文献类型： 外文期刊

作者： Peng Cheng ¹ ; Feng Xu-ping ² ; He Yong ² ; Zhang Chu ² ; Zhao Yi-ying ² ; Xu Jun-feng ¹ ;

作者机构： 1.Zhejiang Acad Agr Sci, State Key Lab Breeding Base Zhejiang Sustainable, Hangzhou 310021, Zhejiang, Peoples R China

2.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China

关键词： Near infrared spectroscopy; Transgenic maize harboring cry1Ab/cry2Aj-G10evo; Partial least squares; Support vector machine

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2018 年 38 卷 4 期

页码：

收录情况： SCI

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.