Next Article in Journal
Lipofuscin Granule Bisretinoid Oxidation in the Human Retinal Pigment Epithelium forms Cytotoxic Carbonyls
Next Article in Special Issue
Integrative Study of the Structural and Dynamical Properties of a KirBac3.1 Mutant: Functional Implication of a Highly Conserved Tryptophan in the Transmembrane Domain
Previous Article in Journal
A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer
Previous Article in Special Issue
Site-Search Process for Synaptic Protein-DNA Complexes
 
 
Article

Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

1
Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea
2
Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi AK-039-5028, Ghana
3
Institute for Future Environmental Ecology Co., Ltd., Jeonju 54883, Korea
4
Seed Conservation Research Division, Baekdudewgan National Arboretum, Bonghwa 36209, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Ian A. Nicholls and Vladimir N. Uversky
Int. J. Mol. Sci. 2022, 23(1), 220; https://doi.org/10.3390/ijms23010220
Received: 4 November 2021 / Revised: 22 December 2021 / Accepted: 22 December 2021 / Published: 25 December 2021
(This article belongs to the Collection Feature Papers in Molecular Biophysics)
In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination. View Full-Text
Keywords: Brassica rapa; transgenic canola; GM detection; Vis-NIR spectroscopy; chemometrics; machine learning Brassica rapa; transgenic canola; GM detection; Vis-NIR spectroscopy; chemometrics; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Sohn, S.-I.; Pandian, S.; Zaukuu, J.-L.Z.; Oh, Y.-J.; Park, S.-Y.; Na, C.-S.; Shin, E.-K.; Kang, H.-J.; Ryu, T.-H.; Cho, W.-S.; Cho, Y.-S. Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods. Int. J. Mol. Sci. 2022, 23, 220. https://doi.org/10.3390/ijms23010220

AMA Style

Sohn S-I, Pandian S, Zaukuu J-LZ, Oh Y-J, Park S-Y, Na C-S, Shin E-K, Kang H-J, Ryu T-H, Cho W-S, Cho Y-S. Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods. International Journal of Molecular Sciences. 2022; 23(1):220. https://doi.org/10.3390/ijms23010220

Chicago/Turabian Style

Sohn, Soo-In, Subramani Pandian, John-Lewis Zinia Zaukuu, Young-Ju Oh, Soo-Yun Park, Chae-Sun Na, Eun-Kyoung Shin, Hyeon-Jung Kang, Tae-Hun Ryu, Woo-Suk Cho, and Youn-Sung Cho. 2022. "Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods" International Journal of Molecular Sciences 23, no. 1: 220. https://doi.org/10.3390/ijms23010220

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop