您好，欢迎访问浙江省农业科学院 机构知识库！
浙江省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis

文献类型： 外文期刊

作者： Feng, Xuping 1 ; Zhao, Yiying 1 ; Zhang, Chu 1 ; Cheng, Peng 2 ; He, Yong 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Qual & Stand Agroprod, Hangzhou 310021, Zhejiang, Peoples R China

关键词： classification;NIR hyperspectral imaging;chemometrics analysis

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN： 1424-8220

年卷期： 2017 年 17 卷 8 期

页码：

收录情况： SCI

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.

  • 相关文献

[1]A Simple and Efficient Method for CRISPR/Cas9-Induced Rice Mutant Screening. Feng Xu-ping,Zhang Chu,Liu Xiao-dan,Shen Ting-ting,He Yong,Feng Xu-ping,Zhang Chu,Liu Xiao-dan,Shen Ting-ting,He Yong,Peng Cheng,Xu Jun-feng. 2018

[2]CAMELLIA (THEACEAE) CLASSIFICATION WITH SUPPORT VECTOR MACHINES BASED ON FRACTAL PARAMETERS AND RED, GREEN, AND BLUE INTENSITY OF LEAVES. Jiang, W.,Tao, Z. M.,Wu, Z. G.,Mantri, N.,Lu, H. F.,Liang, Z. S.,Jiang, W.,Tao, Z. M.,Wu, Z. G.,Mantri, N.. 2017

[3]Spatial distribution of soil organic matter in tillage layers in a southern China basin using classifications and spatial interpolation algorithms. Deng, X. F.,Lv, X. N.,Zhang, M. H.,Li, S. Q.. 2013

[4]Genome organization and phylogenetic tree analysis of Garlic virus E, a new member of genus Allexivirus. Chen, J,Chen, JP. 2002

[5]Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes. Nansen, Christian,Singh, Keshav,Nansen, Christian,Mian, Ajmal,Allison, Brittany J.,Simmons, Christopher W..

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：浙江省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台