International Journal of Bioinformatics and Biomedical Engineering
Articles Information
International Journal of Bioinformatics and Biomedical Engineering, Vol.2, No.2, Mar. 2016, Pub. Date: Mar. 9, 2016
MW-PCA-LDA Applied to Vis-NIR Discriminate Analysis of Transgenic Sugarcane
Pages: 40-45 Views: 692 Downloads: 391
[01] Kaisheng Ma, Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
[02] Lijun Yao, Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
[03] Jiemei Chen, Department of Biological Engineering, Jinan University, Guangzhou, China.
[04] Tao Pan, Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
The moving-window waveband screening is applied to principal component and linear discriminate analyses (PCA-LDA). An integrated method (MW-PCA-LDA) for spectral pattern recognition is proposed, which is successfully employed for the non-destructive recognition of transgenic sugarcane leaves using visible (Vis) and near-infrared (NIR) diffuse reflectance spectroscopy. A Kennard–Stone-algorithm-based process of calibration, prediction and validation in consideration of uniformity and representative was performed to produce objective models. A total of 487 samples of sugarcane leaves in the elongation stage were collected from a planted field. These samples were composed of 306 transgenic samples containing both Bacillus thuringiensis and Bialaphos resistance genes, and 181 non-transgenic samples. Based on the MW-PCA-LDA method, the optimal waveband was 768 nm to 788 nm, and the corresponding validation recognition rates of transgenic and non-transgenic samples achieved 96.2% and 96.3%, respectively. The results show that Vis-NIR spectroscopy combined with the MW-PCA-LDA method can be used for accurate recognition of transgenic sugarcane leaves and provides a quick and convenient means of screening transgenic sugarcane breeding for large-scale agricultural production.
Visible-Near Infrared Spectroscopy, Transgenic Sugarcane Leaves, Spectral Discriminate Analysis, Moving-Window Waveband Screening, Principal Component and Linear Discriminate Analyses
[01] Zhang, X. Y., Yang, B. P., Zhang, S. Z. (2007) Research Progress on Transgenic Sugarcane. Molecular Plant Breedin, vol 5, pp. 155-159.
[02] Arencibia A., Vazquez R. I., Prieto D., Te11ez P., Carmona E. R., Coego A., Hemandez L., Riva G. A., Selman-Housein G. (1997) Transgenic sugarcane plants resistantto stem borerattack. Molecular Breeding, vol 3, pp. 247-255.
[03] Wang, G. Y., Fan, W. X., Chen, B. H., Zhag, J. W., Han, S. D. (2008) Application and Development of Detection Technology of Genetically Modified Foods (GMFs) I. Main Detection Technologies of GMFs and Their Characteristics. Food Science, vol 29, pp. 698-705.
[04] Chen, H. Z., Pan, T., Chen, J. M., Lu, Q. P. (2011) Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods. Chemometrics and Inteligentl Laboratory Systems, vol 107, pp. 139-146.
[05] Pan, T., Li, M. M., Chen, J. M. (2014) Selection Method of Quasi-Continuous Wavelength Combination with Applications to the Near-Infrared Spectroscopic Analysis of Soil Organic Matter. Applied Spectroscopy, vol 68, pp. 263-271.
[06] Pan, T., Wu, Z. T., Chen, H. Z. (2012) Waveband Optimization for Near-Infrared Spectroscopic Analysis of Total Nitrogen in Soil. Chinese Journal of Analytical Chemistry, vol 40, pp. 920-924.
[07] Liu, Z. Y., Liu, B., Pan, T., Yang, J. D. (2013) Determination of amino acid nitrogen in tuber mustard using near-infrared spectroscopy with waveband selection stability. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol 102, pp.269-274.
[08] Pan, T., Chen, Z. H., Chen, J. M., Liu, Z. Y. (2012) Near-Infrared Spectroscopy with Waveband Selection Stability for the Determination of COD in Sugar Refinery Wastewater. Analytical Methods, vol 4, pp.1046-1052.
[09] Xie, J., Pan, T., Chen, J. M., Chen, Z. H. (2010) Joint Optimization of Savitzky-Golay Smoothing Models and Partial Least Squares Factors for Near-infrared Spectroscopic Analysis of Serum Glucose. Chinese Journal of Analytical Chemistry, vol 38, pp.342-346.
[10] Pan, T., Liu, J. M., Chen, J. M., Zhang, G. P., Zhao, Y. (2013) Rapid determination of preliminary thalassaemia screening indicators based on near-infrared spectroscopy with wavelength selection stability. Analytical Methods, vol 5, pp. 4355-4362.
[11] Eriksson, L., Johansson, E., Kettaneh, W. N., Trygg, J., Wikström, C., Wold, S. (2006) Multi- and Megavariate Data Analysis Part I: Basic Principles and Applications. Umetrics Academy, Umea Sweden.
[12] Phil, W., Karl, N. (2001) Near-Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, USA.
[13] Lu, W. Z. (2007) Modern Near-infrared Spectroscopy Analytical Technology. China Petrochemical Press, Beijing.
[14] Liu, G. S., Guo, H. S., Pan, T. (2014) Vis-NIR Spectroscopic Pattern Recognition Combined with SG Smoothing Applied to Breed Screening of Transgenic Sugarcane. Spectroscopy Spectral Analysis, vol 34, pp.2701-2706.
[15] Long, X. L., Liu, G. S., Pan, T., Chen, J. M. (2014) Waveband selection of reagent-free determination for thalassemia screening indicators using Fourier transform infrared spectroscopy with attenuated total reflection. Journal of Biomedical Optics, vol 19, pp.087004-1-087004-11.
[16] Chen, J. M., Pan, T., Liu, G. S., Han, Y., Chen, D. X. (2014) Selection of Stable Equivalent Wavebands for Near-Infrared Spectroscopic Analysis of Total Nitrogen in Soil. Journal of Innovative Optical Health Sciences, vol 7, pp.1-9.
[17] Chen, J. M., Xiao, Q. Q., Pan, T., Yan, X., Wang, D. W., Yao, L. J. (2014) NIR Spectroscopy Combined with Stability and Equivalence MW-PLS Method Applied to Analysis of Hyperlipidemia Indexes. Spectroscopy Spectral Analysis, vol 34, pp. 2827-2832.
[18] Kennard, R. W., Stone, L. A. (1969) Computer aided design of experiments. Technometrics, vol 11, pp. 137–149.
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