In this paper, a new spectral variables selection method, induced mutation genetic algorithm (IMGA), is proposed for near-infrared (NIR) spectroscopy. Based on the idea of genetic algorithm (GA), the IMGA greatly simplifies the process of biological evolution, which not only inherits the advantages of global optimization of the GA, but also effectively improves the convergence speed. In this study, the IMGA is applied to the selection of characteristic spectral variables for green tea origin identification. After five times of genetic evolutions, 11 characteristic spectral variables are selected from 156 spectral variables. Based on the 11 characteristic spectral variables, the classification model is built by partial least squares (PLS), and both the sensitivity and specificity of classification model are raised to 1 for prediction set. The overall results indicate that the IMGA can be well applied to the selection of characteristic spectral variables and effectively improve the prediction accuracy and calculation speed of the near-infrared model.
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References
V. R. Sinija and H. N. Mishra, Food Bioproc. Technol., 4, 136–141 (2011).
X. G. Zhuang, L. L. Wang, Q. Chen, X. Y. Wu, and J. X. Fang, Sci. China Technol., 60, 84–90 (2017).
Q. S. Chen, J. W. Zhao, and H. Lin, Spectrochim. Acta, A, 72, 845–850 (2009).
D. Ono, T. Bamba, Y. Oku, T. Yonetani, and E. Fukusaki, J. Biosci. Bioeng., 112, 247–251 (2011).
Z. M. Guo, Q. S. Chen, L. P. Chen, W. Q. Huang, C. Zhang, and C. J. Zhao, Appl. Spectrosc., 65, 1062–1067 (2011).
Q. O. Yang, J. W. Zhao, Q. S. Chen, H. Lin, and Z. B. Sun, Anal. Methods, 4, 940–946 (2012).
M. Vohland, M. Ludwig, S. Thiele-Bruhn, and B. Ludwig, Geoderma,223–225, 88–96 (2014).
H. Jiang, G. H. Liu, C. L. Mei, S. Yu, X. H. Xiao, and Y. H. Ding, Anal. Bioanal. Chem., 404, 603–611 (2012).
B. M. Smith and P. J. Gemperline, Anal. Chim. Acta, 423, 167–177 (2000).
J. Y. Shi, X. P. Yin, X. B. Zou, J. W. Zhao, and S. G. Ju, Chin. Soc. Agric. Mach., 41, 99–103 (2010).
M. Ghasemi-Varnamkhasti, S. S. Mohtasebi, M. L. Rodriguez-Mendez, A. A. Gomes, M. C. U. Araújo, and R. K. H. Galvão, Talanta,89, 286–291 (2012).
X. G. Zhuang, L. L. Wang, X. Y. Wu, and J. X. Fang, J. Infrared Millimeter Waves,35, 200–205 (2016).
J. Jiang, R. J. Berry, H. W. Siesler, and Y. Ozaki, Anal. Chem., 74, 3555–3565 (2002).
R. Leardi and A. L. Gonzalez, Ch emometr. Intell. Lab., 41, 195–207 (1998).
Q. S. Chen, P. Jiang, and J. W. Zhao, Spectrochim. Acta, A, 76, 50–55 (2010).
J. H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 126–137 (1975).
L. Cséfalvayová, M. Pelikan, I. Kralj Cigić, J. Kolar, and M. Strlič, Talanta, 82, 1784–1790 (2010).
Y. F. Zhai, L. J. Cui, X. Zhou, Y. Gao, T. Fei, and W. X. Gao, Int. J. Remote Sens., 34, 2502–2518 (2013).
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Published in Zhurnal Prikladnoi Spektroskopii, Vol. 87, No. 2, pp. 245–251, March–April, 2020.
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Zhuang, X.G., Shi, X.S., Zhang, P.J. et al. New Induced Mutation Genetic Algorithm for Spectral Variables Selection in Near Infrared Spectroscopy. J Appl Spectrosc 87, 260–266 (2020). https://doi.org/10.1007/s10812-020-00994-4
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DOI: https://doi.org/10.1007/s10812-020-00994-4