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

• Published in: Chemometrics and intelligent laboratory systems Vol. 225; p. 104575
• Main Authors: Yi, Cancan, Tuo, Shuai, Zhang, Lei, Xiao, Han
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.06.2022
• Subjects: Cluster analysis; Kernel entropy composition analysis; Material identification; THz spectrum; Transgenic cotton seeds; Cluster analysis; Kernel entropy composition analysis; Material identification; Transgenic cotton seeds; THz spectrum
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2022.104575

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 intra-class 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. •Rayleigh entropy can be utilized to select the most representative eigenvector during KECA.•An improved KECA clustering algorithm using angular structure is proposed in this paper for the optimal kernel parameter selection.•The researched method is successfully applied to Terahertz spectral recognition.