中文摘要：基因工程生物的基因流动可能会带来环境风险。因而，找到能在作物及作物产品中检测和监测转基因生物的精确、快速、价格公道的方法至关重要。本文基于近红外光谱成像（检测范围874.41-1733.91 nm）和化学计量学数据分析对含有cry1Ab/cry2Aj-G10evo蛋白的转基因玉米及其非转基因亲本玉米进行了鉴别研究。作者采用主成分分析法（PCA）、支持向量机（SVM）和偏最小二乘判别分析（PLS-DA）对数据进行分析，并建立判别模型来区分转基因玉米与非转基因玉米籽粒。研究发现，利用本文研发的无损检测方法可以很容易地识别出转基因玉米与非转基因玉米籽粒之间的差异，并能达到很好的分类效果。最后，通过预测单个高光谱图像中每个像素的特征可以在预测图上直观地识别出转基因玉米籽粒。研究结果表明，基于近红外光谱成像与化学计量学数据分析的技术是识别转基因玉米籽粒的一种很有前途的技术，它可以克服传统分析方法的复杂、简单采样等缺点。
外文摘要：There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of geneticallymodified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.
外文关键词：classification; NIR hyperspectral imaging; chemometrics analysis
作者：Feng, Xuping; Zhao, Yiying; Zhang, Chu; 等
作者单位：Zhejiang Univ
期刊名称：SENSORS
期刊影响因子：2.677
出版年份：2017
出版刊次：8
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