Shipin Kexue (Jan 2024)
Identification of Perilla Based on Three-Dimensional Fluorescence Spectra Using Wavelet Packet Decomposition, Fisher Discriminant Analysis and Support Vector Machine
Abstract
In order to rapidly identify perilla species and avoid passing off, three-dimensional (3D) fluorescence spectral data of perilla from four regions in China were acquired. A feature selection strategy of fluorescence data based on wavelet packet decomposition fused with Fisher discriminant analysis (FDA) was proposed, and effective identification of the four species of perilla was implemented. First, the 3D fluorescence data were preprocessed by using Delaunay triangle interpolation to remove the adverse influence of Rayleigh scattering and Raman scattering; Savitzky-Golar (SG) convolutional smoothing was applied to smooth the data for the purpose of reducing the interference of noise. At the same time, the 3D fluorescence data were initially screened to remove emission wavelengths with fluorescence intensity less than 0.01. Second, the 3-layer sym4 wavelet packet decomposition of the emission spectrum corresponding to each excitation wavelength was performed, and the wavelet packet energy value of the lowest frequency band was calculated as the amount of spectral data characterization for each excitation wavelength. Third, FDA was used for discriminant analysis of these wavelet packet energy values, and the discrepancy information contained in them was fused to obtain the new variables generated by FDA; the first three FD variables with 99% cumulative discriminative power were selected as variables for the characterization of the discrepancy information of different species, and then a characterization strategy for the 3D fluorescence data was proposed. Finally, two pattern recognition algorithms, back propagation neural network (BPNN) and support vector machine (SVM), were used to analyze the characterization variables, and identification results were obtained with FDA + BPNN and FDA + SVM. A correct rate of 97.5% for the training set and 95% for the test set was observed with FDA + BPNN, and the correct rate obtained with FDA + SVM for both the training and test sets was 98.33%. These results showed that 3D fluorescence spectroscopy combined with wavelet packet decomposition, FDA and SVM algorithms could basically identify perilla from different regions, which will provide a basis for further research on perilla, such as quantitative detection of some active components.
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