Excitation-emission Matrix Fluorescence Data Research Articles

Geographical origin traceability and authenticity detection of Chinese red wines based on excitation-emission matrix fluorescence spectroscopy and chemometric methods

Excitation–emission matrix fluorescence (EEMF) spectroscopy combined with chemometrics was developed for the geographical origin traceability and authenticity detection of Chinese red wines. Parallel factor analysis coupled with partial least squares-discriminant analysis (PARAFAC-PLS-DA), N-way partial least squares-discriminant analysis (N-PLS-DA) and unfolded partial least squares-discriminant analysis (U-PLS-DA) were used to identify the geographical origins and adulteration of red wines. Accuracy, sensitivity, specificity and precision were used to evaluate the performances of the discriminant models. The accuracy of PARAFAC-PLS-DA for training samples and prediction samples from different origins was 0.88 and 0.83, respectively. N-PLS-DA and U-PLS-DA, which used full EEMF data as model inputs, had the same excellent performance and both provided an accuracy of 1. With regard to the identification of adulterated samples, the accuracy of PARAFAC-PLS-DA for training samples and prediction samples was 0.71 and 0.83, respectively. By comparison, in both N-PLS-DA and U-PLS-DA models, only one prediction sample with the lowest adulteration level was misclassified. N-way partial least squares regression (N-PLSR) was successfully used to quantify adulteration levels for the first time, with Rc2 > 0.98, both RMSEC and RMSEP < 6%. This study showed that EEMF combined with chemometrics is an effective method for the geographical origin traceability and authenticity detection of Chinese red wines.

Multiplex Detection of Antibiotic Residues in Milk: Application of MCR-ALS on Excitation-Emission Matrix Fluorescence (EEMF) Data Sets.

The presence of antibiotics and their metabolites in milk and dairy products is a serious concern because of their harmful effects on human health. In the current study, a novel synergistic bimetallic nanocluster with gold and silver as an emission fluorescence probe was investigated for the simultaneous determination of tetracycline (TC), ampicillin (AMP), and sulfacetamide (SAC) antibiotics in the milk samples using excitation-emission matrix fluorescence (EEMF) spectroscopy. The multivariate curve resolution-alternating least squares (MCR-ALS) method was implemented to analyze augmented EEMF data sets to quantify the multicomponent systems in the presence of interferences with considerable spectral overlap. A pseudo-univariate calibration curve of the resolved emission spectra intensity against the concentration of the mentioned antibiotics was linear in the range of 5-5000 ng mL-1 for AMP and 50-5000 ng mL-1 for TC and SAC. The calculated values of the limit of detection ranged between 1.4 and 14.6 ng mL-1 with a relative standard deviation (RSD) of less than 4.9%. The obtained results show that the EEMF/MCR-ALS methodology using an emission fluorescence probe is a powerful tool for the simultaneous quantification of TC, AMP, and SAC in complex matrices with highly overlapped spectra.

Prediction of sulfur content in diesel fuel using fluorescence spectroscopy and a hybrid ant colony - Tabu Search algorithm with polynomial bases expansion

It is widely accepted that feature selection is an essential step in predictive modeling. There are several approaches to feature selection, from filter techniques to meta-heuristics wrapper methods. In this paper, we propose a compilation of tools to optimize the fitting of black-box linear models. The proposed AnTSbe algorithm combines Ant Colony Optimization and Tabu Search memory list for the selection of features and uses l1 and l2 regularization norms to fit the linear models. In addition, a polynomial combination of input features was introduced to further explore the information contained in the original data. As a case study, excitation-emission matrix fluorescence data were used as the primary measurements to predict total sulfur concentration in diesel fuel samples. The sample dataset was divided into S10 (less than 10 ​ppm of total sulfur), and S100 (mean sulfur content of 100 ​ppm) groups and local linear models were fit with AnTSbe. For the Diesel S100 local models, using only 5 out of the original 1467 fluorescence pairs, combined with bases expansion, we were able to satisfactorily predict total sulfur content in samples with MAPE of less than 4% and RMSE of 4.68 ​ppm, for the test subset. For the Diesel S10 local models, the use of 4 Ex/Em pairs was sufficient to predict sulfur content with MAPE 0.24%, and RMSE of 0.015 ​ppm, for the test subset. Our experimental results demonstrate that the proposed methodology was able to satisfactorily optimize the fitting of linear models to predict sulfur content in diesel fuel samples without need of chemical of physical pre-treatment, and was superior to classic PLS regression methods and also to our previous results with ant colony optimization studies in the same dataset. The proposed AnTSbe can be directly applied to data from other sources without need for adaptations.

