Infrared Physics & Technology 63 (2014) 74–83

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Infrared Physics & Technology

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Mechanisms of browning development in aggregates of marine organicmatter formed under anoxic conditions: A study by mid-infrared andnear-infrared spectroscopy

1350-4495/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.infrared.2013.12.010

⇑ Corresponding author. Tel.: +39 06 61570454; fax: +39 06 61561906.E-mail addresses: mauro.mecozzi@isprambiente.it, mauromecozzi2004@

libero.it (M. Mecozzi).

Mauro Mecozzi a,⇑, Rita Acquistucci b, Laura Nisini a, Marcelo Enrique Conti c

a Laboratory of Chemometrics and Environmental Applications, ISPRA, Via di Castel Romano 100, 00128 Rome, Italyb Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Centro di Ricerca per gli Alimenti e la Nutrizione, Via Ardeatina 546, 00178 Rome, Italyc Department of Management, Sapienza University of Rome, via del Castro Laurenziano 9, 00161 Rome, Italy

h i g h l i g h t s

� Relationships between FTIR spectra of organic matter with its colour by spectral crosscorrelation analysis.� Relationships between FTNIR spectra of organic matter with its colour by spectral crosscorrelation analysis.� Hypothesis of non enzymatic (Maillard) reactions involved in browning colour development.� Hypothesis of enzymatic browning involved in colour development.� Significance of hydrogen bond in colour development evidenced by two-dimensional correlation spectroscopy.

a r t i c l e i n f o

Article history:Received 7 November 2013Available online 11 December 2013

Keywords:Marine organic matter aggregationMaillard browning reactionsEnzymatic browning reactionsColour IndicesSpectral Cross Correlation AnalysisTwo Dimensional Hetero CorrelationAnalysis

a b s t r a c t

In this paper we analyze some chemical aspects concerning the browning development associated to theaggregation of marine organic matter (MOM) occurring in anoxic conditions. Organic matter samplesobtained by the degradation of different algal samples were daily taken to follow the evolution of theaggregation process and the associated browning process. These samples were examined by Fouriertransform mid infrared (FTIR) and Fourier transform near infrared (FTNIR) spectroscopy and the colourchanges occurring during the above mentioned aggregation process were measured by means of ColourIndices (CIs). Spectral Cross Correlation Analysis (SCCA) was applied to correlate changes in CI values tothe structural changes of MOM observed by FTIR and FTNIR spectra which were also submitted to Two-Dimensional Hetero Correlation Analysis (2HDCORR). SCCA results showed that all biomolecules presentin MOM aggregates such as carbohydrates, proteins and lipids are involved in the browning development.In particular, SCCA results of algal mixtures suggest that the observed yellow–brown colour can be linkedto the development of non enzymatic (i.e. Maillard) browning reactions. SCCA results for MOM further-more suggest that aggregates coming from brown algae also showed evidence of browning related toenzymatic reactions. In the end 2HDCORR results indicate that hydrogen bond interactions among differ-ent molecules of MOM can play a significant role in the browning development.

In this study the combination of spectroscopic techniques such as FTIR and FTNIR with Colour Indicesmeasurements shows a peculiar ability to improve the knowledge of the complex mechanisms related tothe aggregation of marine organic matter and its colour development under anoxic conditions such likethose present in the marine environments at high depth.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

Marine organic matter (MOM) plays a fundamental role in thetransport cycles and in the bioavailability of micronutrients andpollutants, contributing to the traceability and to the dynamics

of water masses [1]. Several studies investigated its mechanismsof formation and its structural characteristics including naturaldissolved MOM (size up to 0.2 lm), particle MOM (size up to0.5 mm) and anomalous size aggregates such as marine snowand mucilages with dimension over 0.5 mm [2,3]. These studiesdescribe MOM as a complex mixture of carbohydrates, proteins,lipids and inorganic ions which stabilize the aggregate net [4,5].In fact, during the evolution of the aggregation process, severalreactions of complexation, polymerization, oxidation, hydrolysis

M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83 75

[6,7] take place in addition to the formation of micelle and supra-molecular structures. Further studies described comparable char-acteristics in the humification of terrestrial organic matter [6–8].

Over the last years, our group has investigated the mechanismsof formation of mucilages in Italian seas, describing the peculiarroles played by carbohydrates, proteins and lipids in this complexprocess [5,9]. During the first massive appearances of mucilage, inthe 80s and 90s, it was generally accepted that its main cause wasthe environmental pollution, although some surveys suggestedthat the natural factors and the hydrological conditions could havehad a stronger impact. In fact, anoxic conditions present along thewater column and depending on specific hydrologic and climaticfactors support the increase of these aggregates [10,11]. In theseanomalous conditions, the oxidative and degradation reactions ofMOM become slower than those caused by aggregation [9]. Furtherstudies confirmed the role of anoxia in the formation of mucilageas synthetic mucilages were easily reproduced in laboratory exper-iments [11,9,12].

These studies describe specific aspects, mechanisms and struc-tural characteristics of aggregates formed under anoxic conditions.The comprehension of the mechanisms involved in the browningreactions and mechanisms occurring during the aggregation re-mains however not completely investigated and the literature isstill poor. Fig. 1 (upper photo) shows some examples of colourchanges during the formation of anomalous size aggregates frommarine snow to different forms of mucilages such as clouds andstringers [13]. Fig. 1 (below) instead, shows the colour evolutionin lyophilized artificial mucilage samples.

