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

Abstract

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes.

Abstract

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.