In the present study, the feasibility of using wavelet analysis to extract the eigen spectra from the absorption spectra of phytoplankton for species detection was investigated. Thirteen absorption spectra taken from single species cultures, representing four divisions (Dinophyta, Bacillariophyta, Haptophyta, and Chlorophyta), six genus (Gymnodinium, Prorocentrurn, Skeletonerna, Guinardia, Phaeoc ystis, and Prasinoph yte ) and seven species ( Karenia mikirnotoi, Karenia brevis, Prorocentrum minimum, Skeletonema costatuma, Guinardia delicatula, Phaeocystis globosa, and Pyramimonas parkeae ), were used. First, the 1D wavelet analysis with five levels was applied to the thirteen absorption spectra, so each spectrum was decomposed with 5 levels. The 5th level component of low frequency corresponds to the background without information for species detection, and 1st and 2nd level component of high frequency is the random noise. The other levels (3rd to 5th) of high frequency are the useful information, and the sum of levels (3rd to 5th) of high frequency was retained as the eigen spectra for species deteetion Second, the clustering analysis was used to the eigen spectra for examining the performance of the wavelet analysis in extracting species information. The clustering results were compared with the known species class information, and the results show that the 13 absorption spectra are correctly classified at the level of division, genus and species. This means that the wavelet analysis has good performance in extracting the eigen spectra for species detection. However, the above results were obtained with only limited species, and the more species data are required to identify the extensive validity of the conclusion.