Optimized model for rapid identification of transgenic soybeans based on morphological analysis
A technology for genetically modified soybeans and morphological analysis, applied in the field of near-infrared spectroscopy detection, can solve the problems of inapplicability to actual conditions and time-consuming
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[0036] Summary of the Study The following example investigates a rapid, non-destructive detection method for transgenic soybeans using near-infrared spectroscopy (NIR) and partial least squares discriminant analysis (PLS-DA). Principal component analysis (PCA) is used to extract relevant features from spectral data and remove outlier samples. In the experiment, 94 samples are used to build the model, and 41 samples are used for validation to evaluate the effect of the model. The effects of sample form (whole grain and powder), wavelength range and spectral preprocessing method on the accuracy of the model were discussed respectively. Overall, powdered soybean samples modeled better than whole soybean samples. Among them, the model with the best judgment effect is the whole soybean at 9403-5438cm -1 , using the second derivative (2nd) to process the spectrum, the correctness of the calibration set and verification set of the model is 100%; -1 , using vector normalization + f...
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