Screening of transgenic maize using near infrared spectroscopy and chemometric techniques

Xuping Feng, Haijun Yin, Chu Zhang, Cheng Peng, Yong He

Abstract


The applicability of near infrared (NIR) spectroscopy combined with chemometrics was examined to develop fast, low-cost and non-destructive spectroscopic methods for classification of transgenic maize plants. The transgenic maize plants containing both cry1Ab/cry2Aj-G10evo proteins and their non-transgenic parent were measured in the NIR diffuse reflectance mode with the spectral range of 700–1900 nm. Three variable selection algorithms, including weighted regression coefficients, principal component analysis -loadings and second derivatives were used to extract sensitive wavelengths that contributed the most discrimination information for these genotypes. Five classification methods, including K-nearest neighbor, Soft Independent Modeling of Class Analogy, Naive Bayes Classifier, Extreme Learning Machine (ELM) and Radial Basis Function Neural Network were used to build discrimination models based on the preprocessed full spectra and sensitive wavelengths. The results demonstrated that ELM had the best performance of all methods, even though the model’s recognition ability decreased as the variables in the training of neural networks were reduced by using only the sensitive wavelengths. The ELM model calculated on the calibration set showed classification rates of 100% based on the full spectrum and 90.83% based on sensitive wavelengths. The NIR spectroscopy combined with chemometrics offers a powerful tool for evaluating large number of samples from maize hybrid performance trials and breeding programs.


Keywords


facile screening method; Zea mays; transgenic maize selection; discrimination model

Full Text:

PDF HTML XML

References


Alishahi A, Farahmand H, Prieto N, Cozzolino D, 2010. Identification of transgenic foods using NIR spectroscopy: a review. Spectrochim Acta Part A 75 (1): 1-7. https://doi.org/10.1016/j.saa.2009.10.001

Barbin D, Elmasry G, Sun DW, Allen P, 2012. Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci 90 (1): 259-268. https://doi.org/10.1016/j.meatsci.2011.07.011

Beghi R, Giovenzana V, Marai S, Guidetti R, 2015. Rapid monitoring of grape withering using visible near‐infrared spectroscopy. J Sci Food Agr 95 (15): 3144-3149. https://doi.org/10.1002/jsfa.7053

Boyd DS, Entwistle JA, Flowers AG, Armitage RP, Goldsmith PC, 2006. Remote sensing the radionuclide contaminated Belarusian landscape: a potential for imaging spectrometry? Int J Remote Sens 27 (10): 1865-1874. https://doi.org/10.1080/01431160500328355

Bryant FB, Yarnold PR, 1995. Principal-components analysis and exploratory and confirmatory factor analysis. In: Reading and understanding multivariate statistics; Grimm LG & Yarnold PR (Eds.), pp: 99-136. Am Psychol Assoc, Washington DC.

Dai Q, Cheng JH, Sun DW, Zeng XA, 2015. Advances in feature selection methods for hyperspectral image processing in food industry applications: A review. Crit Rev Food Sci 55 (10): 1368-1382. https://doi.org/10.1080/10408398.2013.871692

De Bei R, Cozzolino D, Sullivan W, Cynkar W, Fuentes S, Dambergs R, Tyerman S, 2011. Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Aust J Grape Wine R 17 (1): 62-71. https://doi.org/10.1111/j.1755-0238.2010.00117.x

Feng X, Zhao Y, Zhang C, Cheng P, He Y, 2017. Discrimination of transgenic maize kernel using NIR hyperspectral imaging and multivariate data analysis. Sensors 17 (8): 1894. https://doi.org/10.3390/s17081894

García-Molina MD, García-Olmo J, Barro F, 2016. Effective identification of low-gliadin wheat lines by near infrared spectroscopy (NIRS): implications for the development and analysis of foodstuffs suitable for celiac patients. Plos One 11 (3): e0152292. https://doi.org/10.1371/journal.pone.0152292

Gil-Pita R, Yao X, 2009. Evolving edited k-nearest neighbor classifiers. Int J Neural Syst 18 (6): 459-467. https://doi.org/10.1142/S0129065708001725

