The feasibility of using fluorescence excitation-emission matrix(EEM) along with parallel factor analysis(PARAFAC) and nonnegative least squares(NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis(BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis(HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios(CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.
Samples of chromophoric dissolved organic matter （CDOM） in the East China Sea in autumn （October in 2011） were analyzed by excitation emission matrix （EEM） fluorescence spectroscopy combined with parallel factor analysis （PARAFAC）. Three terrestrial humic-like components （C1, C2 and C3） and one protein-like component （C4） were identified. Based on spatial dis- tributions, as well as relationships with salinity, the following assignments were made. The three humic-like components （CI, C2 and C3） showed conservative mixing behavior and came mainly from riverine input. The protein-like component （C4） was considered a combination of autochthonous production and terrestrial inputs and a biologically labile component. Path analysis of samples from the middle and bottom layers revealed that the causal effects on C1 were -78.46% for salinity, and -21.54% for apparent oxygen utilization （AOU）; those on C2 were -76.43% for salinity, and -23.57% for AOU; those on C3 were -70.49% for salinity, 7.01% for Chl-a, and -22.50% for AOU; those on C4 were -55.54% for salinity, 14.6% for Chl-a, and -29.86% for AOU in middle layer; and those on C4 were -57.37% for salinity, 29.02% for Chl-a, and -13.61% for AOU in bottom layer. Results indicated that CDOM in tile East China Sea was mainly affected by terrestrial inputs, and microbial ac- tivities also played a key role in biogeochemical processes of CDOM. The application of the EEM-PARAFAC model present- ed a unique opportunity to observe compositional changes in CDOM in the East China Sea. In addition, the humification index （HIX） suggested that CDOM from the East China Sea was less stable and stayed shorter in the environment.
BAI YingSU RongGuoYAN LiHongYAO PengSHI XiaoYongWANG XiuLin
