Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-23T03:39:24.489Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods

Published online by Cambridge University Press:  10 November 2014

C. LIU ,
W. LIU ,
X. LU ,
W. CHEN ,
F. CHEN ,
J. YANG  and
L. ZHENG
C. LIU
Affiliation:
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
W. LIU
Affiliation:
Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
X. LU
Affiliation:
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
W. CHEN
Affiliation:
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
F. CHEN
Affiliation:
Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, SC 29634, USA
J. YANG*
Affiliation:
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
L. ZHENG*
Affiliation:
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China School of Medical Engineering, Hefei University of Technology, Hefei 230009, China
*
*To whom all correspondence should be addressed. Email: [email protected] and [email protected]; [email protected]
*To whom all correspondence should be addressed. Email: [email protected] and [email protected]; [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Soybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 154 , Issue 1 , January 2016 , pp. 1 - 12
Check for updates
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abdullah, T., Radu, S., Hassan, Z. & Hashim, J. K. (2006). Detection of genetically modified soy in processed foods sold commercially in Malaysia by PCR-based method. Food Chemistry 98, 575–579.CrossRefGoogle Scholar
Acevedo, F. J., Jiménez, J., Maldonado, S., Domínguez, E. & Narváez, A. (2007). Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. Journal of Agricultural and Food Chemistry 55, 6842–6849.CrossRefGoogle ScholarPubMed
Agelet, L. E., Gowen, A. A., Hurburgh, C. R. Jr & O'Donell, C. P. (2012). Feasibility of conventional and Roundup Ready® soybeans discrimination by different near infrared reflectance technologies. Food Chemistry 134, 1165–1172.CrossRefGoogle Scholar
Ahrent, D. K. & Caviness, C. E. (1994). Natural cross-pollination of twelve soybean cultivars in Arkansas. Crop Science 34, 376–378.CrossRefGoogle Scholar
Bakshi, A. (2003). Potential adverse health effects of genetically modified crops. Journal of Toxicology and Environmental Health, Part B 6, 211–225.CrossRefGoogle ScholarPubMed
Chiang, Y. C. & Kiang, Y. T. (1987). Geometric position of genotypes, honeybee foraging patterns and outcrossing in soybean. Botanical Bulletin Academia Sinica 28, 1–11.Google Scholar
Christiansen, A. N., Carstensen, J. M., Papadopoulou, O., Chorianopoulos, N., Panagou, E. Z. & Nychas, G. J. E. (2011). Multi spectral imaging analysis for meat spoilage discrimination. In Proceedings of the 7th International Conference on Predictive Modelling of Food Quality and Safety, Dublin, Ireland, 12–15 September 2011 (Eds Cummins, E., Frías, J. M. & Valdramidis, V. P.), pp. 384–387. Dublin, Ireland: University College Dublin, Dublin Institute of Technology & Teagasc Food research Centre.Google Scholar
Christiansen, A. N., Carstensen, J. M., Møller, F. & Nielsen, A. A. (2012). Monitoring the change in colour of meat: a comparison of traditional and kernel-based orthogonal transformations. Journal of Spectral Imaging 3, 1–10. doi: 10.1255/jsi.2012.a1CrossRefGoogle Scholar
Cortes, C. & Vapnik, V. (1995). Support vector network. Machine Learning 20, 273–297.CrossRefGoogle Scholar
Cruz-Castillo, J. G., Ganeshanandam, S., MacKay, B. R., Lawes, G. S., Lawoko, C. R. O. & Woolley, D. J. (1994). Applications of canonical discriminant analysis in horticultural research. HortScience 29, 1115–1119.CrossRefGoogle Scholar
Cui, D., Zhang, Q., Li, M., Hartman, G. L. & Zhao, Y. (2010). Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosystems Engineering 107, 186–193.CrossRefGoogle Scholar
Cunha, W. G., Tinoco, M. L. P., Pancoti, H. L., Ribeiro, R. E. & Aragão, F. J. L. (2010). High resistance to Sclerotinia sclerotiorum in transgenic soybean plants transformed to express an oxalate decarboxylase gene. Plant Pathology 59, 654–660.CrossRefGoogle Scholar
