首页|Improved Kernel Entropy Composition Analysis method for transgenic cotton seeds recognition based on Terahertz Spectroscopy

Improved Kernel Entropy Composition Analysis method for transgenic cotton seeds recognition based on Terahertz Spectroscopy

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  • NSTL
  • Elsevier
Terahertz (THz) Spectroscopy, featured by low energy, instantaneity and spectral fingerprint, is promising in material identification. Kernel Entropy Composition Analysis (KECA), different from the commonly used THz time-domain spectroscopy analysis methods of PCA and Kernel Principal Component Analysis (KPCA), focuses on decomposing eigenvalues and eigenvectors of a kernel matrix where its original spectral data are projected into a high-dimensional feature space, selecting eigenvectors as projection vectors which contribute most to Rayleigh entropy of original data and getting a new database. However, the parameters selection of kernel function in traditional KECA method have a significant effect on the accuracy of final analysis results. Aimed at this problem, this paper has proposed a new approach called improved KECA method for transgenic cotton seeds recognition based on Terahertz Spectroscopy. The improved KECA takes as the criterion function difference between intraclass and inter-class dispersion based on angular structure for Kernel parameter optimization, and selects kernel parameter with the maximum value of the criterion function as the best option. Then, the clustering method based on angular structure distance is used for the identification of different substance species. In order to test whether the proposed method is effective or not, this paper has applied the THz time-domain spectroscopy technology to detect the three transgenic cotton seeds of Xinluzhong6, Xinqiu107 and Yingmian8 respectively. And then the absorbance spectrum data of these three cotton seeds will be subject to clustering analysis through the proposed improved KECA method.

Transgenic cotton seedsTHz spectrumKernel entropy composition analysisCluster analysisMaterial identificationTransgenic cotton seedsTHz spectrumKernel entropy composition analysisCluster analysisMaterial identificationTIME-DOMAIN SPECTROSCOPYIDENTIFICATIONMIXTURESDETECT

Yi, Cancan、Tuo, Shuai、Zhang, Lei、Xiao, Han

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Wuhan Univ Sci & Technol

2022

10.1016/j.chemolab.2022.104575

Chemometrics and Intelligent Laboratory Systems

Chemometrics and Intelligent Laboratory Systems

EISCI
ISSN：0169-7439
年,卷(期)：2022.225
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