文献类型： 会议论文

第一作者： Guangjun Qiu

作者： Guangjun Qiu ¹ ; Enli Li ² ; Ning Wang ³ ;

作者机构： 1.College of Engineering, South China Agricultural University, Guangzhou 510640, China

2.Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078,USA

3.Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China

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

会议名称： American Society of Agricultural and Biological Engineers Annual International Meeting

主办单位：

页码： 4013-4025

摘要： Seed purity is a key indicator of crop seed quality. The conventional methods for cultivars 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 wereidentified 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 (S1MCA), 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 to improve the purity of seed cultivars for the seed industry.

分类号： S-532