科研之友
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摘要
The Savitzky-Golay (SG) method and moving-window waveband screening are applied to a coupling model of principal component (PCA) and linear discriminant analyses (LDA). An SG-pretreatment-based 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 456 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 (Bt) and bialaphos resistance (Bar) genes, and 150 non-transgenic samples. According to the spectral recognition effects, two parallel optimal SG modes were selected. The one of the 1st order derivative, 3rd degree polynomial and 25 smoothing points was taken as an example to pretreat the diffuse reflectance spectra. Based on the MW-PCA-LDA method, the optimal waveband was 768 nm to 822 nm, the optimal PC combination was PC1-PC3 and the corresponding validation recognition rates of transgenic and non-transgenic samples achieved 99.1% and 98.0%, respectively. The results show that Vis-NIR spectroscopy combined with SG pretreatment and 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.
