Detection of genetically modified substances based on terahertz and multi-weight vector neural network

Ding, Fan, Fan Fan, Lanlan, Li, Tiejun, Liu, Jianjun, Pan, X., Yang, Senquan

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For Russian citation (Opticheskii Zhurnal):

J. Liu, T. Li, S. Yang, L. Fan Fan, F. Ding, X. Pan Detection of genetically modified substances based on terahertz and multi-weight vector neural network (Определение генетически модифицированных продуктов на основе анализа терагерцовых спектров многовесовой нейронной сетью) [на англ. яз.] // Оптический журнал. 2021. Т. 88. № 7. С. 3–11. http://doi.org/10.17586/1023-5086-2021-88-07-03-11

J. Liu, T. Li, S. Yang, L. Fan Fan, F. Ding, X. Pan Detection of genetically modified substances based on terahertz and multi-weight vector neural network (Определение генетически модифицированных продуктов на основе анализа терагерцовых спектров многовесовой нейронной сетью) [in English] // Opticheskii Zhurnal. 2021. V. 88. № 7. P. 3–11. http://doi.org/10.17586/1023-5086-2021-88-07-03-11

For citation (Journal of Optical Technology):

J. Liu, T. Li, S. Yang, L. Fan Fan, F. Ding, and X. Pan, "Detection of genetically modified substances based on the terahertz spectrum and a multi-weight vector neural network," Journal of Optical Technology. 88(7), 354-359 (2021). https://doi.org/10.1364/JOT.88.000354

Abstract:

Genetically modified food has always been a hot issue in the field of food safety. Genetically modified food has always been a hot issue in the field of food safety. In order to realize the detection of genetically modified materials, a bionic recognition model of multi-weight vector neural network is proposed by combining multi-weight vector neural network with terahertz time domain spectroscopy. In this paper, for each class of samples, 50 samples are randomly selected as the training set, a multiweight vector neural network bionic recognition model is established, and 50 samples are selected as the first test set to verify the recognition rate. Other dissimilar samples are used as the second testset to verify their misjudgment rate. The experimental results show that the model can effectively identify transgenic materials with similar spectral characteristics. The model proposed in this paper provides a new method for the detection and identification of genetically modified organisms.

Keywords:

terahertz spectroscopy, principal component, multi weight vector neuron

Acknowledgements:

This work is supported by Support for scientific research projects (scientific research projects in colleges and universities) (No. 2019KTSCX165); supported by Foundation Funded Project of doctoral (No. 99000617); supported in part by research grants from the Science and Technology Program of Shaoguan (No. 2019sn056; 2019sn066), supported in part by the Key platforms and major scientific research projects of Universities in Guangdong (No. 2017KQNCX183), supported in part by the Key Project of Shaoguan University (No. SZ2017KJ08).

OCIS codes: 300.0300, 040.2235, 010.1030

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