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

文献类型： 外文期刊

作者： Qiu, Guangjun 1 ; Lu, Enli 1 ; Wang, Ning 2 ; Lu, Huazhong 3 ; Wang, Feiren 1 ; Zeng, Fanguo 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510640, Guangdong, Peoples R China

2.Oklahoma State Univ, Dept Biosyst & Agr Engn, Stillwater, OK 75078 USA

3.Guangdong Acad Agr Sci, Guangzhou 510640, Guangdong, Peoples R China

关键词： FT-NIR; discriminant analysis; KNN; SIMCA; PLS-DA; SVM-DA; cultivars; sweet corn seed

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

ISSN：

年卷期： 2019 年 9 卷 8 期

页码：

收录情况： SCI

摘要： 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.

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