期刊
APPLIED SCIENCES-BASEL
卷 9, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/app9081530
关键词
FT-NIR; discriminant analysis; KNN; SIMCA; PLS-DA; SVM-DA; cultivars; sweet corn seed
类别
资金
- Sub-task of National Key Research and Development Plan of China [2018YFD0701002]
- (Basic Research and Application Research) Major Projects of Guangdong Province [2016KZDXM028]
- Sub-task of National Science and Technology Support Plan of China [2015BAD18B0301]
- Science and Technology Program of Guangdong Province [2017B020206005]
- Science and Technology Program of Guangzhou [201704020067]
- South China Agricultural University Doctoral Students Overseas Joint Education Programs [2018LHPY023]
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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