 8(2020), 2, p 30 (DE-627)737287594 (DE-600)2704218-2 22279040 nnns volume:8 year:2020 number:2, p 30 https://doi.org/10.3390/chemosensors8020030 kostenfrei https://doaj.org/article/18462877b5994285b32e07fff02faa12 kostenfrei https://www.mdpi.com/2227-9040/8/2/30 kostenfrei https://doaj.org/toc/2227-9040 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2, p 30
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allfieldsSound	10.3390/chemosensors8020030 doi (DE-627)DOAJ047444436 (DE-599)DOAJ18462877b5994285b32e07fff02faa12 DE-627 ger DE-627 rakwb eng QD415-436 Xiaoyan Tang verfasserin aut An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry. electronic nose tea pyrethroid pesticide BP neural network technique Biochemistry Wenmin Xiao verfasserin aut Tao Shang verfasserin aut Shanyan Zhang verfasserin aut Xiaoyang Han verfasserin aut Yuliang Wang verfasserin aut Haiwei Sun verfasserin aut In Chemosensors MDPI AG, 2013 8(2020), 2, p 30 (DE-627)737287594 (DE-600)2704218-2 22279040 nnns volume:8 year:2020 number:2, p 30 https://doi.org/10.3390/chemosensors8020030 kostenfrei https://doaj.org/article/18462877b5994285b32e07fff02faa12 kostenfrei https://www.mdpi.com/2227-9040/8/2/30 kostenfrei https://doaj.org/toc/2227-9040 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2, p 30
language	English
source	In Chemosensors 8(2020), 2, p 30 volume:8 year:2020 number:2, p 30
sourceStr	In Chemosensors 8(2020), 2, p 30 volume:8 year:2020 number:2, p 30
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	electronic nose tea pyrethroid pesticide BP neural network technique Biochemistry
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container_title	Chemosensors
authorswithroles_txt_mv	Xiaoyan Tang @@aut@@ Wenmin Xiao @@aut@@ Tao Shang @@aut@@ Shanyan Zhang @@aut@@ Xiaoyang Han @@aut@@ Yuliang Wang @@aut@@ Haiwei Sun @@aut@@
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callnumber-first	Q - Science
author	Xiaoyan Tang
spellingShingle	Xiaoyan Tang misc QD415-436 misc electronic nose misc tea misc pyrethroid pesticide misc BP neural network technique misc Biochemistry An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea
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topic_title	QD415-436 An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea electronic nose tea pyrethroid pesticide BP neural network technique
topic	misc QD415-436 misc electronic nose misc tea misc pyrethroid pesticide misc BP neural network technique misc Biochemistry
topic_unstemmed	misc QD415-436 misc electronic nose misc tea misc pyrethroid pesticide misc BP neural network technique misc Biochemistry
topic_browse	misc QD415-436 misc electronic nose misc tea misc pyrethroid pesticide misc BP neural network technique misc Biochemistry
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title	An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea
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title_full	An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea
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author_browse	Xiaoyan Tang Wenmin Xiao Tao Shang Shanyan Zhang Xiaoyang Han Yuliang Wang Haiwei Sun
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title_sort	electronic nose technology to quantify pyrethroid pesticide contamination in tea
callnumber	QD415-436
title_auth	An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea
abstract	The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry.
abstractGer	The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry.
abstract_unstemmed	The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry.
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title_short	An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea
url	https://doi.org/10.3390/chemosensors8020030 https://doaj.org/article/18462877b5994285b32e07fff02faa12 https://www.mdpi.com/2227-9040/8/2/30 https://doaj.org/toc/2227-9040
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author2	Wenmin Xiao Tao Shang Shanyan Zhang Xiaoyang Han Yuliang Wang Haiwei Sun
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