Spectroscopy and Spectral Analysis, Volume. 43, Issue 1, 239(2023)
Research on Quantitative Regression Method of IR Spectra of Organic Compounds Based on Ensemble Learning With Wavelength Selection
[1] [1] Zhao C H, Gao b, Zhang L J, et al. Infrared Physics and Technology, 2018, 95: 61.
[2] [2] Zhang Y, Sui B, Shen H, et al. Computers and Electronics in Agriculture, 2019, 160: 23.
[3] [3] Zhang X, Liu H, Yu S, et al. Geoderma, 2018, (320): 12.
[4] [4] Liu G, Gousseau Y, Xia G S. Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints, 2016 23rd International Conference on Pattern Recognition (ICPR), 2016, 3234.
[7] [7] Yun Y H, Li H D, Deng B C, et al. Trends in Analytical Chemistry, 2019, 113: 102.
[8] [8] Yang Y, Wang L, Wu Y, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2017, 182: 73.
[9] [9] Galvo R K, Fragoso W D. Chemometrics and Intelligent Laboratory Systems, 2008, 92(1): 83.
[10] [10] Geurts P, Ernst D, Wehenkel L. Machine Learning, 2006, 63(1): 3.
[11] [11] Liu X W, Zhu X Z, Li M M, et al. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2020, 42(5): 1191.
[12] [12] Diego I M, Munoz A, Moguerza J M. Machine Leaning, 2010, 78: 137.
[13] [13] Alam K M R, Siddique N, Adeli H. Neural Computing and Applications, 2020, 32(12): 8675.
Get Citation
Copy Citation Text
JU Wei, LU Chang-hua, ZHANG Yu-jun, CHEN Xiao-jing, JIANG Wei-wei. Research on Quantitative Regression Method of IR Spectra of Organic Compounds Based on Ensemble Learning With Wavelength Selection[J]. Spectroscopy and Spectral Analysis, 2023, 43(1): 239
Received: Oct. 13, 2021
Accepted: --
Published Online: Mar. 28, 2023
The Author Email: