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Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods

Published online by Cambridge University Press:  10 November 2014

C. LIU ,
W. LIU ,
X. LU ,
W. CHEN ,
F. CHEN ,
J. YANG  and
L. ZHENG
C. LIU
Affiliation:
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
W. LIU
Affiliation:
Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
X. LU
Affiliation:
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
W. CHEN
Affiliation:
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
F. CHEN
Affiliation:
Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, SC 29634, USA
J. YANG*
Affiliation:
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
L. ZHENG*
Affiliation:
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China School of Medical Engineering, Hefei University of Technology, Hefei 230009, China
*
*To whom all correspondence should be addressed. Email: [email protected] and [email protected]; [email protected]
*To whom all correspondence should be addressed. Email: [email protected] and [email protected]; [email protected]
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Summary

Soybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 154 , Issue 1 , January 2016 , pp. 1 - 12
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Copyright
Copyright © Cambridge University Press 2014 

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References

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