Guo H, Pan T, Chen J, Wang J, Cao G, 2014. Vis−NIR wavelength selection for non-destructive siscriminant analysis of breed screening of transgenic sugarcane. Anal Methods-UK 6 (21): 8810-8816. https://doi.org/10.1039/C4AY01833H

Huang GB, Zhou H, Ding X, Zhang R, 2012. Extreme learning machine for regression and multiclass classification. IEEE T Syst Man CY B 42 (2): 513-529. https://doi.org/10.1109/TSMCB.2011.2168604

Islam M J, Wu QMJ, Ahmadi M, Sid-Ahmed MA, 2007. Investigating the performance of naive- bayes classifiers and k- nearest neighbor classifiers. J Converg Inform Technol 5 (2): 133-137. https://doi.org/10.1109/ICCIT.2007.148

Jin H, Li L, Cheng J, 2015. Rapid and non-destructive determination of moisture content of peanut kernels using hyperspectral imaging technique. Food Anal Method 8 (10): 1-9. https://doi.org/10.1007/s12161-015-0147-1

Kamle S, Ojha A, Kumar A. 2011. Development of an enzyme linked immunosorbant assay for the detection of Cry2Ab Protein in transgenic plants. Gm Crops 2 (2): 118-125. https://doi.org/10.4161/gmcr.2.2.16191

Kosic D, 2015. Fast clustered radial basis function network as an adaptive predictive controller. Neural Netw 63: 79-86. https://doi.org/10.1016/j.neunet.2014.11.008

Kumaravelu C, Ravi A, Gopal A, Joshi J, 2017. Estimation of oil content of single cotton seed using NIR spectrometer by area under curve method. Trends in Industrial Measurement and Automation (TIMA), IEEE Conf, pp: 1-4. https://doi.org/10.1109/TIMA.2017.8064798

Lian C, Zeng Z, Yao W, Tang H, 2014. Performance of combined artificial neural networks for forecasting landslide displacement. IEEE Conf, pp: 418-423. https://doi.org/10.1109/IJCNN.2014.6889497

Liu C, Liu W, Lu X, Chen W, Yang J, Zheng L, 2014. Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods. Food Chem 153 (12): 87-93. https://doi.org/10.1016/j.foodchem.2013.11.166

Liu Y, Qin L, Han L, Xiang Y, Zhao D, 2015. Overexpression of maize SDD1 (ZmSDD1) improves drought resistance in Zea mays L. by reducing stomatal density. Plant Cell Tiss Org 122 (1): 147-159. https://doi.org/10.1007/s11240-015-0757-8

Luna AS, Silva APD, Pinho JSA, Ferré J & Boqué R, 2013. Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy. Spectrochim Acta A 100: 115-119. https://doi.org/10.1016/j.saa.2012.02.085

Minami H, Iwahashi M, 2011. Molecular self-assembling of N-methylacetamide in solvents. Int J Specty 2011: 640121. https://doi.org/10.1155/2011/640121

Murayama K, Czarnikmatusewicz B, Wu Y, Tsenkova R, Ozaki Y, 2000. Comparison between Conventional Spectral Analysis Methods, Chemometrics, and Two-Dimensional Correlation Spectroscopy in the Analysis of Near-Infrared Spectra of Protein. Appl Spectrosc 54 (7): 978-985. https://doi.org/10.1366/0003702001950715

Pan T, Xie J, Chen J, Chen H, 2010. Joint optimization of savitzky-golay smoothing modes and PLS factors was applied to near infrared spectral analysis of serum cholesterol. IEEE Conf, pp: 1-4. https://doi.org/10.1109/ICBBE.2010.5514789

Rinnan Å, Berg FVD, Engelsen SB, 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trac-Trend Anal Chem 28 (10): 1201-1222. https://doi.org/10.1016/j.trac.2009.07.007

