Dissolved Organic Matter Characterization Using Multiway Spectral

Published online October 27, 2006

Reproduced from Soil Science Society of America Journal. Published by Soil Science Society of America. All copyrights reserved.

Dissolved Organic Matter Characterization Using Multiway Spectral Decomposition of Fluorescence Landscapes Tsutomu Ohno* and Rasmus Bro characterizing its chemical nature (Leenheer and Croue´, 2003). Fluorescence spectroscopy is a rapid, nondestructive, and highly sensitive method that can provide information on the chemical properties of the fluorescing fraction of the organic matter. Fluorescence involves the absorption of photons by molecules, resulting in their promotion from the v 5 0 vibrational ground state to various vibrational excited states. In solutions, a rapid (10213 s) radiationless transfer of excess vibrational energy to the solvent results in the transition of all the excited molecules to the lowest excited vibrational level. The excited molecules then return to the ground state by the emission of a photon. Due to the loss of some portion of the energy initially absorbed through vibrational relaxation, the fluorescent emission is always of lower energy (greater wavelength) than the absorbed electromagnetic radiation. Fluorescence has been used to characterize DOM because of its ability to distinguish different classes of organic matter (Senesi, 1992; Alberts and Takacs, 2004). Early studies typically measured the fluorescence intensity in the emission mode, where excitation wavelength is fixed and emission is measured as its wavelength is varied. These emission fluorescence spectra for organic matter ligands are rather broad and not well defined, which leads them to be qualitative tools for organic matter characterization (Gosh and Schnitzer, 1980; Miano et al., 1988). Fluorescence spectroscopy for organic matter characterization has been advanced by the use of excitation– emission matrix (EEM) spectroscopy, which measures emission spectra across a range of excitation wavelengths, resulting in a landscape surface defined by the fluorescence intensity at pairs of excitation and emission wavelengths (Coble et al., 1990; Sierra et al., 2005). The EEM approach has been used to characterize DOM extracted from a variety of sources: leaf litter (Yang et al., 1994), crop residues (Ohno and Cronan, 1997; Merritt and Erich, 2003), humic substances (Mobed et al., 1996), and municipal wastewater treatment sludge (Westerhoff et al., 2001). Although the EEM spectroscopic approach has a greater density of spectral information than the traditional fluorescence approaches, the EEM landscape has been typically characterized by noting the locations of one or more peaks corresponding to maximum fluorescence intensities (“peak picking”). Two fluorophores frequently observed in DOM samples are located near the excitation–emission wavelength pairs 270|280/ 335|350 nm and also at 310|325/420|445 nm. These have been characterized as “protein-like” and “humiclike,” respectively (Coble et al., 1990; Merritt and Erich,

ABSTRACT Dissolved organic matter (DOM) plays an important role in many soil ecosystem functions. Multidimensional fluorescence spectroscopy of DOM with parallel factor analysis (PARAFAC) of the resulting spectral landscape has been successful in characterizing DOM from a variety of aquatic sources. This study was conducted to assess the multiway PARAFAC approach for quantitatively characterizing the fluorescent landscapes of DOM from aqueous extracts of soils and soil amendments. The DOM was extracted from plant biomass representative of crop, wetlands, and tree species; animal manures; and soils from controlled studies of cropping systems with known histories of organic amendments. The fluorescence landscape spectra were collected in the excitation range from 240 to 400 nm and emission range from 300 to 500 nm in 3-nm increments. The excitation and emission spectra modeled from the PARAFAC analysis showed that the plant biomass, animal manure, and soil DOM contained five fluorescing components: tryptophan-like (peak location at excitation 270 nm, emission 354 nm), tyrosine-like (273/309 nm), and three humic-substance-like components (.240/465 nm, 306/405 nm, and 315/447 nm). Principal component analysis of the concentration loading showed that the soil-derived DOM was very similar despite the different types and quantities of organic amendments incorporated in the different cropping systems. This study shows that PARAFAC analysis of multidimensional fluorescence spectra can model the chemical profile of terrestrial DOM in a chemically meaningful way. This represents a significant advance over current approaches to interpreting the complex DOM fluorescence spectra.

T

of plant biomass is the primary source material for organic matter formation in soils (Kaiser and Guggenberger, 2000; Kogel-Knabner, 2002). In addition, many agricultural management practices include the land spreading of animal manure for plant nutrient recycling and disposal, which can lead to significant increases in both dissolved and total organic matter levels in soils (Ohno et al., 2005). The labile nature of DOM results in this operational fraction being the most mobile and, presumably, the fraction most directly involved in reactions such as complexation with ions and sorption to soil surfaces (Zsolnay, 1996). Because of this reactivity, DOM may participate in critical soil functions such as soil weathering, nutrient bioavailability, trace metal mobility and toxicity, and C cycling in terrestrial ecosystems (Morel and Hering, 1993; Stevenson, 1994). Dissolved organic matter is a complex, heterogeneous mixture of molecules that poses analytical challenges in HE ANNUAL INPUT

T. Ohno, Dep. of Plant, Soil, and Environmental Sciences, Univ. of Maine, 5722 Deering Hall, Orono, ME 04469-5722; R. Bro, Royal Veterinary and Agricultural Univ., Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark. Received 6 Jan. 2006. *Corresponding author ([email protected]). Published in Soil Sci. Soc. Am. J. 70:2028–2037 (2006). Soil Chemistry doi:10.2136/sssaj2006.0005 ª Soil Science Society of America 677 S. Segoe Rd., Madison, WI 53711 USA

Abbreviations: DOM, dissolved organic matter; EEM, excitation– emission matrix; PARAFAC, parallel factor analysis.

