[Fluorescence discrimination technique for phytoplankton based on the wavelet analysis]

Daubechies7 (db7) wavelet was selected to decompose the 3-D fluorescence spectra of 27 species of phytoplankton belonging to 22 generas of 6 divisions found in major lakes, then the scale vectors and time-series vectors were obtained as candidates for feature spectra. The third scale vector (Ca3) of db7 was chosen as feature spectra by Bayesian discriminant analysis, and the reference spectra were obtained via hierarchical cluster analysis to feature spectra. Based on the above data, a fluorescence discrimination technique was developed by multiple linear regression resolved by non-negative least squares. For single species algae cultures, the average correct discrimination ratio (CDR) was 98.6%, with the average relative content of 90.8% at division level. Furthermore, the noise immunity of reference spectra was tested by adding noise at different proportions. For the dominant division of laboratory mixed samples, the average CDR was 97.0%, with the average relative content of 67.7% at division level, and the average CDR of subdominant division was 90.7%, with the average relative content of 32.3%. The results showed that the technique is feasible to some extent.