This paper presents a novel approach for identifi cation of terahertz (THz) spectra of genetically modifi ed organisms (GMOs) based on hyper sausage neuron (HSN), and THz transmittance spectra of some typical transgenic sugarbeet samples are investigated to demonstrate its feasibility. Principal component analysis (PCA) is applied to extract features of the spectrum data, and instead of the original spectrum data, the feature signals are fed into the HSN pattern recognition, a new multiple weights neural network (MWNN). The experimental result shows that the HSN model not only can correctly classify different types of transgenic sugar-beets, but also can reject nonsimilar samples of the same type. The proposed approach provides a new effective method for detection and identification of genetically modified organisms by using THz spectroscopy.
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Published in Zhurnal Prikladnoi Spektroskopii, Vol. 82, No. 1, pp. 109–114, January–February, 2015.
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Liu, J., Li, Z., Hu, F. et al. Identification of Transgenic Organisms Based on Terahertz Spectroscopy and Hyper Sausage Neuron. J Appl Spectrosc 82, 104–110 (2015). https://doi.org/10.1007/s10812-015-0071-6
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DOI: https://doi.org/10.1007/s10812-015-0071-6