2028


OHNO & BRO: DISSOLVED ORGANIC MATTER CHARACTERIZATION

2003). Chen et al. (2003) have operationally quantified the EEM spectra by delineating the EEM landscape into five regions and calculating the integrated volume under each region to characterize the DOM. The regions are characterized as being like aromatic protein (two regions), fulvic acid, microbial byproduct, and humic acid. While such ad hoc measures can provide useful indications, they do not take full advantage of the information inherent in the measured EEM spectra. Parallel factor analysis (PARAFAC), a multiway data analysis method, has been shown to decompose the suite of complex EEM landscapes into chemically meaningful spectral components (Bro, 1997; Andersen and Bro, 2003; Smilde et al., 2004). In essence, PARAFAC allows, for the first time, the direct measurement of a complex mixture with EEM fluorescence spectroscopy and separation into its basic constituents. This is not a trivial task, but the equivalent of chromatography. The use of such mathematical chromatography has shown its unique value in a number of highly different fields of environmental and ecological sciences. Application of PARAFAC to DOM in an estuary catchment revealed that five components could be identified and that the relative compositions of these components were dependent on the land use surrounding the sample location (Stedmon et al., 2003). Hall et al. (2005) reported that three components modeled aquatic DOM from the Mystic River watershed in Massachusetts. Cory and McKnight (2005) reported the presence of 13 components in DOM from diverse aquatic environments. The PARAFAC method has also been successfully used to characterize a variety of oils (heavy and light fuel oils, lubricating oils, crude oils, and spilled oils) with five fluorophores (Christensen et al., 2005). Increased understanding of the chemical nature of DOM derived from soils and soil amendments would help in establishing judicious management of these resources, which could potentially lead to enhanced soil quality and function. The objective of this study was to show that PARAFAC is a feasible method to characterize the fluorescence EEM of terrestrially derived DOM extracted from diverse sources including soils, animal manures, and aboveground plant biomass from species representative of agricultural, forested, and wetland ecosystems. MATERIALS AND METHODS Dissolved Organic Matter Sources Crop residue of field-grown wheat straw (Triticum aestivum L.) was obtained in the fall after grain harvest in Kansas. Corn residue (Zea mays L.) and soybean residue [Glycine max (L.) Merr.] were obtained from Iowa. Aboveground portions of field-grown oat (Avena sativa L.), hairy vetch (Vivia villosa L.), crimson clover (Trifolium incarnatum L.), red clover (Trifolium pratense L.), alfalfa (Medicago sativa L.), lupin (Lupinus albus L.), millet (Panicum miliaceum L.), and canola (Brassica napus L.) were obtained in a full flowering stage from a cropping system study in Maine. The plant samples were air dried and ground to pass a 1-mm sieve. The representative wetland species studied were arrowhead (Sagittaria latifolia Willd.), common cattail (Typha latifolia L.), pickerel weed (Pontederia cordata L.), royal fern (Osmunda

2029

regalis L.), and soft rush (Juncus effusus L.). The whole aboveground portions of the plants were sampled at maturity from the Orono Bog in Orono, ME. The representative tree leaf tissues were American beech (Fagus grandifolia Ehrh.), birch (Betula papyrifera Marsh.), red maple (Acer rubrum L.), red oak (Quercus rubra L.), and blue spruce (Picea pungens Engelm.). The leaves were sampled midsummer from about 2 to 3 m above the forest floor from the University of Maine Research Forest located in Stillwater, ME. The plant samples were air dried before being ground to pass a 1-mm sieve. The beef, dairy, poultry, and swine manure samples used were described by Griffin and Honeycutt (2000). The manure samples were air dried and sieved through a 2-mm sieve. Soils from four cropping system trials in Maine with 5 to 13 yr of treatment history were used in this study. All the soils were sampled in 2000 from the top 15-cm surface layer using a core sampler. Twenty core samples from each plot were composited and mixed to ensure homogeneity before sieving to remove large roots and gravel. The soils were then air dried for archival purposes. The Potato Ecosystem Study located in Presque Isle, ME, was started in 1990. The organic amendment consisted of a pea (Pisum sativum L.), hairy vetch, and oatgreen manure mixture, beef manure, and potato cull compost. The unamended control treatment used inorganic fertilizer. A full description of the study has been reported elsewhere (Gallandt et al., 1998). The Grass Fertility Study was located in Stillwater, ME, and initiated in 1995. The organic matter amendment consisted of liquid dairy manure, and the control received no nutrient applications. The study has been detailed in Griffin et al. (2002). The Liebman Rotation Study was located in Stillwater, ME, and initiated in 1990. Soils were sampled from the wheat plus red clover plots and the control plots consisted of wheat only. The amendment treatment received a small single application of composted dairy manure in 1997. This study has been described in detail by Davis and Liebman (2001) and Liebman and Gallandt (2002). The Porter Rotation Study was initiated in 1987 and the amended treatment consisted of pea, hairy vetch, and oat green manure and the control consisted of oat alone. The details of this study have been reported in Griffin and Porter (2004).

