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

Anal Methods. 2021 Nov 4;13(42):5065-5074. doi: 10.1039/d1ay01076j.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chemometrics
  • Cottonseed Oil*
  • Genotype
  • Hyperspectral Imaging
  • Seeds / genetics
  • Spectroscopy, Near-Infrared*

Substances

  • Cottonseed Oil