Identification of Transgenic Soybean Varieties Using Mid-Infrared Spectroscopy

Transgenic technology has enormous significance in increasing food production, protecting biodiversity and reducing the use of chemical pesticides and so on. However, there may be some security risks; therefore, research on genetically modified crop identification technology is attracting more and more attention. Mid-infrared spectroscopy combined with feature extraction methods were used to investigate the feasibility of identifying different kinds of transgenic soybeans in the wavelength range of 3 818～734 cm-1. For this purpose, partial least squares-discriminant analysis (PLS-DA) was employed as pattern recognition methods to classify three non-GMO parent soybeans(HC6, JACK and W82)and their transgenic soybeans. The results of the calibration set were 96.67%, 96.67% and 83.33% for three non-GMO parent soybeans and their transgenic soybeans, and the results of the prediction set were 83.33%, 85% and 85%. X-loading weights, variable importance in the projection (VIP) algorithm and second derivative (2-Der) algorithm were applied to select sensitive wavenumbers. The sensitive wavelengths selected with x-loading weights were used to build PLS-DA model, the classification accuracy of the calibration set were 91.11%, 91.67% and 81.67%, and the results of the prediction set were 80%, 80% and 75%. By using the VIP algorithm, the classification accuracy of the calibration set were 94.44%, 95% and 76.67%, and the results of the prediction set were 80%, 85% and 75%. By using the 2-Der algorithm, the classification accuracy of the calibration set were 88.89%, 81.67% and 80%, and the results of the prediction set were 76.67%, 75% and 75%. Principal components analysis (PCA) and independent component analysis (ICA) were applied to extract feature information. The principal components were combined with PLS-DA model. The classification accuracy of the calibration set were 96.67%, 90% and 80%, and the results of the prediction set were 80%, 90% and 80%. The independent components were combined with PLS-DA model. The classification accuracy of the calibration set were 93.33%, 83.33% and 83.33% while the results of the prediction set were 83.33%, 75% and 75%. The overall results indicated that mid-infrared spectroscopy could accurately identify the varieties of the non-GMO parent soybeans, which provided a new idea for nondestructive testing of transgenic soybeans. Feature extraction methods could be used to build more concise models and reduce the amount of program operations combined with sensitive wavenumbers selection methods.