Dissolved Organic Matter Extractions Deionized–distilled water (DI-H2O) was used for all solutions and extractions. To obtain DOM from the plant materials and animal manures, 1.00 g of sample was extracted with 40 mL of DI-H2O for 16 h at 48C with periodic hand shaking by inversion of the extraction bottles. Suspensions were then centrifuged at 900 3 g for 25 min before vacuum filtering through 0.4-mm polycarbonate filters. Soil DOM was extracted by adding 10.0 mL of deionized H2O to 1.00 g of soil in a 15-mL centrifuge tube. The suspensions were shaken on an orbital shaker for 30 min at room temperature (22 6 18C), centrifuged at 900 3 g for 30 min, and filtered through 0.45-mm Acrodisk syringe filters. The extraction period was selected to minimize microbial DOM alteration during extraction (Zhou and Wong, 2000).

Chemical Analysis The concentration of total soluble C in the extracts was determined using a Shimadzu TOC 5000 analyzer (Shimadzu Scientific Instruments, Columbia, MD). Absorbances at 240 nm were obtained using an Agilent 8453 diode-array detector spectrophotometer (Aligent Technologies, Palo Alto, CA) and a 1-cm quartz cuvette. All DOM solutions were diluted with DI-H2O to set absorbance at 240 nm to 0.10 to minimize inner


2030

SOIL SCI. SOC. AM. J., VOL. 70, NOVEMBER–DECEMBER 2006

filtration effects. Fluorescence measurements were obtained using a Hitachi F-4500 spectrofluorometer (Hitachi, Tokyo) with the excitation range set from 240 to 400 nm and the emission range set from 300 to 500 nm in 3-nm increments. Instrumental parameters were: excitation and emission slits, 5 nm; response time, 8 s; and scan speed, 240 nm min21.

Excitation–Emission Matrix Data Preprocessing Several preprocessing steps were used to minimize the influence of scatter lines and other attributes of the EEM landscape that are due to the background solution matrix. An EEM that was collected from a control DI-H2O solution was subtracted from each sample EEM to remove the lower intensity Raman scatter effects (Christensen et al., 2005). The blank subtraction will not adequately remove the higher intensity Rayleigh scatter lines. Thus, the Rayleigh scatter lines and the region immediately adjacent to the region where the emission and excitation wavelengths are equal were removed by setting the fluorescence intensity values of these data points as missing. In addition, the EEM has a triangular-shaped region where the emission wavelength is less than the excitation wavelength (upper left-hand corner), which is physically not possible, and these data pairs were set to zero.

PARAFAC Spectral Decomposition The PARAFAC model (Harshman, 1970) can provide a chemically meaningful model of EEM fluorescence spectra (Smilde et al., 2004). For one fluorophore, the emission intensity at a specific wavelength, j, when excited at wavelength k, can ideally be approximated by

xjk 5 abj ck

[1]

where xjk is the intensity of the light emitted at emission wavelength j at excitation wavelength k, a is the concentration (in an arbitrary scale) of the analyte, bj is the relative emission emitted at wavelength j, and ck is the relative amount of light absorbed at the excitation wavelength k. If several analytes are present, then the intensity can be written as a function of these F analytes by simply summing the individual contributions:

Oa b c F

xjk 5

f 51

f

jf kf

[2]

where the relative emission of analyte f at emission j is bjf , the relative absorption at excitation k is ckf, and the concentration of analyte f is af . Equation [2] implies that the contribution to the emission from each analyte is independent of the contributions of the remaining analytes. For several samples, and aif being the concentration of the fth analyte in the ith sample, the model becomes

O a b c , i 5 1, … I; j 5 1, … J; k 5 1, … K F

xijk 5

if

jf kf

f 51

[3] This model of several samples is exactly the same as the PARAFAC model of a three-way array with typical elements xijk and hence the parameters aif, bjf , and ckf can be determined by fitting a PARAFAC model to the three-way data equaling the set of EEMs. The PARAFAC model is a low-rank trilinear model: low rank because each component (f ) is a rank 1 contribution in multilinear algebra; and trilinear because the model is linear in each of the three terms when the other terms are considered constant. Using the correct number of com-

ponents F, PARAFAC therefore directly provides estimates of the relative concentrations and excitation and emission spectra. The elements xijk can be held in a three-way array X of size I 3 J 3 K, where I is the number of samples, J the number of emission wavelengths, and K the number of excitation wavelengths. The PARAFAC modeling was conducted with MATLAB (Mathworks, 2005) using PLS_Toolbox version 3.5 (Eigenvector Research, 2005). A non-negativity constraint was applied to the parameters to allow only chemically relevant results because negative concentrations and fluorescence intensities are chemically impossible, assuming that quenching and inner filter effects are negligible. The PARAFAC models with two to eight components were fitted to the data to investigate the correct number of components.

