基于太赫兹光谱技术与CS-SVM的转基因产品鉴别

陈涛; 李智; 胡放荣; 殷贤华; 许川佩

doi:doi:10.3964/j.issn.1000-0593(2017)02-0618-06

光谱学与光谱分析, 2017, 37 (2): 618, 网络出版: 2017-06-20

基于太赫兹光谱技术与CS-SVM的转基因产品鉴别

Discrimination of GMOs Using Terahertz Spectroscopy and CS-SVM

论文大纲

陈涛李智胡放荣殷贤华许川佩

作者单位

广西自动检测技术与仪器重点实验室, 桂林电子科技大学电子工程与自动化学院, 广西桂林 541004

太赫兹光谱转基因生物支持向量机布谷鸟搜索算法 Terahertz spectroscopy Genetically modified organisms Support vector machines Cuckoo Search algorithm

摘要

提出了一种基于太赫兹（THz)光谱技术以及布谷鸟搜索（CS)算法优化支持向量机（SVM)的有效的转基因产品鉴别方法（CS-SVM)。实验采用太赫兹时域光谱（THz-TDS)系统测量了三种转基因大豆种子及其亲本样品在02~12 THz波段的THz光谱, 并采用SVM方法对转基因和非转基因大豆种子进行了分类鉴别研究, 其中SVM的两个重要参数（惩罚因子和核参数)采用CS算法进行优化。实验结果表明, 应用THz光谱技术结合CS-SVM方法为转基因和非转基因生物的检测和识别提供了一种快速、无损和可靠的分析方法。

Abstract

This paper develops an effective identification method to discriminate genetically modified (GM) and non-GM organisms. The method is proposed based on terahertz (THz) spectroscopy and support vector machines optimized by Cuckoo Search algorithm (CS-SVM). In this study, the THz spectra of three GM and non-GM soya seed samples were obtained by using terahertz time-domain spectroscopy (THz-TDS) system between 02 and 12 THz. Then, the SVM model is employed to distinguish GM and non-GM soya seeds, in which the two crucial parameters, including the penalty factor and kernel parameter, are optimized by CS algorithm. The experimental results show that THz spectroscopy combined with CS-SVM can provide a rapid, reliable and non-invasive method for GMOs and non-GMOs discrimination.

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陈涛, 李智, 胡放荣, 殷贤华, 许川佩. 基于太赫兹光谱技术与CS-SVM的转基因产品鉴别[J]. 光谱学与光谱分析, 2017, 37(2): 618. CHEN Tao, LI Zhi, HU Fang-rong, YIN Xian-hua, XU Chuan-pei. Discrimination of GMOs Using Terahertz Spectroscopy and CS-SVM[J]. Spectroscopy and Spectral Analysis, 2017, 37(2): 618.

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