Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes
2021
Rocha, Priscilla Dantas | Medeiros, Everaldo Paulo | Silva, Carolina Santos | da Silva Simões, Simone
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.
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