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Appl. Sci. 2019, 9(8), 1530; https://doi.org/10.3390/app9081530

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

1
College of Engineering, South China Agricultural University, Guangzhou 510640, China
2
Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 75078, USA
3
Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
*
Author to whom correspondence should be addressed.
Received: 25 February 2019 / Revised: 11 April 2019 / Accepted: 11 April 2019 / Published: 12 April 2019
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Abstract

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds. View Full-Text
Keywords: FT-NIR; discriminant analysis; KNN; SIMCA; PLS-DA; SVM-DA; cultivars; sweet corn seed FT-NIR; discriminant analysis; KNN; SIMCA; PLS-DA; SVM-DA; cultivars; sweet corn seed
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Qiu, G.; Lü, E.; Wang, N.; Lu, H.; Wang, F.; Zeng, F. Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. Appl. Sci. 2019, 9, 1530.

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