Abstract: In this study, a rapid method using Fourier transform infrared (FTIR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the identification of five species of bolete mushrooms. The original spectra of 272 samples from different species were preprocessed and optimized by multiplicative signal correction (MSC) and second derivative (2D). The classification results from the original and the optimal data matrixes were compared. Then the single and fused spectral data were analyzed by partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). The results showed that 1) compared with the original spectra, the preprocessing methods of 2D and MSC could optimize the spectral information and improve the classification accuracy; 2) base on FTIR, UV, low-level data fusion and mid-level data fusion data matrixes, respectively, the prediction accuracy of the PLS-DA models was 86.87%, 66.67%, 78.89% and 95.56%, and the prediction accuracy of the SVM models was 88.89%, 74.44%, 91.11% and 100.00%, respectively, indicating that mid-level data fusion was better than the other methods; 3) in terms of the prediction accuracy, the classification ability of the SVM model with mid-level data was better than that of the PLS-DA model with mid-level data. In conclusion, the SVM model based on mid-level data fusion represents a rapid method for the effective identification of different species of bolete mushrooms. It can provide an effective method for identification and quality control of edible mushrooms.

Key words: data fusion, boletes, species identification, ultraviolet spectrum, Fourier transform infrared spectrum