Orthogonal signal correction assisted PLS analysis of EEMF spectroscopic data sets: fluorimetric analysis of polycyclic aromatic hydrocarbon mixtures

Excitation-emission matrix fluorescence (EEMF) spectroscopy was combined with orthogonal signal correction (OSC) assisted partial least square (PLS) analysis to achieve simultaneous quantification of aqueous mixtures of carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs). The application of OSC algorithm on EEMF data sets prior to PLS analysis was found to significantly improve the performance of calibration model towards the PAHs quantification.

Study of photodegradation kinetics of aflatoxins in cereals using trilinear component modeling of excitation-emission matrix fluorescence data.

In this work, a smart analytical strategy that combines excitation-emission matrix (EEM) fluorescence detection with alternating trilinear decomposition (ATLD) algorithm was developed for fast, on-line and interference-free study on the photodegradation kinetics of aflatoxin B1 (AFB1) and aflatoxin G1 (AFG1) in rice and wheat under UV-Vis light (λ = 250-500 nm) treatment. With the aid of prominent "second-order advantage" of ATLD method, pure fluorescence signals of two targeted analytes can be directly resolved out from heavily overlapping spectral environment and accurately quantified even in the presence of unknown matrix interferences. Cereal samples in kinetic processing of photodegradation were detected without complex pretreatment steps except for a simple extraction using methanol/water solution (4:1, v/v), which solves the problem facing varied matrix interferences in the case of on-line monitoring of aflatoxins. The kinetic signals of analytes of interest were directly extracted regardless of varied matrix backgrounds of various cereals. The kinetic curves and degradation speeds of AFB1 and AFG1 can be estimated by resolved quantitative data, optimal radiation conditions including 365 nm wavelength and 35 J m-2 density were discussed for high-efficiency detoxification control of aflatoxins in rice and wheat. This strategy was promising to be as an alternative tool for eco-friendly photodegradation kinetic study of mycotoxins or other hazards in complex foodstuff matrixes.

Impact of diverse background interferences on the alternating trilinear decomposition modeling of excitation-emission matrix fluorescence data acquired from different sample sources.

Alternating trilinear decomposition (ATLD) method enables the qualitative and quantitative analysis of excitation-emission matrix fluorescence (EEMF) data acquired from complex samples. However, the impact of diverse background interferences from different sample sources on the performances of ATLD method has never been lucubrated. In this work, simulated and real EEMF data sets from different sample sources with diverse background interferences were collected and subjected to ATLD analysis. The performances of ATLD modeling individual and global EEMF data sets were comprehensively compared in terms of the resolved spectral profiles and quantitative results. It was found that ATLD method can use the same set of calibration samples to resolve and quantify multiple components of interest in multiple complex systems with diverse background interferences, regardless of individual or global modeling. The results revealed that the qualitative and quantitative results provided by ATLD method were affected neither by diversity of background interferences nor by data merging as long as the acquired EEMF data sets conform to the trilinear component model. This property of ATLD method can enrich the "second-order advantage", i.e. the term "unknown interferences" in the concept of "second-order advantage" refers to not only constant background interferences but also diverse background interferences, which will be certain to further expand the practicality of ATLD method in complex sample analysis, especially in the field of fluorescence spectroscopy.

Optimizing Parallel Factor (PARAFAC) Assisted Excitation-Emission Matrix Fluorescence (EEMF) Spectroscopic Analysis of Multifluorophoric Mixtures.

Parallel factor (PARAFAC) analysis is the most commonly used mathematical technique to analyse the excitation-emission matrix fluorescence (EEMF) data sets of mutifluorophoric mixtures. PARAFAC essentially performs the mathematical chromatography on the EEMF data sets and helps in extracting pure excitation, pure emission and contribution profiles of each of the fluorophores without requiring any pre-separation step. The application of PARAFAC requires the initialisation of the spectral variables that is usually achieved by performing the singular value decomposition (SVD) analysis on EEMF data sets. One of the problem with SVD based initialisation is that it orthogonalises the data sets and makes the PARAFAC modelling of the EEMF data sets computationally challenging task that needs to be taken care. To address this issue, the present introduces an alternate approach for initialising the spectral variables for performing the PARAFAC analysis. The proposed approach essentially involve initialisation of the spectral variables with random numbers in a constraint manner. The proposed approach is found to provide the desired computational economy, robustness and analytical effectiveness to the PARAFAC analysis of EEMF data sets.