RESULTS AND DISCUSSION Emission and Excitation–Emission Matrix Fluorescence Spectra The complex heterogeneous mixture of DOM makes the identification of specific compounds difficult, expensive, and time consuming, if possible at all. It is estimated that current analytical methods can resolve 1 to 10% of the DOM into specific compounds (Leenheer and Croue´, 2003). One approach to investigating the chemical nature of DOM has been through fluorescence spectroscopy to provide ensemble chemical properties of DOM molecules (Senesi, 1990), rather than separation and identification of specific molecules. Fluorescence spectroscopy has several intrinsic advantages for the characterization of DOM, such as its high sensitivity, which allows characterization of the DOM at low native concentrations (Leenheer and Croue´, 2003), and its sensitivity to the chemical environment of the fluorophore (Lakowicz, 1983). This chemical sensitivity enables the characterization of structural changes as the chemical environment changes, as well as the interaction of the fluorophore with its surrounding components. The broad nature of the emission spectra with no fine structure for DOM derived from wheat residue, poultry manure, and soil is shown in Fig. 1. The emission spectra of DOM have been used to provide a humification index (HIX) by quantifying the red shift of the emission spectra toward longer wavelengths with increasing humification (Cox et al., 2000; Ohno, 2002). The HIX index is calculated by dividing the emission intensity in the 435to 480-nm region by the emission intensity in the 300- to 345-nm region. The HIX values for the wheat residue, poultry manure, and soil DOM were 2.0, 2.7, and 4.6, respectively, indicating a greater level of DOM complexity and the condensed (aromatic) nature of the DOM for the poultry and soil DOM compared with the plant biomass DOM. The low HIX values for the wheat extract indicate that the plant biomass DOM solutions are probably enriched in polysaccharides and other weakly chromophoric biomolecules. The spectra shown in Fig. 1 are examples of a single emission scan from a fixed excitation wavelength of 254 nm. Full-scan EEM fluorescence spectroscopy collects emission spectra from a range of excitation wavelengths, allowing a complete profile of fluorescence intensity response along



both excitation and emission wavelength variables. A representative soil EEM from the beef manure amended soil of the Potato Ecosystem Study is shown in Fig. 2. The visual “peak-picking” method indicates that the soil EEM probably contains three fluorophores: the strongest peak has an excitation maximum of ,240 nm and emission maximum at |440 nm, a moderate peak at an excitation of |315 nm and emission at |440 nm, and a weak peak at an excitation of |275 nm and emission at |310 nm. The broad nature of both single-scan (Fig. 1) and fullscan (Fig. 2) fluorescence spectra and their general similarity in shape has made detailed, chemical interpretation of the spectra difficult. Chemometric methods use statistical approaches to extract useful information from chemical measurements and are useful in discerning relationships between chemical properties and composition, as well as identifying classes of objects with the given properties of interest (Beebe et al., 1998). Chemometric methods have been shown to be useful in classifying soils based on spectroscopic properties (Kalahne et al., 2000). The EEM landscape used in this study has high data density with 3618 excitation/emission fluorescence intensity combinations, which provides considerable chemical information; however, the size of the data makes the analysis and synthesis of that information difficult with traditional approaches and suggests the possibility of using chemometric methods to analyze the data set.

PARAFAC Modeling of Dissolved Organic Matter Parallel factor analysis is ideally suited for EEM analysis. Since the PARAFAC model is mathematically identified and coincides with the physical processes governing fluorescence, it is possible to decompose EEM data into chemically meaningful parameters (Smilde et al., 2004). Parallel factor analysis can take fully overlapping fluorescence data and decompose them into score and loading vectors that are estimates of relative concentrations of chemical analytes and excitation and emission spectra. Thus, the PARAFAC model can perform mathematical chromatography on mixture data, enabling identification and quantification of specific analytes. It is unlikely, however, that PARAFAC of DOM EEM data can resolve individual chemical compounds present in complex materials such as biomass and soils, since spectral characteristics of chemically similar compounds are almost identical (Christensen et al., 2005; Stedmon and Markager, 2005). The unlikelihood of resolving specific compounds by PARAFAC for DOM is not necessarily a major weakness. Resolved PARAFAC-derived components can be viewed as being “quasi-particles” in the sense defined by Sposito and Blaser (1992). The components are not real molecules, but rather they are mathematical constructs representing noninteracting ligands whose modeled parameters closely mimic the actual mixture of fluorescing compounds present in the DOM extracts. With biological processes often producing a suite of compounds with subtle structural differences and corresponding similarity in spectral features, PARAFAC modeling

2031

Fig. 1. Fluorescence emission spectra for dissolved organic matter that was extracted from wheat straw residue, poultry manure, and the Liebman E Rotation soil. Excitation wavelength was 254 nm (AU 5 arbitrary units).

is useful because it provides an objective description of the average behavior of the discrete, diverse classes of compounds present in samples. The major problem in using PARAFAC to represent sets of EEMs is that the low-rank trilinear model must be appropriate. Essentially this means that the fluorescence signals should be linear in concentration, additive and hence independent of each other, and that the spectral response of a given analyte (quasiparticle) must remain the same in all samples. There are situations where these assumptions are not valid. In particular, basic fluorescence theory would predict that PARAFAC is best used with dilute samples to reduce inner filter effects, quenching, etc. In practice though, PARAFAC has been used for directly measuring optically dense samples without significant problems because the tri-

Fig. 2. Full scan excitation–emission matrix fluorescence spectrum for beef manure-amended soil from the Potato Ecosystem Study.


