Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes.

Autor:	Rocha PD; State University of Paraiba, Bairro Universitário, Rua Baraúnas, 351 Campina Grande, Paraiba, 58429-500, Brazil. simonesimoes@servidor.uepb.edu.br., Medeiros EP; Brazilian Agricultural Research Corporation, Embrapa Cotton, Rua Osvaldo Cruz, 1143, Bairro Centenário, Campina Grande, Paraiba, 58428-095, Brazil., Silva CS; Department of Chemistry Engineering, Federal University of Pernambuco, Av. da Arquitetura, Cidade Universitária, Recife, Pernambuco, 50740-540, Brazil. carolina.santossilva@ufpe.br.; Department of Food Sciences and Nutrition, Faculty of Health Sciences, University of Malta, Msida, Malta., da Silva Simões S; State University of Paraiba, Bairro Universitário, Rua Baraúnas, 351 Campina Grande, Paraiba, 58429-500, Brazil. simonesimoes@servidor.uepb.edu.br.
Jazyk:	angličtina
Zdroj:	Analytical methods : advancing methods and applications [Anal Methods] 2021 Nov 04; Vol. 13 (42), pp. 5065-5074. Date of Electronic Publication: 2021 Nov 04.
DOI:	10.1039/d1ay01076j
Abstrakt:	Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based on HSI in the near infrared range (HSI-NIR) to distinguish conventional from transgenic cotton seeds. Three different chemometric approaches, one pixel-based and two object-based, using partial least squares discriminant analysis (PLS-DA) were built and their performances were compared considering the pros and cons of each approach. Specificity and sensitivity values ranged from 0.78-0.92 and 0.62-0.93, respectively, for the different approaches.
Databáze:	MEDLINE
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