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

被引:13
作者
Sohn, Soo-In [1 ]
Pandian, Subramani [1 ]
Zaukuu, John-Lewis Zinia [2 ]
Oh, Young-Ju [3 ]
Park, Soo-Yun [1 ]
Na, Chae-Sun [4 ]
Shin, Eun-Kyoung [1 ]
Kang, Hyeon-Jung [1 ]
Ryu, Tae-Hun [1 ]
Cho, Woo-Suk [1 ]
Cho, Youn-Sung [1 ]
机构
[1] Natl Inst Agr Sci, Rural Dev Adm, Dept Agr Biotechnol, Jeonju 54874, South Korea
[2] Kwame Nkrumah Univ Sci & Technol KNUST, Dept Food Sci & Technol, Kumasi Ak 0395028, Ghana
[3] Inst Future Environm Ecol Co Ltd, Jeonju 54883, South Korea
[4] Baekdudewgan Natl Arboretum, Seed Conservat Res Div, Bonghwa 36209, South Korea
关键词
Brassica rapa; transgenic canola; GM detection; Vis-NIR spectroscopy; chemometrics; machine learning; NEAR-INFRARED SPECTROSCOPY; OILSEED RAPE; QUALITY; EXPRESSION; BEVERAGES; SYSTEMS; CROPS; LIGHT;
D O I
10.3390/ijms23010220
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
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页数:15
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