Introducing 'Simple Variable Selection (SVS) Approach' for Improving the Quantitative Accuracy of Chemometric Assisted Fluorimetric Estimations of Dilute Aqueous Mixtures.

Excitation emission matrix fluorescence (EEMF) spectroscopy is a multiparametric fluorescence technique where the fluorescence intensity of a fluorophore is a function of excitation wavelength, emission wavelength and its concentration. The manual analysis of large volume of highly correlated EEMF data sets towards developing a calibration model for quantifying each fluorophores present in multifluorophoric mixtures is a difficult and time-consuming task. Over the years, Partial least square (PLS) algorithm has found its application towards providing swift and efficient analyses of large volumes of highly correlated spectral data sets. The PLS assisted EEMF spectroscopy has been successfully used towards quantifying the fluorophores in multifluorophoric mixtures without involving any pre-separation. However, the accuracy and robustness of developed calibration model can be significantly improved provided PLS analysis is carried out on the analytically relevant EEMF spectral variables. In the present work, a variable selection method baptized as simple variable selection (SVS) approach is introduced that provides a simple and computationally economical means of identifying the useful spectral variables for subsequent PLS analysis. The proposed SVS approach is successfully validated by analyzing the complex EEMF data sets of multifluorophoric mixtures of consisting of multifluorophoric mixtures of biological relevance. The proposed approach is found to provide a simple, swift and efficient means for developing a robust PLS assisted EEMF spectroscopy based calibration model for simultaneous quantification of various fluorophores present in multifluorophoric mixtures.

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures.

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

Random Initialisation of the Spectral Variables: an Alternate Approach for Initiating Multivariate Curve Resolution Alternating Least Square (MCR-ALS) Analysis.

Multivariate curve resolution alternating least square (MCR-ALS) analysis is the most commonly used curve resolution technique. The MCR-ALS model is fitted using the alternate least square (ALS) algorithm that needs initialisation of either contribution profiles or spectral profiles of each of the factor. The contribution profiles can be initialised using the evolve factor analysis; however, in principle, this approach requires that data must belong to the sequential process. The initialisation of the spectral profiles are usually carried out using the pure variable approach such as SIMPLISMA algorithm, this approach demands that each factor must have the pure variables in the data sets. Despite these limitations, the existing approaches have been quite a successful for initiating the MCR-ALS analysis. However, the present work proposes an alternate approach for the initialisation of the spectral variables by generating the random variables in the limits spanned by the maxima and minima of each spectral variable of the data set. The proposed approach does not require that there must be pure variables for each component of the multicomponent system or the concentration direction must follow the sequential process. The proposed approach is successfully validated using the excitation-emission matrix fluorescence data sets acquired for certain fluorophores with significant spectral overlap. The calculated contribution and spectral profiles of these fluorophores are found to correlate well with the experimental results. In summary, the present work proposes an alternate way to initiate the MCR-ALS analysis.

Area correlation constraint for the MCR−ALS quantification of cholesterol using EEM fluorescence data: A new approach

This work demonstrates the use of a new additional constraint for the Multivariate Curve Resolution−Alternating Least Squares (MCR−ALS) algorithm called “area correlation constraint”, introduced to build calibration models for Excitation Emission Matrix (EEM) data. We propose the application of area correlation constraint MCR−ALS for the quantification of cholesterol using a simulated data set and an experimental data system (cholesterol in a ternary mixture). This new constraint includes pseudo-univariate local regressions using the area of resolved profiles against reference values during the alternating least squares optimization, to provide directly accurate quantifications of a specific analyte in concentration units. In the two datasets investigated in this work, the new constraint retrieved correctly the analyte and interference spectral profiles and performed accurate estimations of cholesterol concentrations in test samples. This the first study using the proposed area constraint using EEM measurements. This new constraint approach emerges as a new possibility to be tested in general cases of second-order multivariate calibration data in the presence of unknown interferents or in more involved higher order calibration cases.

Ozonation effects on emerging micropollutants and effluent organic matter in wastewater: characterization using changes of three-dimensional HP-SEC and EEM fluorescence data.

