Discrimination of transgenic soybean seeds by terahertz spectroscopy
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...
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| Format: | Online |
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Nature Publishing Group
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080623/ |
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pubmed-50806232016-10-31 Discrimination of transgenic soybean seeds by terahertz spectroscopy Liu, Wei Liu, Changhong Chen, Feng Yang, Jianbo Zheng, Lei Article 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. Nature Publishing Group 2016-10-26 /pmc/articles/PMC5080623/ /pubmed/27782205 http://dx.doi.org/10.1038/srep35799 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Liu, Wei Liu, Changhong Chen, Feng Yang, Jianbo Zheng, Lei |
| spellingShingle |
Liu, Wei Liu, Changhong Chen, Feng Yang, Jianbo Zheng, Lei Discrimination of transgenic soybean seeds by terahertz spectroscopy |
| author_facet |
Liu, Wei Liu, Changhong Chen, Feng Yang, Jianbo Zheng, Lei |
| author_sort |
Liu, Wei |
| title |
Discrimination of transgenic soybean seeds by terahertz spectroscopy |
| title_short |
Discrimination of transgenic soybean seeds by terahertz spectroscopy |
| title_full |
Discrimination of transgenic soybean seeds by terahertz spectroscopy |
| title_fullStr |
Discrimination of transgenic soybean seeds by terahertz spectroscopy |
| title_full_unstemmed |
Discrimination of transgenic soybean seeds by terahertz spectroscopy |
| title_sort |
discrimination of transgenic soybean seeds by terahertz spectroscopy |
| description |
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. |
| publisher |
Nature Publishing Group |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080623/ |
| _version_ |
1613698618433208320 |