2032


linear deviations were not severely significant. Formal mathematical conditions for when the PARAFAC model is expected to be mathematically identified can be found in the literature (Sidiropoulos and Bro, 2000). For example, a natural prerequisite for being able to separate contributions from different fluorophores is that their pure spectra must differ. Otherwise, it is not possible to determine whether the signal arises from one or the other. The PARAFAC models with two to eight components were computed for the data set containing DOM from plant biomass, animal manure, and soils. The determination of the correct number of components in the data set was assessed primarily by the core consistency diagnostic score, which should be close to 100% for appropriate models. The core consistency provides an estimate of how well the model captures trilinear information; if the consistency turns low, i.e., toward zero, it is a strong indication that the model is invalid (Bro and Kiers, 2003). The core consistency diagnostic scores were 97.5% for five components and 25.4% for six components, indicating that the five-component model provided the greatest spectral resolution of components and was probably the most appropriate model for the plant biomass and manure data set (Fig. 3). This conclusion was also substantiated by the overall appearance of the five-component model with low residuals and meaningful components. The five-component PARAFAC model explained 99.99% of the variability in the data set. The choice of the appropriate number of components to use in using PARAFAC for modeling EEM data is important and difficult and has been described in detail in several studies (Andersen and Bro, 2003; Stedmon and Markager, 2005). Essentially the number of components is chosen sufficiently high to describe the systematic variation in the data but avoiding too many components by excluding components that lead to too low core consistency values. The five components modeled are shown as their excitation and emission spectral loadings, which are the PARAFAC estimates of the pure excitation and emission spectra obtained from fitting the model to the EEM

Fig. 3. Core consistency diagnostic values for the non-negativityconstrained PARAFAC models having two to seven components for the plant biomass, animal manure, and soil DOM data set.

data (Fig. 4). That there are five components in the PARAFAC model implies that the EEM data can be fully characterized up to the noise by the contribution from five components. Each fluorophore is represented by one PARAFAC component and each component consists of a sample loading scores holding relative concentrations, an excitation loading representing the estimated excitation spectrum, and an emission loading representing the emission loading. The first three components have excitation and emission spectral loadings that have been assigned in the literature to humic-like and fulvic-like molecules (Baker, 2001; Yamashita and Tanoue, 2003; Hall et al., 2005). These three components had multiple excitation maxima and a single emission maximum, a pattern that has been reported for humicand fulvic-like DOM substances of aquatic estuary samples (Stedmon and Markager, 2005) and that has been observed in the EEM landscapes of soil and aquatic humic and fulvic acid substances (Sierra et al., 2005). Components 4 and 5 have spectral loadings that are very similar to tryptophan and tyrosine amino acids, respectively, which have been found frequently in DOM solutions and probably arise from DOM extracts that have proteins containing these amino acids (Yamashita and Tanoue, 2003; Stedmon and Markager, 2005).

Chemical Composition of Dissolved Organic Matter The relative concentrations of the identified fluorophores for the samples are estimated in the first loading of the PARAFAC model. The relative fractional distribution of the five components for the DOM extracted from soils, manures, crops, wetland plants, and tree leaves are shown in Fig. 5. It is important to recognize that the relative fraction distributions presented are based on their relative fluorescence signal contributions, rather than their true chemical concentration contribution. Expression of the distribution on a chemical concentration basis would require knowledge of the fluorescence quantum efficiencies of the individual components, which are currently unknown. The soil DOM was well modeled by the first three components, which accounted for 95% of the fluorescent signal. The low amount of the tryptophan-like component and the near absence of the tyrosine-like protein component indicate that these proteins or polypeptides do not remain in a water-soluble form after the plant biomass or animal manure is incorporated into the soil. They may be broken apart and either are immobilized microbially as a source of N, which is often limiting in soils, or could be strongly sorbed to soil surfaces. In addition, the low variability (average coefficient of variability was 5%) of the relative distribution shows that soil-derived DOM molecules had a high degree of self-similarity. Unlike the soil DOM set, all five component contents of the individual samples in the manure and plant biomass sets (Fig. 5B–5E) were highly variable (average coefficients of variation of 58% for manures and 35% for plant biomass) indicating that composition of the



2033

Fig. 4. Excitation and emission spectral loadings of the non-negativity-constrained five- component PARAFAC model of the plant biomass, animal manure, and soil DOM data set.

