International Journal of Bioinformatics and Biomedical Engineering
Articles Information
International Journal of Bioinformatics and Biomedical Engineering, Vol.2, No.2, Mar. 2016, Pub. Date: Mar. 9, 2016
MW-PCA-LDA Applied to Vis-NIR Discriminate Analysis of Transgenic Sugarcane
Pages: 40-45 Views: 1549 Downloads: 930
[01] Kaisheng Ma, Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
[02] Lijun Yao, Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
[03] Jiemei Chen, Department of Biological Engineering, Jinan University, Guangzhou, China.
[04] Tao Pan, Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
The moving-window waveband screening is applied to principal component and linear discriminate analyses (PCA-LDA). An integrated method (MW-PCA-LDA) for spectral pattern recognition is proposed, which is successfully employed for the non-destructive recognition of transgenic sugarcane leaves using visible (Vis) and near-infrared (NIR) diffuse reflectance spectroscopy. A Kennard–Stone-algorithm-based process of calibration, prediction and validation in consideration of uniformity and representative was performed to produce objective models. A total of 487 samples of sugarcane leaves in the elongation stage were collected from a planted field. These samples were composed of 306 transgenic samples containing both Bacillus thuringiensis and Bialaphos resistance genes, and 181 non-transgenic samples. Based on the MW-PCA-LDA method, the optimal waveband was 768 nm to 788 nm, and the corresponding validation recognition rates of transgenic and non-transgenic samples achieved 96.2% and 96.3%, respectively. The results show that Vis-NIR spectroscopy combined with the MW-PCA-LDA method can be used for accurate recognition of transgenic sugarcane leaves and provides a quick and convenient means of screening transgenic sugarcane breeding for large-scale agricultural production.
Visible-Near Infrared Spectroscopy, Transgenic Sugarcane Leaves, Spectral Discriminate Analysis, Moving-Window Waveband Screening, Principal Component and Linear Discriminate Analyses
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