Daugaarda, S. B., Adler-Nissen, J. & Carstensen, J. M. (2010). New vision technology for multidimensional quality monitoring of continuous frying of meat. Food Control 21, 626–632.CrossRefGoogle Scholar
Devos, O., Ruckebusch, C., Durand, A., Duponchel, L. & Huvenne, J. P. (2009). Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemometrics and Intelligent Laboratory Systems 96, 27–33.CrossRefGoogle Scholar
Dissing, B. S. (2011). New vision technology for multidimensional quality monitoring of food processes. Ph.D. Thesis, Technical University of Denmark (DTU).Google Scholar
Dissing, B. S., Nielsen, M. E., Ersbøll, B. K. & Frosch, S. (2011). Multispectral imaging for determination of astaxanthin concentration in salmonids. PLoS ONE 6, e19032. doi: 10.1371/journal.pone.0019032CrossRefGoogle ScholarPubMed
Dissing, B. S., Papadopoulou, O. S., Tassou, C., Ersbøll, B. K., Carstensen, J. M., Panagou, E. Z. & Nychas, G. J. (2013). Using multispectral imaging for spoilage detection of pork meat. Food and Bioprocess Technology 6, 2268–2279.CrossRefGoogle Scholar
Dörries, H. H., Remus, I., Grönewald, A., Grönewald, C. & Berghof-Jäger, K. (2010). Development of a qualitative, multiplex real-time PCR kit for screening of genetically modified organisms (GMOs). Analytical and Bioanalytical Chemistry 396, 2043–2054.CrossRefGoogle ScholarPubMed
Erickson, E. H. (1975). Effect of honey bees on yield of three soybean cultivars. Crop Science 15, 84–86.CrossRefGoogle Scholar
Feng, Y. Z. & Sun, D. W. (2012). Application of hyperspectral imaging in food safety inspection and control: a review. Critical Reviews in Food Science and Nutrition 52, 1039–1058.CrossRefGoogle ScholarPubMed
García, M. C., García, B., García-Ruiz, C., Gómez, A., Cifuentes, A. & Marina, M. L. (2009). Rapid characterisation of (glyphosate tolerant) transgenic and non-transgenic soybeans using chromatographic protein profiles. Food Chemistry 113, 1212–1217.CrossRefGoogle Scholar
Gray, C. J., Shaw, D. R., Gerard, P. D. & Bruce, L. M. (2008). Utility of multispectral imagery for soybean and weed species differentiation. Weed Technology 22, 713–718.CrossRefGoogle Scholar
Gøtterup, J., Olsen, K., Knøchel, S., Tjener, K., Stahnke, L. H. & Møller, J. K. S. (2008). Colour formation in fermented sausages by meat-associated staphylococci with different nitrite and nitrate-reductase activities. Meat Science 78, 492–501.CrossRefGoogle ScholarPubMed
Hübner, P., Waiblinger, H. U., Pietsch, K. & Brodmann, P. (2001). Validation of PCR methods for quantitation of genetically modified plants in food. Journal of AOAC International 84, 1855–1864.CrossRefGoogle ScholarPubMed
James, C. (2010). Executive Summary. Global Status of Commercialized Biotech/GM Crops: 2010. ISAAA Brief 42–2010. Ithaca, NYA: ISAAA. Available from: http://www.isaaa.org/resources/publications/briefs/42/executivesummary/ (verified September 2014).Google Scholar
Ishimoto, M., Rahman, S. M., Hanafy, M. S., Khalafalla, M. M., El-Shemy, H. A., Nakamoto, Y., Kita, Y., Takanashi, K., Matsuda, F., Murano, Y., Funabashi, T., Miyagawa, H. & Wakasa, K. (2010). Evaluation of amino acid content and nutritional quality of transgenic soybean seeds with high-level tryptophan accumulation. Molecular Breeding 25, 313–326.CrossRefGoogle Scholar
Jiao, Z., Si, X. X., Li, G. K., Zhang, Z. M. & Xu, X. P. (2010). Unintended compositional changes in transgenic rice seeds (Oryza sativa L.) studied by spectral and chromatographic analysis coupled with chemometrics methods. Journal of Agricultural and Food Chemistry 58, 1746–1754.CrossRefGoogle ScholarPubMed
Kim, W. S., Chronis, D., Juergens, M., Schroeder, A. C., Hyun, S. W., Jez, J. M. & Krishnan, H. B. (2012). Transgenic soybean plants overexpressing O-acetylserine sulfhydrylase accumulate enhanced levels of cysteine and Bowman–Birk protease inhibitor in seeds. Planta 235, 13–23.CrossRefGoogle ScholarPubMed
Konduru, S., Kruse, J. & Kalaitzandonakes, N. (2008). The global economic impacts of Roundup Ready soybeans. In Genetics and Genomics of Soybean (Ed. Stacey, G.), pp. 375–395. Plant Genetics and Genomics: crops and Models, Vol 2. New York: Springer Science +Business Media, LLC.CrossRefGoogle Scholar
