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

Abstract

SUMMARYSoybean 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.

Discrimination of transgenic soybean seeds by terahertz spectroscopy

“…As non-destructive technologies, spectroscopic techniques are rapid and easy to operate without complicated sample preparations. Infrared spectroscopy17, near infrared (NIR)18192021, visible/near infrared (VIS-NIR)22, and multispectral imaging2324 techniques combined with chemometric methods have shown their success in the rapid identification of GM organisms. Although many of the spectroscopic techniques mentioned above have been used to identify GM organisms, little attention has been paid to the use of terahertz (THz) spectroscopy for the detection of GM organisms.…”

Discrimination of haploid and diploid maize kernels via multispectral imaging

“…, Liu et al. ). This method is able to capitalize on the already existing marker system ( R1‐nj ) and would also not require the development of a new set of optical sensors/software to handle the data.…”

“…As mentioned in the introduction, other pilot studies have been conducted which evaluate the ability to use an automated system to discriminate between haploid and hybrid seed in an induction cross. The method proposed herein, as mentioned, uses the VideometerLab 3 system which has been documented as a useful tool for the non-destructive and automated analysis of seed phenotypes (Olesen et al 2011, Shetty et al 2012, Liu et al 2014. This method is able to capitalize on the already existing marker system (R1-nj) and would also not require the development of a new set of optical sensors/software to handle the data.…”

Multispectral Imaging for Plant Food Quality Analysis and Visualization

“…Also, the prediction of castor seed viability by using normalized canonical discriminant analysis (nCDA) resulted in a 96% classification accuracy, and it confirmed the feasibility to apply multispectral technology in seed viability testing (Olesen and others ). To classify rice and soybean seeds according to their varieties and genetic origins, LS‐SVM and BPNN models showed better performance (over 94%) than PLSDA models (Liu and others , , ). Additionally, multispectral imaging systems, combined with Fisher linear discriminant analysis (FLDA) and library support vector machine (Lib‐SVM), were used for nondestructive variety discrimination of 3 varieties of pears and 5 categories of teas.…”