DOM varied appreciably among materials. The animal manure DOM distribution was fairly even across the five modeled components. The crop- and wetlandplants-derived DOM component profiles were similar, and they differed from the tree-leaves-derived DOM. Principal components analysis of concentration loadings was conducted to show the relationships of the com-

ponent contents amongst all the DOM sources (Fig. 6). The first two principal components captured 79% of the variability and two observations are easily visible. The strong self-similarity for the soil-derived DOM is shown with a tight clustering of the points, and the strong dissimilarity for the animal-manure-derived DOM is evident with the large distance between their points on


2034


Fig. 5. Relative percentage distribution of the contents for the five components in the PARAFAC model for the DOM extracted from: (A) soils, (B) animal manures, (C) crop species, (D) wetland plants, and (E) tree leaves.

the plot. The plant-biomass-derived DOM lies between soil and manure DOM in terms of similarity. The chemical composition of the plant-derived-DOM may be strongly influenced by factors such as plant age and environmental characteristics of the location (Bazzaz and Grace, 1997), which may influence its spectral character and chemical composition. The goal of this study was to demonstrate that PARAFAC can profile the DOM composition in a chemically meaningful way, which was not previously possible. While this study does not necessarily answer why the chemical profiles of the DOM sources differ, this ability to profile is important and is expected to assist in determining the relationship

between DOM structure and its spectral character in the future. The homogeneity of the soil-derived DOM in this study is surprising. The soils were specifically selected from four different cropping system studies to provide a contrast between amended (animal or green manure) soils that received an average of 415 to 8100 kg C ha21 more annually than their paired, unamended control soils for a 5- to 13-yr period. The two cropping systems that incorporated animal manure increased the total C content of the soils (Ohno et al., 2005). Thus, although animal manure amendment may increase the soil organic matter content, the results from this study suggest



2035

Fig. 6. Principal component (PC) analysis of the concentration scores for the five components in the PARAFAC model of the plant biomass, animal manure, and soil DOM data set.

that the chemical composition of the DOM in soil solution may not be altered by the addition of animal or green manures. There is a possibility that organic amendment alters the chemical composition of DOM, but the change occurs in a part of the DOM molecule that is not fluorescence active.

PARAFAC Dissolved Organic Matter Components Given the complexity of natural organic matter, the five components found in the plant biomass, animal manure, and soil DOM data sets may appear to be a low number of constituents. One explanation for this may be the relatively low sample numbers in this exploratory DOM data set (n 5 47). In a demonstration of how sample numbers can affect the number of components resolved in a data set, a study of an estuary and its catchment with 90 samples derived five components, while a follow-up study of the same estuary and catchment with 1276 samples derived eight components (Stedmon et al., 2003; Stedmon and Markager, 2005). Greater numbers of samples may make it possible to resolve fluorophores with similar spectral properties that cannot be separated with data containing fewer samples. To further investigate the similarity of the first three components to those reported for humic substances, the spectral loadings from these three components were compared with the loadings obtained for six terrestrial reference humic materials obtained from the International Humic Substances Society (IHSS) and analyzed by fluorescence EEM spectroscopy (He et al., 2006). The three-component PARAFAC model for the IHSS materials had a core consistency diagnostic of 99.1%, indicating an excellent model, and the excitation and emission spectral loadings are shown in Fig. 7. The components of the DOM were qualitatively similar to those of the IHSS components. Component 1 differed the most between the DOM and the IHSS set, with the excitation spectra of the DOM set containing a double peak and the emission peak of the IHSS set red-shifted

Fig. 7. Excitation and emission spectral loadings of the non-negativityconstrained three-component PARAFAC model for the reference humic substances.


2036


(peak maximum was shifted to longer wavelengths) by 30 nm (Fig. 4A and 7A). Component 2 matched well, with almost identical excitation spectra, and both emission spectra exhibited a broad peak (Fig. 4B and 7B). Component 3 also matched very well, with matching excitation double peaks and an emission peak of the IHSS set red-shifted by 20 nm (Fig. 4C and 7C). This study supports previous findings that waterextractable DOM of plant biomass (Ohno and Cronan, 1997; Merritt and Erich, 2003;) and soil (Ohno and First, 1998) have similar fluorescence spectral properties to humic substances. Fulvic and humic acids are terms traditionally used to describe fractions of soil organic matter based on their solubilities in acidic and basic extracts of soils, which have served as a convenient means to operationally fractionate soil organic matter into two discrete categories based on their physicochemical properties. One interpretation of this similarity reflects the new paradigm for the structure of soil humic substances (Sutton and Sposito, 2005). In this view, soil humic and fulvic acids are composed of low molecular weight components held together in associations through hydrophobic interactions and H bonding. In this archetype, biomolecules, which were explicitly excluded in the traditional definition of humic substances, are clearly important components of soil humic acids. The presence of “fulvic- and humic-like” components in these DOM data sets would be consistent with the new definition of humic substances advanced by Sutton and Sposito (2005).