The degradation of effluent organic matter (EfOM) in a municipal wastewater treated by ozonation was characterized using the methods of high-performance size-exclusion chromatography (HP-SEC) and excitation/emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC). The removal of 40 diverse trace-level contaminants of emerging concern (CEC) present in the wastewater was determined as well. Ozonation caused a rapid decrease of the absorbance and fluorescence of the wastewater, which was associated primarily with the oxidation of high and low apparent molecular weight (AMW) EfOM fractions. PARAFAC analysis also showed that components C1 and C2 decreased prominently in these conditions. The EfOM fraction of intermediate molecular weight ascribable to a terrestrial humic-like component (C3) tended to be less reactive toward ozone. Relative changes of EEM fluorescence were quantified using F max values of PARAFAC-identified components (∆F/F 0max). Unambiguous relationships between ∆F/F 0max values and the extent of the degradation of the examined CECs (∆C/C0) were established. This allowed correlating main parameters of the ∆C/C0 vs. ∆F/F 0max relationships with the rates of oxidation of these CECs. The results demonstrate the potential of online measurements of EEM fluorescence for quantitating effects of ozonation on EfOM and micropollutants in wastewater effluents.

Simultaneous determination of umbelliferone and scopoletin in Tibetan medicine Saussurea laniceps and traditional Chinese medicine Radix angelicae pubescentis using excitation-emission matrix fluorescence coupled with second-order calibration method

A chemometrics-assisted excitation-emission matrix (EEM) fluorescence method is presented for simultaneous determination of umbelliferone and scopoletin in Tibetan medicine Saussurea laniceps (SL) and traditional Chinese medicine Radix angelicae pubescentis (RAP). Using the strategy of combining EEM fluorescence data with second-order calibration method based on the alternating trilinear decomposition (ATLD) algorithm, the simultaneous quantification of umbelliferone and scopoletin in the two different complex systems was achieved successfully, even in the presence of potential interferents. The pretreatment is simple due to the “second-order advantage” and the use of “mathematical separation” instead of awkward “physical or chemical separation”. Satisfactory results have been achieved with the limits of detection (LODs) of umbelliferone and scopoletin being 0.06ngmL−1 and 0.16ngmL−1, respectively. The average spike recoveries of umbelliferone and scopoletin are 98.8±4.3% and 102.5±3.3%, respectively. Besides, HPLC-DAD method was used to further validate the presented strategy, and t-test indicates that prediction results of the two methods have no significant differences. Satisfactory experimental results imply that our method is fast, low-cost and sensitive when compared with HPLC-DAD method.

Understanding the effect of calibration set design for the application of MCR-ALS analysis on excitation–emission matrix fluorescence (EEMF) data sets under commonly used non-negativity constraints

In this work, by analysing excitation–emission matrix fluorescence (EEMF) spectroscopic data sets with multivariate curve-resolution alternating least square (MCR-ALS) analysis under the commonly used mathematical constraint of non-negativity, an attempt was made to understand how the outcome of curve resolution technique can be influenced by the design of calibration sets. MCR-ALS analysis with commonly used mathematical constraints has been successfully used to analyse multi-component systems having significant spectral overlap. However, the present work showed that even for a simple system where the components do not have spectral overlap with each other, the MCR-ALS technique can resolve the spectra only if the calibration set is suitably designed. To carry out this study, three fluorophores—benzo[a]pyrene, perylene, and pyrene—having no spectral overlap with each other, were chosen. Selection of such fluorophores would enable the observation of fluorescence signatures of one fluorophore if it appears in the MCR-ALS retrieved profile of other fluorophores. Ten calibration sets with different approaches were created. EEMF data sets acquired for these calibration sets were subjected to MCR-ALS analysis. With most of the calibration sets, it was difficult to retrieve the pure EEMF spectra of all three fluorophores even if they did not have spectral overlap with each other. This work also evolved certain criterion for creating the calibration set which would enable the retrieval of pure EEMF spectra of all fluorophores of a multifluorophoric mixture using curve resolution technique. The present work clearly shows that pure spectral profiles of all the fluorophores under commonly used mathematical constraints can only be retrieved provided curve resolution analysis is performed on a suitably designed calibration set.

