中文摘要：基因工程技术在过去的几十年中取得了长足的进步，但这种技术对环境、伦理和宗教影响的潜在问题是未知的。因而，对农产品及其衍生产品中转基因生物进行检测是十分必要的。本文提出了一种基于近红外光谱（NIR）与化学计量学的转基因和非转基因玉米的鉴定方法。在近红外漫反射模式下测量了含有Cy1Ab／Cy2AJ-G10EVO蛋白的转基因玉米、其亲本、非转基因玉米的籽粒和面粉，其光谱范围为900-1700 nm，并利用Savitzky-Golay(SG)平滑算法对提取出的光谱数据进行去除噪声处理。基于全波段光谱和PCA主成分分别建立了偏最小二乘判别分析(PLS)和支持向量机判别模型(SVM)。研究发现，在转基因玉米籽粒全谱的判别分析模型中，SVM判别模型效果要优于PLS判别模型，SVM模型识别正确率达到90%以上，PLS的模型识别率只有85%左右。以PCA降维后建立的模型中，SVM模型也取得了最优的效果，建模集和预测集识别正确率达到100%。虽然转基因玉米在研磨加工后外源蛋白和DNA有所下降，但是转基因玉米粉末基于全波段光谱建立的SVM模型的建模集正确率仍有90.625%。研究结果表明，INR光谱技术和化学计量学方法可作为区分转基因玉米和其它转基因食品的可行方法。
外文摘要：Genetic engineering technique has made rapid strides in the past decades, however, the potential problems of this technique for environmental, ethical and religious impact are unknown. It is necessary to research on the detection of genetically modified organisms in agricultural crops and in products derived. In the present study, Near infrared spectroscopy (NIR) combined with chemometrics was successfully proposed to identify transgenic and non-transgenic maize. Transgenic maize single kernel and flour containing both cry1Ab/cry2Aj-G10evo protein and their parent, non-transgenic ones were measured in NIR diffuse reflectance mode with spectral range of 900 similar to 1 700 nm. Savitzky-Golay(SG)was used to preprocess the selection spectral region with absolute noises. Two classification methods, partial least square (PLS) and support vector machine (SVM); were used to build discrimination models based on the preprocessed full spectra and the dimension reduction information extracted by principal component analysis (PCA). Discriminant results of transgenic maize kernel based on SVM obtained a better performance by using the preprocessed full spectra compared to PLS model. The SVM achieved more than 90% calibration accuracy, while the PLS obtained just about 85% accuracy. By applying the PCA dimension reduction of the NIR reflectance in conjunction with the SVM model, the discrimination of transgenic from non-transgenic maize kernel was with accuracy up to 100% for both calibration set and validation set. The correct classification for transgenic and non-transgenic maize flour was 90. 625% using SVM based on preprocessed full spectra, although degration of exogenous gene and protein existed during the milling. The results indicated that INR spectroscopy techniques and chemometrics methods could be feasible ways to differentiate transgenic maize and other transgenic food.
外文关键词：Near infrared spectroscopy, Transgenic maize harboring cry1Ab/cry2Aj-G10evo, Partial least squares, Support vector machine
作者：Peng Cheng; Feng Xu-ping; He Yong; Zhang Chu; Zhao Yi-ying; Xu Jun-feng
作者单位：Zhejiang Univ
期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS
期刊影响因子：0.344
出版年份：2018
出版刊次：4