CONCLUSIONS Fluorescence spectroscopy has been used as a rapid and sensitive method to characterize DOM, but has been limited to qualitative or semiquantitative spectral interpretation due to the broad nature of the fluorescence spectra without fine structure. In this study, the recently developed PARAFAC methodology was successfully used to decompose the meaningful components for DOM molecules isolated from plant biomass, animal manures, and soils. Five components could be decomposed from the DOM sample sets used in this study. Three of the components have been shown to be similar to components present in humic and fulvic acids. The other two components are similar to tryptophan- and tyrosin-like amino acids. This study demonstrates the ability of PARAFAC to yield information about the composition of DOM. Future work will extend this chemometric approach to characterize reactions of interest to soil chemistry such as complexation with metals and sorption to surfaces. ACKNOWLEDGMENTS This project was supported by National Research Initiative Competitive Grant no. 2003-35107-13628 from the USDA Cooperative State Research, Education, and Extension Service, and has also been supported by Hatch funds provided by the Maine Agricultural and Forest Experiment Station. This is Maine Agricultural and Forest Experiment Station Journal Publication no. 2882.

REFERENCES Alberts, J.J., and M. Takacs. 2004. Total luminescence spectra of IHSS standard and reference fulvic acids, humic acids and natural organic matter: Comparison of aquatic and terrestrial source terms. Org. Geochem. 35:243–256. Andersen, C.M., and R. Bro. 2003. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. J. Chemometrics 17:200–215. Baker, A. 2001. Fluorescence excitation-emission matrix characterization of some sewage-impacted rivers. Environ. Sci. Technol. 35:948–953. Bazzaz, F.A., and J. Grace. 1997. Plant resource allocation. Academic Press, New York. Beebe, K.R., R.J. Pell, and M.B. Seasholtz. 1998. Chemometrics: A practical guide. John Wiley & Sons, New York. Bro, R. 1997. PARAFAC: Tutorial and applications. Chemometrics Intell. Lab. Syst. 38:149–171. Bro, R., and H.A.L. Kiers. 2003. A new efficient method for determining the number of components in PARAFAC models. J. Chemometrics 17:274–286. Chen, W., P. Westerhoff, J.A. Leenheer, and K. Booksh. 2003. Fluorescence excitation- emission matrix regional integration to quantify spectra for dissolved organic matter. Environ. Sci. Technol. 37:5701–5710. Christensen, J.H., A.B. Hansen, J. Mortensen, and O. Andersen. 2005. Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis. Anal. Chem. 77:2210–2217. Coble, P.G., S.A. Green, N.V. Blough, and R.B. Gagosian. 1990. Characterisation of dissolved organic matter in the Black Sea by fluorescence spectroscopy. Nature 348:432–435. Cory, R.M., and D.M. McKnight. 2005. Fluorescence spectroscopy reveals ubiquitous presence of oxidized and reduced quinones in dissolved organic matter. Environ. Sci. Technol. 39:8142–8149. Cox, L., R. Celis, M.C. Hermosin, J. Cornejo, A. Zsolnay, and K. Zeller. 2000. Effect of organic amendments on herbicide sorption as related to the nature of the dissolved organic matter. Environ. Sci. Technol. 34:4600–4605. Davis, A.S., and M. Liebman. 2001. Nitrogen source influences wild mustard growth and competitive effect on sweet corn. Weed Sci. 49:558–566. Eigenvector Research. 2005. PLS_Toolbox version 3.5. Eigenvector Research, Manson, WA. Gallandt, E.R., E.B. Mallory, A.R. Alford, F.A. Drummond, E. Groden, M. Liebman, M. Marra, J.C. McBurnie, and G.A. Porter. 1998. Comparison of alternative pest and soil management strategies for Maine potato production systems. Am. J. Alternative Agric. 13:146–161. Gosh, K., and M. Schnitzer. 1980. Fluorescence excitation spectra of humic substances. Can. J. Soil Sci. 60:373–379. Griffin, T., E. Giberson, and M. Wiedenhoeft. 2002. Yield response of long-term mixed grassland swards and nutrient cycling under different nutrient sources and management regimes. Grass Forage Sci. 57:1–11. Griffin, T.S., and C.W. Honeycutt. 2000. Using growing degree days to predict nitrogen availability from livestock manures. Soil Sci. Soc. Am. J. 64:1876–1882. Griffin, T.S., and G.A. Porter. 2004. Altering soil carbon and nitrogen stocks in intensively tilled two-year rotations. Biol. Fertil. Soils 39:366–374. Hall, G.J., K.E. Clow, and J.E. Kenny. 2005. Estuarial fingerprinting thorough multidimensional fluorescence and multivariate analysis. Environ. Sci. Technol. 39:7560–7567. Harshman, R.A. 1970. Foundations of the PARAFAC procedure: Models and conditions for an ‘explanatory’ multi-modal factor analysis. UCLA Work. Pap. Phonetics 16:1–84. He, Z., T. Ohno, B.J. Cade-Menun, M.S. Erich, and C.W. Honeycutt. 2006. Spectral and chemical characterization of phosphates associated with humic substances. Soil Sci. Soc. Am. J. 70:1741–1751. Kaiser, K., and G. Guggenberger. 2000. The role of DOM sorption to mineral surfaces in the preservation of organic matter in soils. Org. Geochem. 31:711–725. Kalahne, R., M. Amin, J. Sobottka, and R. Sauerbrey. 2000. Methods of multivariate data analysis applied to the investigation of fen soils. Anal. Chim. Acta 420:205–216.



