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ISSN: 1023-5086

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ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

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Terahertz spectroscopy detection of genetically modified sugar beets containing the Xa21 gene based on chemometrics methods

For Russian citation (Opticheskii Zhurnal):

Jianjun Liu Terahertz spectroscopy detection of genetically modified sugar beets containing the Xa21 gene based on chemometrics methods (Определение содержания гена Xа21 в генетически модифицированной сахарной свёкле методом терагерцовой спектроскопии в сочетании с хемометрическими методами) [на англ. яз.] // Оптический журнал. 2016. Т. 83. № 10. С. 60–65.

 

Jianjun Liu Terahertz spectroscopy detection of genetically modified sugar beets containing the Xa21 gene based on chemometrics methods (Определение содержания гена Xа21 в генетически модифицированной сахарной свёкле методом терагерцовой спектроскопии в сочетании с хемометрическими методами) [in English] // Opticheskii Zhurnal. 2016. V. 83. № 10. P. 60–65.

For citation (Journal of Optical Technology):

Jianjun Liu, "Terahertz spectroscopy detection of genetically modified sugar beets containing the Xa21 gene based on chemometrics methods," Journal of Optical Technology. 83(10), 627-631 (2016). https://doi.org/10.1364/JOT.83.000627

Abstract:

Because traditional detection of genetically modified organisms (GMOs) has the disadvantages of being costly, time consuming, and awkward to perform, a novel method for the detection of GMOs based on terahertz spectroscopy combined with chemometrics methods is proposed. In this work, terahertz spectrum data of a genetically modified sugar beet and its parents are pretreated by using principal component analysis and then using the weighted discriminant analysis model, which is an improved discriminant analysis that applys a weighted algorithm to the detection of a genetically modified sugar beet and its parents. It is found from the experimental results that the samples are expressed by the zonation of saline minerals. By analyzing this phenomenon, it is easy to identify these genetically modified sugar beets. By combining terahertz spectroscopy with chemometrics methods, this paper provides a precise, fast, convenient, and nondestructive method for the detection of GMOs.

Keywords:

THz, genetically modified, spectroscopy, detection, WDA

References:

1. Fujimot H., Itoh K., Yamamoto M., Kyozuka J., Shimamoto K. Insect resistant rice generated by introduction of a modified δ-endotoxin gene of Bacillus thuringiensis // Nat. Biotechnol. 1994. V. 4. № 4. P. 485–485.
2. Aviron S., Sanvido O., Romeis J., Herzog F., Bigler F. Case-specific monitoring of butterflies to determine potential effects of transgenic Bt-maize in Switzerland // Agr. Ecosyst. Environ. 2009. V. 131. № 3. P. 137–144.
3. Zhang X. Rapid isolation of single-chain antibodies from a human synthetic phage display library for detection of Bacillus thuringiensis (Bt) Cry1B toxin // Ecotoxicol. Environ. Safety. 2012. V. 81. № 1. P. 84–90.
4. Giovannoli C., Anfossi L., Baggiani C., Giraudi G. Binding properties of a monoclonal antibody against the Cry1Ab from Bacillus Thuringensis for the development of a capillary electrophoresis competitive immunoassay // Anal. Bioanal. Chem. 2008. V. 392. № 3. P. 385–393.
5. Vergragt P.J., Brown H.S. Genetic engineering in agriculture: new approaches for risk management through sustainability reporting // Technol. Forecast. Soc. Change. 2008. V. 75. P. 783–798.

6. Borjigin M., Eskridge C., Niamat R. Electrospun fiber membranes enable proliferation of genetically modified cells // International Journal of Nanomedicine. 2013. V. 8. P. 855–864.
7. Milcamps A., Rabe S., Cade R. Validity assessment of the detection method of maize event Bt10 through investigation of its molecular structure // Journal of Agricultural and Food Chemistry. 2009. V. 57. №. 8. P. 3156–3163.
8. Fiehn O., Kopka J., Trethewey N. Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry // Analytical Chemistry. 2000. V. 72. № 15. P. 3573–3580.
9. Margarit E., Reggiardo M.I., Vallejos R.H. Detection of BT Genetically Modified maize in foodstuffs // Food Research International. 2006. V. 39. P. 250–255.
10. Zhu D., Liu J.F., Tang Y.B. A reusable DNA biosensor for the detection of genetically modified organism using magnetic bead-based electrochemiluminescence // Sensors and Actuators B. 2010. V. 149. № 1. P. 221–225.
11. Baranski R., Baranska M. Discrimination between nongenetically modified (non-GM) and GM plant tissue expressing cysteine-rich polypeptide using FT-Raman spectroscopy // Agr. Food Chem. 2008. V. 56. № 12. P. 4491–4496.
12. Roussel S.A., Hardy C.L., Hurburgh C.R., Rippke G.R. Detection of Roundup Ready-soybeans by near-infrared spectroscopy // Appl. Spectrosc. 2001. V. 55. P. 1425–1430.
13. Xie L., Ying Y., Ying T. Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique // Food Eng. 2007. V. 82. P. 395–401.
14. Xie L., Ying Y., Ying T., Yu H., Fu X. Discrimination of transgenic tomatoes based on visible/near-infrared spectra // J. Anal. Chim. Acta. 2007. V. 584. № 2. P. 379–384.
15. Xu W., Liu X., Xie L., Ying Y. Comparison of Fourier Transform near-infrared, visible near-infrared, midinfrared, and Raman spectroscopy as non-invasive tools for transgenic rice discrimination // Trans. ASABE. 2014. V. 57. № 1. P. 141–150.
16. Jianjun Liu, Zhi Li, Fangrong Hu. Hyper sausage neuron: recongnition of transgenic sugar-beet based on terahertz spectroscopy // Optics and Spectroscopy. 2015. V. 118. № 1. P. 182–187.
17. Jianjun Liu, Zhi Li, Fangrong Hu. Method for identifying transgenic cottons based on terahertz spectra and WLDA // Optik. 2015. V. 126. № 19. P. 1872–1877.
18. Jianjun Liu, Zhi Li, Fangrong Hu. A THz spectroscopy nondestructive identification method for transgenic cotton seed based on GA-SVM // Optical and Quantum Electronics. 2015. V. 47. № 2. P. 313–322.
19. Jianjun Liu, Zhi Li. Identification of GMOs by terahertz spectroscopy and ALAP-SVM // Optical and Quantum Electronics. 2014. V. 47. № 3. P. 685–695.
20. Wendao Xu, Lijuan Xie, Zunzhong Ye. Discrimination of transgenic rice containing the cry1ab protein using terahertz spectroscopy and chemometrics // Scientific Reports. 2015. V. 5. № 11115. P. 1–9.
21. Zhang X.R., Liu F. A patten classification method based on GA and SVM // 6th International Conference on Signal Processing. 2002. P. 110–113.
22. Vaitilingom M., Pijnenburg H., Gendre F., Brignon P. Real-time quantitative PCR detection of genetically modified Maximizer maize and Roundup Ready soybean in some representative foods // J. Agr. Food Chem. 1999. V. 47. P. 5261–5266.