Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods.
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.
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China.
Changhong
C
Liu
Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China.
Wei
W
Liu
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Xuzhong
X
Lu
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China.
Wei
W
Chen
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China. Electronic address: yjianbo@263.net.
Jianbo
J
Yang
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China; School of Medical Engineering, Hefei University of Technology, Hefei 230009, China. Electronic address: lzheng@hfut.edu.cn.
Lei
L
Zheng
D000083722:Bacillus thuringiensis Toxins; D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D016002:Discriminant Analysis; D004731:Endotoxins / Q000235:genetics / Q000378:metabolism; D030841:Food, Genetically Modified; D006460:Hemolysin Proteins / Q000235:genetics / Q000378:metabolism; D016571:Neural Networks, Computer; D012275:Oryza / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D030821:Plants, Genetically Modified / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D012639:Seeds / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D013057:Spectrum Analysis / Q000295:instrumentation / Q000379:methods*; D060388:Support Vector Machine
D000083722:Bacillus thuringiensis Toxins; D001426:Bacterial Proteins; D004731:Endotoxins; D006460:Hemolysin Proteins; C030956:insecticidal crystal protein, Bacillus Thuringiensis
Chemometric; Multispectral imaging; Nondestructive determination; Rice seed; Transgenic