Kogel-Knabner, I. 2002. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol. Biochem. 34:139–162. Lakowicz, J.R. 1983. Principles of fluorescence spectroscopy. Plenum Press, New York. Leenheer, J.A., and J.-P. Croue´. 2003. Characterizing aquatic dissolved organic matter. Environ. Sci. Technol. 37:18A–26A. Liebman, M., and E.R. Gallandt. 2002. Differential responses to red clover residue and ammonium nitrate fertilizer by common bean and wild mustard. Weed Sci. 50:521–529. Mathworks. 2005. MATLAB version 7.1, release 14. Mathworks Inc., Natick, MA. Merritt, K.A., and M.S. Erich. 2003. Influence of organic matter decomposition on soluble carbon and its copper-binding capacity. J. Environ. Qual. 32:2122–2131. Miano, T.M., G. Sposito, and J.P. Martin. 1988. Fluorescence spectroscopy of humic substances. Soil Sci. Soc. Am. J. 52:1016–1019. Mobed, J.J., S.L. Hemmingsen, J.L. Autry, and L.B. McGown. 1996. Fluorescence characterization of IHSS humic substances: Total luminescence spectra with absorbance correction. Environ. Sci. Technol. 30:3061–3065. Morel, F., and J.G. Hering. 1993. Principles and applications of aquatic chemistry. John Wiley & Sons, New York. Ohno, T. 2002. Fluorescence inner-filtering correction for determining the humification index of dissolved organic matter. Environ. Sci. Technol. 36:742–746. Ohno, T., and C.S. Cronan. 1997. Comparative effects on ionic- and nonionic-resin purification treatments on the chemistry of dissolved organic matter. Int. J. Environ. Anal. Chem. 66:119–136. Ohno, T., and P.R. First. 1998. Assessment of the Folin and Ciocalteu’s method for determining soil phenolic carbon. J. Environ. Qual. 27:776–782. Ohno, T., T.S. Griffin, M. Liebman, and G.A. Porter. 2005. Green- and animal-manure based cropping systems effects on soil phosphorus and organic matter. Agric. Ecosyst. Environ. 105:625–634. Senesi, N. 1990. Molecular and quantitative aspects of the chemistry of fulvic acid and its interactions with metal ions and organic chemicals. Part II. The fluorescence spectroscopy approach. Anal. Chim. Acta 232:77–106. Senesi, N. 1992. Application of electron spin resonance and fluo-

2037

rescence spectroscopies to the study of soil humic substances. p. 13–52. In J. Kubat (ed.) Humic substances in soil, sediment, and water. John Wiley & Sons, New York. Sidiropoulos, N.D., and R. Bro. 2000. On the uniqueness of multilinear decomposition of n-way arrays. J. Chemometrics 14:229–239. Sierra, M.M.D., M. Giovanela, E. Parlante, and E.J. Soriano-Sierra. 2005. Fluorescence fingerprint of fulvic and humic acids from varied origins as viewed by single-scan and excitation/emission matrix techniques. Chemosphere 58:715–733. Smilde, A.K., R. Bro, and P. Geladi. 2004. Multi-way analysis. Applications in the chemical sciences. John Wiley & Sons, New York. Sposito, G., and P. Blaser. 1992. Revised quasiparticle model of protonation and metal complexation reactions. Soil Sci. Soc. Am. J. 56:1095–1099. Stedmon, C.A., and S. Markager. 2005. Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnol. Oceanogr. 50: 686–697. Stedmon, C.A., S. Markager, and R. Bro. 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Mar. Chem. 82:239–254. Stevenson, F.J. 1994. Humus chemistry: Genesis, composition, reactions. John Wiley & Sons, New York. Sutton, R., and G. Sposito. 2005. Molecular structure in soil humic substances: The new view. Environ. Sci. Technol. 39:9009–9015. Westerhoff, P., W. Chen, and M. Esparza. 2001. Fluorescence analysis of a standard fulvic acid and tertiary treated wastewater. J. Environ. Qual. 30:2037–2046. Yamashita, Y., and E. Tanoue. 2003. Chemical characterization of protein-like fluorophores in DOM in relation to aromatic amino acids. Mar. Chem. 82:255–271. Yang, A., G. Sposito, and T. Lloyd. 1994. Total luminescence spectroscopy of aqueous pine litter (O horizon) extracts: Organic ligands and their Al or Cu complexes. Geoderma 62:327–344. Zhou, L.X., and J.W.C. Wong. 2000. Microbial decomposition of dissolved organic matter and its control during a sorption experiment. J. Environ. Qual. 29:1852–1856. Zsolnay, A. 1996. Dissolved humus in soil waters. p. 171–223. In A. Piccolo (ed.) Humic substances in terrestrial ecosystems. Elsevier, Amsterdam.