A THz spectroscopy nondestructive identification method for transgenic cotton seed based on GA-SVM

The purpose of this paper is to construct an identification model that can discriminate different transgenic cotton seeds with similar characteristics based on terahertz (THz) spectroscopy. An improved support vector machine (SVM) which using genetic algorithm (GA) to optimize parameters is proposed in this paper. Principal Component Analysis is applied to extract relevant features from original spectrum information and eliminate the anomalous samples. Instead of original spectral information, the feature spectrum is selected to be fed into the model of GA-SVM, where an improved SVM method to identify those samples. The results demonstrate that the GA-SVM method can effectively identify the distinct transgenic cottons, and THz spectroscopy can provide a nondestructive, rapid and reliable method to distinguish different transgenic cottons.