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.
| Original language | English |
|---|---|
| Pages (from-to) | 5065-5074 |
| Number of pages | 10 |
| Journal | Analytical Methods |
| Volume | 13 |
| Issue number | 42 |
| DOIs | |
| Publication status | Published - 14 Nov 2021 |
| MoE publication type | A1 Journal article-refereed |
Funding
The authors would like to thank the Brazilian Embrapa (SEG 20.20.00.120.00.00 and 30.19.00.135.00.00), Brazilian agencies CNPq, CAPES and FACEPE (BFP-0800-1.06/17) for scholarships support for this work. PROPESQ/UEPB (1.06.04.00-6-398/2017-1) and NUQAAPE–FACEPE (APQ-0346-1.06/14) for the funds granted for the research.