Rodríguez-Pulido FJ, Barbin DF, Sun DW, Gordillo B, González-Miret ML, Heredia FJ, 2013. Grape seed characterization by NIR hyperspectral imaging. Postharvest Biol Tec 76: 74-82. https://doi.org/10.1016/j.postharvbio.2012.09.007

Saad AG, Pék Z, Szuvandzsiev P, Gehad DH, Helyes L, Saad AG, Pék Z, Szuvandzsiev P, Gehad DH, Helyes L, 2017. Determination of carotenoids in tomato products using Vis/NIR spectroscopy. J Microbiol Biotechn Food Sci 7 (1): 27-31. https://doi.org/10.15414/jmbfs.2017.7.1.27-31

Schaefer C, Lecomte C, Clicq D, Merschaert A, Norrant E, Fotiadu F, 2013. On-line near infrared spectroscopy as a Process Analytical Technology (PAT) tool to control an industrial seeded API crystallization. J Pharmaceut Biomed Anal 83 (5): 194-201. https://doi.org/10.1016/j.jpba.2013.05.015

Schart JG, Wiel CCMVD, Lotz LAP, Smulders MJM, 2016. Opportunities for products of new plant breeding techniques. Trends Plant Sci 21(5): 438-449. https://doi.org/10.1016/j.tplants.2015.11.006

Saptoro A, Tadé MO, Vuthaluru H. 2012. A modified kennard-stone algorithm for optimal division of data for developing artificial neural network models: chemical product and process modeling. Chem Prod Process Model 7 (1): 1-14. https://doi.org/10.1515/1934-2659.1645

Taverniers I, Bockstaele EV, Loose MD, 2004. Cloned plasmid DNA fragments as calibrators for controlling GMOs: different real-time duplex quantitative PCR methods. Anal Bioanal Chem 378 (5): 1198-1207. https://doi.org/10.1007/s00216-003-2372-5

Waddell EE, Williams MR, Sigman ME, 2014. Progress toward the determination of correct classification rates in fire debris analysis II: utilizing soft independent modeling of class analogy (SIMCA). J Forensic Sci 59 (4): 927-935. https://doi.org/10.1111/1556-4029.12417

Wu Z, Ouyang G, Shi X, Ma Q, Wan G, Qiao Y, 2014. Absorption and quantitative characteristics of C-H bond and O-H bond of NIR. Opt Spectrosc 117 (5): 703-709. https://doi.org/10.1134/S0030400X1411023X

Yang X, Lei L, Jiang X, Wei W, Cai X, Su J, Feng W, Lu BR, 2017. Genetically engineered rice endogenous 5-enolpyruvoylshikimate-3-phosphate synthase (epsps) transgene alters phenology and fitness of crop-wild hybrid offspring. Sci Rep-UK 7 (1): 6834. https://doi.org/10.1038/s41598-017-07089-9

Xie L, Ying Y, Ying T, Yu H, Fu X, 2007. Discrimination of transgenic tomatoes based on visible/near-infrared spectra. Anal Chim Acta 584 (2): 379-384. https://doi.org/10.1016/j.aca.2006.11.071

Xu X, Li Y, Zhao H, Wen SY, Wang SQ, Huang J, Luo Y B, 2005. Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray. J Agr Food Chem 53 (10): 3789-3794. https://doi.org/10.1021/jf048368t

Yadav UP, Ayre BG, Bush DR, 2015. Transgenic approaches to altering carbon and nitrogen partitioning in whole plants: assessing the potential to improve crop yields and nutritional quality. Front Plant Sci 6: 275. https://doi.org/10.3389/fpls.2015.00275

Yu HY, Niu X Y, Lin H J, Ying Y B, Li BB, Pan XX, 2015. A feasibility study on on-line determination of rice wine composition by Vis-NIR spectroscopy and least-squares support vector machines. Food Chem 113 (1): 291-296. https://doi.org/10.1016/j.foodchem.2008.06.083

Zhang C, Liu F, Kong W, He Y, 2015. Application of visible and near-infrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves. Sensors 15 (7): 16576-16588. https://doi.org/10.3390/s150716576




DOI: 10.5424/sjar/2018162-11805