Feng Xuping, Zhao Yiying, Zhang Chu, Cheng Peng, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Institute of Quality and Standard for Agro-Products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
Sensors (Basel). 2017 Aug 17;17(8):1894. doi: 10.3390/s17081894.
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 genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both 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.
转基因生物的基因流动存在潜在的环境风险。找到准确、快速且经济的方法来检测和监测作物及衍生作物产品中转基因生物的存在非常重要。在本研究中，利用近红外（NIR）范围（874.41 - 1733.91 nm）的高光谱成像结合化学计量数据分析，对含有两种蛋白质的转基因玉米籽粒及其非转基因亲本进行了检测。通过应用主成分分析（PCA）进行探索性分析，并使用支持向量机（SVM）和偏最小二乘判别分析（PLS - DA）构建判别模型，以区分转基因玉米籽粒与其对照。结果表明，利用本研究开发的无损测定方法可以轻松直观地看出转基因和非转基因玉米籽粒之间的明显差异，并且能够实现优异的分类，计算和预测准确率几乎达到100%。本研究还表明，通过竞争性自适应重加权采样法（CARS）选择的54个波长，SVM和PLS - DA模型可以获得良好的性能，使得在线应用的分类处理更加快速。最后，通过预测各个高光谱图像上每个像素的特征，在预测图上直观地识别出转基因玉米籽粒。得出的结论是，高光谱成像与化学计量数据分析相结合是一种很有前景的鉴定转基因玉米籽粒的技术，因为它克服了传统分析方法的一些缺点，如采样复杂和单一。