Lee, J. H. & Choung, M. G. (2011). Nondestructive determination of herbicide-resistant genetically modified soybean seeds using near-infrared reflectance spectroscopy. Food Chemistry 126, 368–373.CrossRefGoogle Scholar
Liu, C., Liu, W., Lu, X., Ma, F., Chen, W., Yang, J. & Zheng, L. (2014). Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PLoS ONE 9, e87818. doi: 10.1371/journal.pone.0087818Google ScholarPubMed
Liu, G., Su, W., Xu, Q., Long, M., Zhou, J. & Song, S. (2004). Liquid-phase hybridization based PCR-ELISA for detection of genetically modified organisms in food. Food Control 15, 303–306.CrossRefGoogle Scholar
Ljungqvist, M. G., Frosch, S., Nielsen, M. E. & Ersbøll, B. K. (2013). Multispectral image analysis for robust prediction of astaxanthin coating. Applied Spectroscopy 67, 738–746.CrossRefGoogle ScholarPubMed
Lleó, L., Barreiro, P., Ruiz-Altisent, M. & Herrero, A. (2009). Multispectral images of peach related to firmness and maturity at harvest. Journal of Food Engineering 93, 229–235.CrossRefGoogle Scholar
Løkke, M. M., Seefeldt, H. F., Skov, T. & Edelenbos, M. (2013). Color and textural quality of packaged wild rocket measured by multispectral imaging. Postharvest Biology and Technology 75, 86–95.CrossRefGoogle Scholar
McPherson, R. M. & MacRae, T. C. (2009). Evaluation of transgenic soybean exhibiting high expression of a synthetic Bacillus thuringiensis cry1A transgene for suppressing lepidopteran population densities and crop injury. Journal of Economic Entomology 102, 1640–1648.CrossRefGoogle ScholarPubMed
Obeid, P. J., Christopoulos, T. K. & Ionnou, P. C. (2004). Rapid analysis of genetically modified organisms by in-house developed capillary electrophoresis chip and laser-induced fluorescence system. Electrophoresis 25, 922–930.CrossRefGoogle ScholarPubMed
Ocaña, M. F., Fraser, P. D., Patel, R. K. P., Halket, J. M. & Bramley, P. M. (2007). Mass spectrometry detection of CP4 EPSPS in genetically modified soya and maize. Rapid Communications in Mass Spectrometry 21, 319–328.CrossRefGoogle ScholarPubMed
Park, B., Lawrence, K. C., Windham, W. R. & Smith, D. P. (2004). Multispectral imaging system for fecal and ingesta detection on poultry carcasses. Journal of Food Process Engineering 27, 311–327.CrossRefGoogle Scholar
Rød, S. K., Hansen, F., Leipold, F. & Knøchel, S. (2012). Cold atmospheric pressure plasma treatment of ready-to-eat meat: inactivation of listeria innocua and changes in product quality. Food Microbiology 30, 233–238.CrossRefGoogle ScholarPubMed
Rong, J., Song, Z., Su, J., Xia, H., Lu, B. R. & Wang, F. (2005). Low frequency of transgene flow from Bt/CpTI rice to its nontransgenic counterparts planted at close spacing. New Phytologist 168, 559–566.CrossRefGoogle ScholarPubMed
Roussel, S. A., Hardy, C. L., Hurburgh, C. R. Jr & Rippke, G. R. (2001). Detection of Roundup ReadyÔ soybeans by near-infrared spectroscopy. Applied Spectroscopy 55, 1425–1430.CrossRefGoogle Scholar
Seo, J. S., Sohn, H. B., Noh, K., Jung, C., An, J. H., Donovan, C. M., Somers, D. A., Kim, D. I., Jeong, S. C., Kim, C. G., Kim, H. M., Lee, S. H., Choi, Y. D., Moon, T. W., Kim, C. H. & Cheong, J. J. (2012). Expression of the Arabidopsis AtMYB44 gene confers drought/salt-stress tolerance in transgenic soybean. Molecular Breeding 29, 601–608.CrossRefGoogle Scholar
Wang, J. (2000). From DNA biosensors to gene chips. Nucleic Acids Research 28, 3011–3016.CrossRefGoogle ScholarPubMed
WHO (2002). Foods Derived from Modern Technology: 20 Questions on Genetically Modified Foods. Geneva, Switzerland: WHO.Google Scholar
Xie, L., Ying, Y. & Ying, T. (2009). Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics. Journal of Food Engineering 94, 34–39.CrossRefGoogle Scholar
Yoshimura, Y., Matsuo, K. & Yasuda, K. (2006). Gene flow from GM glyphosate-tolerant to conventional soybeans under field conditions in Japan. Environmental Biosafety Research 5, 169–173.CrossRefGoogle ScholarPubMed