Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis （PARAFAC） and CHEMTAX. The PARAFAC model was applied to fluo-rescence excitation-emission matrix （EEM） of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis （BDA）. Then the reference fluo-rescent component ratio matrix was constructed for CHEMTAX, and the EEM-PARAFAC-CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios （CDRs） when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacil-lariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.

Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares

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.

Changes of DOM and its correlation with internal nutrient release during cyanobacterial growth and decline in Lake Chaohu, China

The seasonal changes in dissolved organic matter(DOM),and its correlation with the release of internal nutrients during the annual cycle of cyanobacteria in the eutrophic Lake Chaohu,China,were investigated from four sampling periods between November 2020 and July 2021.The DOM fluorescence components were identified as protein-like C1,microbial humic-like C2,and terrestrial humic-like C3.The highest total fluorescence intensity(FT)of DOM in sediments during the incubation stage is due to the decomposition and degradation of cyanobacteria remains.The lowest humification of DOM and the highest proportion of C1 in waters during the initial cyanobacterial growth indicate that fresh algae are the main source.The highest molecular weight of DOM and FT of the C2 in sediments during cyanobacterial outbreaks indicate the concurrent deposition of undegraded cyanobacterial remains and microbial degradation.The components of DOM are affected mainly by the dissolved total phosphorus in waters,while the temperature drives the annual cycle of cyanobacteria.The decreasing C1 in sediments and increasing nutrients in waters from the cyanobacterial incubation to outbreak indicate that mineralization of algal organic matter contributes importantly to the release of internal nutrients,with the strongest release of phosphorus observed during the early growth of cyanobacteria.The humic-like C2 and C3 components could also affect the dynamics of internal phosphorus through the formation of organic colloids and organic–inorganic ligands.The results show that the degradation of DOM leads to nutrients release and thus supports the continuous growth of cyanobacteria in eutrophic Lake Chaohu.

Overlying water fluoride concentrations influence dissolved organic matter composition and migration from pore water in sediment via bacterial mechanisms

Fluoride(F^(-))is widespread in aquatic environments;however,it is not clear whether the fluctuation of F^(-)concentrations in overlying lake water affects the composition and migration of dissolved organic matter(DOM)from sediment.A case study was presented in Sand Lake,China,and an experiment was conducted to analyze the influence of different F^(-)concentrations in overlying water on DOM characteristics.Diffusion resulted in similarities in DOM components between overlying and pore waters,and bacterial activities and enzyme variation resulted in differences between them.Higher F^(-)concentrations in overlying water resulted in a higher pH of pore water,which favored the enrichment of protein-like substances.Higher F^(-)concentrations caused lower DOM concentrations and lower maximum fluorescence intensities(Fmax)of protein-like components in pore water.The F^(-)concentrations had significantly negative correlations with Shannon indexes(P<0.05).Thiobacillus influenced the migration of tyrosinelike substances by decreasing the pH of pore water.Trichococcus and Fusibacter altered the Fmax of protein-like,humic-like,and fulvic-like substances.The F^(-)concentrations affected the DOM composition and migration due to the response of functional bacterial communities,which were positively correlated with the relative abundance of Thiobacillus and negatively correlated with the relative abundances of Trichococcus and Fusibacter.The high F^(-)concentrations influenced the biosynthesis and degradation of protein-like substances by shifting the abundances of the relevant enzymes.The results of this study may provide ideas for investigating DOM cycling under the influence of F^(-),especially in lakes with fluctuations in F^(-)concentrations.

Comparison of three-dimensional fluorescence analysis methods for predicting formation of trihalomethanes and haloacetic acids

This work investigated the application of several fluorescence excitation–emission matrix analysis methods as natural organic matter（NOM） indicators for use in predicting the formation of trihalomethanes（THMs） and haloacetic acids（HAAs）. Waters from four different sources（two rivers and two lakes） were subjected to jar testing followed by 24 hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures： dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking,and parallel factor analysis of fluorescence spectra. Based on multi-linear modeling of THMs and HAAs, principle component（PC） scores resulted in the lowest mean squared prediction error of cross-folded test sets（THMs： 43.7（μg/L）^2, HAAs： 233.3（μg/L）^2）. Inclusion of principle components representative of protein-like material significantly decreased prediction error for both THMs and HAAs. Parallel factor analysis did not identify a protein-like component and resulted in prediction errors similar to traditional NOM surrogates as well as fluorescence peak picking. These results support the value of fluorescence excitation–emission matrix–principal component analysis as a suitable NOM indicator in predicting the formation of THMs and HAAs for the water sources studied.