Detection of Roundup Ready™ Soybeans by Near-Infrared Spectroscopy

Roussel, Sylvie; Hardy, Connie L.; Hurburgh, Charles R.; Rippke, Glen R.

doi:10.1366/0003702011953586

Cited by 54 publications

(32 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This type of PLS is called PLS discriminant analysis. It is often used in hyper-spectral analysis (Olsson, Borjesson, Lundstedt, & Schnurer, 2000;Roussel, Bellon-Maurel, Roger, & Grenier, 2003;Roussel, Hardy, Hurburgh, & Rippke, 2001;Wang, Dowell, & Lacey, 1999).…”

Section: Partial Least Squaresmentioning

confidence: 99%

Bruise detection on Jonagold apples by visible and near-infrared spectroscopy

et al. 2005

View full text Add to dashboard Cite

Section: Partial Least Squaresmentioning

confidence: 99%

Bruise detection on Jonagold apples by visible and near-infrared spectroscopy

et al. 2005

View full text Add to dashboard Cite

“…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.…”

mentioning

confidence: 99%

Discrimination of transgenic soybean seeds by terahertz spectroscopy

Liu

Chen

et al. 2016

Sci Rep

View full text Add to dashboard Cite

Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.

show abstract

“…Recently, this technique has been used to distinguish transgenic products from conventional ones. Roussel, Hardy, Hurburgh, and Rippke (2001) detected and segregated Roundup Ready TM soybeans from conventional soybeans using partial leastsquares (PLS), locally weighted regression (LWR) and artificial neural networks (ANN) models by NIR spectroscopy. 93% accurate classification was obtained using a database of approximately 8000 samples with LWR method.…”

Section: Introductionmentioning

confidence: 99%