Identification of GMOs by terahertz spectroscopy and ALAP-SVM

An approach for identification of terahertz (THz) spectral of genetically modified organisms (GMOs) based on active learning affinity propagation clustering algorithm (ALAP) combined with support vector machine (SVM) in this paper, and THz transmittance spectra of some typical genetically modified (GM) cotton samples are investigated to prove its feasibility. Firstly, principal component analysis is applied to extract features of the spectrum data. Secondly, instead of the original spectrum data, the feature signals are fed into the ALAP-SVM pattern recognition, where an improved active learning ALAP is applied to SVM. The experimental results show that THz spectroscopy combined with ALAP-SVM can be effectively utilized for identification of different GM cottons. The proposed approach provides a new effective method for detection and identification of different GMOs by using THz spectroscopy.