Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology

In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/...

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Main Authors:	Shao, Yongni, Jiang, Linjun, Zhou, Hong, Pan, Jian, He, Yong
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4829843/

id	pubmed-4829843
recordtype	oai_dc
spelling	pubmed-48298432016-04-19 Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology Shao, Yongni Jiang, Linjun Zhou, Hong Pan, Jian He, Yong Article In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides. Nature Publishing Group 2016-04-13 /pmc/articles/PMC4829843/ /pubmed/27071456 http://dx.doi.org/10.1038/srep24221 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Shao, Yongni Jiang, Linjun Zhou, Hong Pan, Jian He, Yong
spellingShingle	Shao, Yongni Jiang, Linjun Zhou, Hong Pan, Jian He, Yong Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology
author_facet	Shao, Yongni Jiang, Linjun Zhou, Hong Pan, Jian He, Yong
author_sort	Shao, Yongni
title	Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology
title_short	Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology
title_full	Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology
title_fullStr	Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology
title_full_unstemmed	Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology
title_sort	identification of pesticide varieties by testing microalgae using visible/near infrared hyperspectral imaging technology
description	In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4829843/
_version_	1613565542350716928

Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology

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