中文摘要：目前，转基因产品的检测方法大多是基于可见/近红外光谱，在支持向量机（SVM）模型中，这种方法很难建立参数，且有大量的频谱数据计算。为解决这些问题，本文构建了一种基于太赫兹谱（THz）和自适应粒子群优化的SVM模型的新算法，（APSO-SVM），该算法可以构建转基因棉花种子的分类机制。研究结果发现，该方法的整体识别率高达97.3%，这表明基于APSO-SVM的THz光谱可以对转基因棉花种子进行可靠、快速、简便、无损的检测。
外文摘要：Currently, the transgenic products detection methods are mostly based on visible/near-infrared light spectrum. In addition, it is hard to set up the parameters in the support vector machine (SVM) model and there is a large amount of calculation on spectrum data. To solve these problems, this paper proposed an algorithm based on terahertz (THz) spectrum and SVM using adaptive particle swarm optimize (APSO-SVM) for building up the classifications of transgenic cotton seed. To conduct the transgenic cotton seed classification, within the wavelength region 150 mu m-3 mm, the THz spectrums are first sampled from 165 samples of three newest transgenic cotton seeds. Then, the 165 transgenic cotton seeds are recognized based on the APSO-SVM. Experiment results indicate that the total recognition rate is up to 97.3%, which prove that the THz spectrum combined with APSO-SVM can provide a reliable, rapid, simple and nondestructive detection method for transgenic cotton seed.
作者：Li, T. J.；Liu, J. J.；Shao, G. F.；等
作者单位：Jiujiang University
期刊名称：OPTICS AND SPECTROSCOPY
期刊影响因子：0.644
出版年份：2016
出版刊次：4
点击下载：一种基于APSO和SVM的、可用于转基因生物识别的新型THz光谱识别方法