The use of EEM fluorescence data and OPLS/UPLS-DA algorithm to discriminate between normal and cancer cell lines: a feasibility study

Excitation emission matrix (EEM) fluorescence spectroscopy combined with the OPLS method has been investigated as a promising tool to discriminate between normal and cancer cell lines in two datasets: (i) using several types of normal and cancer cells (including 3T3, ARPE, HEK, HepG2, HeLa, HT-29 and 786-0 cells); (ii) considering the expression of matrix metalloproteinase-2 and -9 (MMP-2 and MMP-9) in suspensions of HEK and 786-0 cell lines. Partial Least Squares-Discriminant Analysis (PLS-DA) using the score matrix from PARAFAC (Parallel Factor Analysis), UPLS-DA (Unfolded Partial Least Squares with Discriminant Analysis) and orthogonal projection to latent structures (OPLS) were used as the bases for the discrimination models. UPLS-DA presented relevant performance for cancer cells in both datasets, with 100% and 66.7% correct prediction for first and second cases, respectively, and poor discrimination relative to normal cells in the first dataset (25%). By using the OPLS, we achieved 75% correct prediction for normal cells and maintained 100% concordance for cancer objects. On applying OPLS to the second dataset, we obtained 100% correct prediction in both classes (normal and cancer) for calibration and prediction sets. These results suggest that EEM fluorescence spectroscopy combined with chemometrics could be used as a clinical tool for cancer cell detection based on intrinsic biomolecular signatures.

Characterization of biochar-derived dissolved organic matter using UV–visible absorption and excitation–emission fluorescence spectroscopies

In recent years, biochar has become of considerable interest for a variety of environmental applications. However, the feasibility of its application is entirely dependent on its physical and chemical properties, including the characteristics of biochar-derived dissolved organic matter (DOM). The goal of this study was to assess the use of optical analysis for the purpose of characterizing biochar-derived DOM. Three different biochars (slow pyrolysis birch and maple; fast pyrolysis maple) were produced and leached in distilled water over 17d. Samples were taken on days 3, 10, 13 and 17, filtered, and analyzed for DOC content. Samples were also subjected to optical analysis using UV–visible absorption and excitation–emission matrix (EEM) fluorescence spectroscopies. EEM fluorescence data were further analyzed using parallel factor analysis (PARAFAC). Absorbance and fluorescence results were combined and examined using principal component analysis (PCA). Significant differences in the water soluble organic carbon content were observed for all biochar types. The estimated aromaticity (SUVA254) and mean molecular weight (S275–295) of biochar-derived DOM were also found to differ based on biochar type. PARAFAC analysis identified three humic-like components and one protein-like component. Distinct DOM signatures were observed for each biochar type. Transformations in biochar DOM characteristics over time were also observed. The PCA showed a clear delineation in biochar types based on their optical properties. The results of this study indicate that optical analysis may provide valuable information regarding the characteristics of biochar-derived DOM.

Analysis of dilute aqueous multifluorophoric mixtures using excitation–emission matrix fluorescence (EEMF) and total synchronous fluorescence (TSF) spectroscopy: A comparative evaluation

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence (TSF) spectroscopy are two conceptually different fluorescence techniques that have been used to map the fluorescence responses of the fluorophores present in a multifluorophoric mixture. EEMF was introduced four decades back and most of the fluorimeters have the suitable computer program which allows the acquisition EEMF spectra. Recently introduced TSF spectroscopy has been shown to possess good application potential in analytical fluorimetry and has started attracting the attention of analytical chemists. TSF data structure, however, is intrinsically different from EEMF data structure and a better understanding of TSF data structure is crucial to utilising its application potential. In the present work, a comprehensive comparative study between EEMF and TSF spectroscopic data set was performed by taking aqueous mixtures containing low concentrations of benzo[a]pyrene, chrysene, and pyrene as test case. The EEMF and TSF data structures were clearly explained by taking pyrene as an example. The effects of Rayleigh and Raman scattering on the quality of EEMF and TSF data sets were studied. EEMF and TSF data sets of dilute aqueous mixtures of benzo[a]pyrene, chrysene, and pyrene were subjected to three chemometric techniques PARAFAC, N-PLS, and MCR-ALS analysis. TSF data set in particular was found to be highly attuned to MCR-ALS analysis. Obtained results of chemometric analyses on EEMF and TSF data sets show that TSF data of dilute aqueous mixtures provides more accurate spectral and concentration information than EEMF data sets. Therefore, TSF spectroscopy could be considered as an alternate to the EEMF for the analyses of dilute multifluorophoric mixtures.

