Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7598279 | Food Chemistry | 2014 | 7 Pages |
Abstract
Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Changhong Liu, Wei Liu, Xuzhong Lu, Wei Chen, Jianbo Yang, Lei Zheng,