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Issue 48, 2016
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Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

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Abstract

This paper proposes the use of Near Infrared Hyperspectral Imaging (NIR-HSI) as a new strategy for fast and non-destructive classification of cotton seeds with respect to variety. A total of 807 seeds of four different cotton varieties are employed in this study. For classification purposes, each seed is represented by an average spectrum obtained by coaveraging the pixel spectra of the NIR-HSI image. Conventional NIR and VIS-NIR spectra are also employed for comparison. By using Partial-Least-Squares Discriminant Analysis (PLS-DA), correct classification rates of 98.0%, 89.7% and 91.7% were achieved in the NIR-HSI, conventional NIR and conventional VIS-NIR datasets. The superiority of the NIR-HSI system can be ascribed to a more comprehensive scan of the seed area, as compared to the conventional VIS-NIR spectrometer.

Graphical abstract: Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

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Publication details

The article was received on 21 Oct 2016, accepted on 07 Nov 2016 and first published on 08 Nov 2016


Article type: Paper
DOI: 10.1039/C6AY02896A
Citation: Anal. Methods, 2016,8, 8498-8505
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    Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

    S. F. Carreiro Soares, E. P. Medeiros, C. Pasquini, C. de Lelis Morello, R. K. Harrop Galvão and M. C. Ugulino Araújo, Anal. Methods, 2016, 8, 8498
    DOI: 10.1039/C6AY02896A

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