| 潘学文,尹向东,赵永红.基于太赫兹光谱和APSO-SVM的转基因棉籽识别方法[J].电子测量与仪器学报,2017,31(3):377-382 |
| 基于太赫兹光谱和APSO-SVM的转基因棉籽识别方法 |
| Distinguish of genetically modified cotton seed by using terahertz spectroscopy and APSO SVM |
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| DOI：10.13382/j.jemi.2017.03.007 |
| 中文关键词: 太赫兹 支持向量机 转基因棉种子 分类辨别 自适应粒子群 |
| 英文关键词:terahertz SVM genetically modified cotton seed classification to distinguish APSO |
| 基金项目:湖南省教育厅科学研究优秀青年项目（14B070）、广西自动检测技术与仪器重点实验室开放基金(YQ16204)、湖南科技学院科研项目（2015XKY004）、湖南科技学院重点学科建设项目（电路与系统）资助 |
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| Author | Institution |
| Pan Xuewen | School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China |
| Yin Xiangdong | School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China |
| Zhao Yonghong | 2. School of MechanoElectronic Engineering, Xidian University, Xi’an 710071, China;
3. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China |
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| 中文摘要: |
| 针对目前转基因产品检测是基于可见光/近红外光谱，在支持向量机建模中参数难以确定及光谱数据计算量过大的问题，提出了一种基于太赫兹光谱和自适应动态粒子群优化的支持向量机算法，以实现对转基因棉种子的分类判别。为实现转基因棉种子的分类识别，在波长150 μm~3 mm采集3种最新转基因棉种子165个样本的太赫兹光谱，并用基于自适应动态粒子群优化的支持向量机对165个转基因棉种子进行识别。实验结果表明，综合识别率达到97.3%，太赫兹光谱结合自适应动态粒子群支持向量机可为转基因棉种子类型辨别提供一种快速、精确、简便、无损的检测方法。 |
| 英文摘要: |
| Aiming at the inspection of genetically modified product is mainly based on visible or near infrared spectroscopy at present, the support vector machine (SVM) modeling parameter is difficult to determine and the problem of the large amount of spectrum data calculation, a support vector machine (SVM) algorithm based on terahertz spectroscopy and adaptive particle swarm optimization (APSO) is proposed to distinguish genetically modified cotton seed. To achieve distinguish genetically modified cotton seed, the present invention is train of thought collect 165 samples of three kinds of latest genetically modified cotton seed of terahertz spectroscopy in range of 150 μm~3 mm wavelength and identification of 165 genetically modified cotton seeds based on APSO SVM. The experiment results show that the comprehensive recognition rate reached 97.3%. It can provide a precise, fast, convenient, and nondestructive detection method to distinguish genetically modified cotton seed by using terahertz spectroscopy couple to APSO SVM. |
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