Simultaneous determination of plant growth regulators in environmental samples using chemometrics-assisted excitation–emission matrix fluorescence: Experimental study on the prediction quality of second-order calibration method

In this work, with the purpose of developing an effective and inexpensive method, excitation–emission matrix fluorescence data and second-order calibration method based on the self-weighted alternating trilinear decomposition (SWATLD) algorithm were combined for simultaneous determination of 2-naphthoxyacetic acid (NOA) and 1-naphthaleneacetic acid methyl ester (NAAME) in environmental samples, i.e. soil and sewage samples. In order to investigate the prediction quality of the proposed method, different strategies, such as taking spectroscopic measurements in the presence of different matrix interferents and at different fluorescence spectrophotometers, were introduced to build calibration models and comparisons among them were done subsequently. The root-mean-square error of prediction and t-test were used to compare different SWATLD-based calibration models. The limits of detection obtained for NOA and NAAME were 0.36–0.95ngmL−1 and 1.32–2.69ngmL−1, respectively, for different models. Such a chemometrics-based protocol may possess great potential to be extended as a promising alternative for more practical applications in environment monitoring and for the design of small intelligent and field-portable analytical instruments that rely on statistical discrimination, not complete instrumental separation, of the target analytes even in the presence of unknown and uncalibrated interferences.

Effects of Watershed History on Dissolved Organic Matter Characteristics in Headwater Streams

Dissolved organic matter (DOM) is recognized as a major component in the global carbon cycle and is an important driver in aquatic ecosystem function. Climate, land use, and forest cover changes all impact stream DOM and alter biogeochemical cycles in terrestrial environments. We determined the temporal variation in DOM quantity and quality in headwater streams at a reference watershed (REF), a watershed clear-cut 30 years ago (CC), and a watershed converted to a white pine plantation 50 years ago (WP) at the US Forest Service, Coweeta Hydrologic Laboratory, in the Nantahala Mountains of western North Carolina, USA. Average stream dissolved organic carbon (DOC) concentrations in CC or WP were 60 and 80% of those in REF, respectively. Stream DOM composition showed that the difference was mainly due to changes in humic-like components in chromophoric DOM. In addition, excitation–emission matrix fluorescence data with parallel factor analysis indicate that although the concentration of protein-like components did not differ significantly among watersheds, their relative abundance showed an enrichment in CC and WP compared to REF. The ratio of humic acid-type to fulvic acid-type components was highest and lowest at REF and WP, respectively. Our data suggest that forest ecosystem disturbance history affects the DOM quantity and quality in headwater streams over decades as a result of changes in watershed soil organic matter characteristics due to differences in organic matter inputs.

Solar radiation–enhanced dissolution of particulate organic matter from coastal marine sediments

We investigated the influence of solar radiation on the transfer of organic matter from the particulate to dissolved phase during resuspension of coastal sediments collected from seven sites across Florida Bay (organic carbon values ranged from 2% to 9% by weight). Sediments were resuspended in oligotrophic seawater for 48 h in 1‐liter quartz flasks in the dark and under simulated solar radiation (SunTest XLS+) at wet weight concentrations of 100 mg L−1 and 1 g L−1 (dry weights ranged from 27 to 630 mg L−1). There were little to no dissolved organic carbon (DOC) increases in dark resuspensions, but substantial DOC increases occurred in irradiated resuspensions. DOC levels increased 4 mg C L−1 in an irradiated 1 g L−1 suspension (dry weight 400 mg L−1) of an organic‐rich (7% organic carbon) sediment. At a particle load commonly found in coastal waters (dry weight 40 mg L−1), an irradiated suspension of the same organic‐rich sediment produced 1 mg C L−1. DOC increases in irradiated resuspensions were well‐correlated with particulate organic carbon (POC) added. Photodissolution of POC ranged from 6% to 15% at high sediment levels and 10% to 33% at low sediment levels. Parallel factor analysis modeling of excitation‐emission matrix fluorescence data (EEM PARAFAC) suggested the dissolved organic matter (DOM) produced during photodissolution included primarily humic‐like components and a less important input of protein‐like components. Principal component analysis (PCA) of EEM data revealed a marked similarity in the humic character of photodissolved DOM from organic‐rich sediments and the humic character of Florida Bay waters.