Oceanographic studies made using absorption [14] and fluores-cence [15] spectroscopy, investigate MOM colour in order to studyphytoplankton distribution and to identify terrestrial and marineinputs of organic matter. For example, a recent study reportingthe spectral characteristics of chromophoric (i.e. coloured) dis-solved organic matter, shows that its visible emission arises fromterrestrial sources whereas the ultraviolet emission arises fromautochthonous and marine sources [16].

The ever increasing interest for MOM colour led to some studiesperformed in the so called Twilight zone (at ca. 100–800 m ofdepth). The result was that MOM colour could depend on the pres-ence of Maillard reaction (MR) products arising from the condensa-tion of carbohydrates with proteins [17].

Fig. 1. Examples of colour observed in different MOM aggregates. Marine snow (upper lemucilages from the alga Cistoseira barbata during the temporal evolution of MOM aggrelegend, the reader is referred to the web version of this article.)

MR is traditionally studied in food science and this typology ofreactions is described as strongly dependent on temperature(>70 �C), water activity (maximum at 0.6–0.7), pH conditions andchemical characteristics of the substrates [18,19]. On the oppositethesis concerning the humification of organic matter suggest thatMR can also occur at room temperature if supported by peculiarpH (between 6 and 8) and anoxic conditions [20,21]. These condi-tions, also present along the seawater column at depth higher than100 m, could support the presence of MR products within theuncharacterized and inertness organic matter of the marine envi-ronment [22].

We measured the Colour Indices (CIs), a measurement used infood science [23], with the aim of verifying structural characteris-tics in MOM aggregates formed under anoxic conditions which canbe related the presence of MR reactions and products. Therefore,CIs measurements and FTIR and FTNIR spectra of mucilages werejointly submitted to Spectral Cross Correlation Analysis [24] toinvestigate the structural and evolutionary changes potentially re-lated to colour changes. In addition, FTIR and FTNIR spectra werealso submitted to Two-Dimensional Hetero Correlation Analysis(2HDCORR), another powerful tool for the investigation of struc-tural changes in molecular systems [25].

The advantage of this approach is due to the specific ability ofthe vibrational spectroscopic techniques such as FTIR and FTNIR,which can give information on all the molecules present inMOM. On the other hand, fluorescence and absorption spectros-copy can investigate only chromophoric active molecules such asaromatic and polycondensated systems [26].

2. Methods and materials

2.1. Sampling of marine algae used for synthetic mucilage production

Four algal mixtures, made of different green red and brown al-gae, were used for artificial mucilage production. In any mixture,green and red or green and brown, the weight to weight ratioof the algal typology was close to 50%. Moreover, two brownalgal samples consisting of Padina pavonica (L.) Gail and Cystoseirabarbata respectively, were considered for mucilage production. Allthe algae were sampled along the coasts of Civitavecchia

ft photo), marine mucilage (upper right photo) and lyophilized samples of artificialgation (bottom photo). (For interpretation of the references to colour in this figure

76 M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83

(Tyrrhenian sea, Central Italy) on spring (May and June) and sum-mer (July and August) 2010. Brown algae and, in particular thesetwo species, were also selected in view of their aptitude as a tracemetal biomonitors in low contaminated marine ecosystems [27].

2.2. Formation of synthetic mucilage in laboratory conditions

Algal samples were placed into plastic containers filled withseawater taken from the same site of sampling within some hoursfrom sampling. Seawater was kept at 24 ± 1 �C without any type ofwater and air circulation as previously reported [5,9]. Organic mat-ter samples coming from the degradation of the algal biomass weredaily sampled from their first appearance, showing the beginningof mucilage formation and the beginning of browning develop-ment. First appearance of MOM aggregates generally started afterfive days from incubation of algal samples. The sample collectionwas terminated within 20 days approximately, when aggregatesshowed a marked brown colour. So, we collected between 10 and12 sub-samples of mucilage aggregates (about 2 mg each) for anyspecies (single brown alga or algal mixtures). Afterwards, theaggregates were lyophilized prior to FTIR, FTNIR and CI measure-ments. This pretreatment is necessary to eliminate the spectralinterferences of the residual seawater.

2.3. FTIR spectroscopic study of mucilage formation

FTIR spectra were collected in diffuse reflectance mode (DRIFT)using a Jasco single beam spectrophotometer mod. 410, equippedwith an EasyDiff (Pike Technology) accessory. Pellets of previouslydried KBr (200 mg) and lyophilized mucilage samples (2 mg) wereprepared. The spectra were collected between the 4000 and650 cm�1 range at spectral resolution 2 cm�1 and after 250 scans,using the cosine function as apodization technique. Any FTIR spec-trum was baseline corrected and submitted to an 11 point smooth-ing filter [28]. Spectra were saved as ASCII files for furtherapplications of SCCA and 2HDCORR. All the FTIR spectra were con-sidered for the chemometric study.

2.4. FTNIR spectroscopic study of mucilage formation

FTNIR spectra were also collected in DRIFT mode using a Jascosingle beam spectrophotometer mod. 420, equipped with an Easy-Diff (Pike Technology) accessory. Powder of lyophilized mucilagesamples (between 10 and 20 mg) were placed in a steel cup. Thespectra were collected in the 12,000 and 4000 cm�1 range at spec-tral resolution 4 cm�1 and after 500 scans, using the cosine func-tion as apodization. As FTIR spectra, FTNIR spectra were baselinecorrected and submitted to an 11 point smoothing filter. Like FTIRspectra, FTNIR spectra were also saved as ASCII files and used forthe chemometric study.

2.5. Diffuse visible reflectance spectroscopic (DVIS) measurements ofcolour development in mucilage formation

The red (+a�) yellow (+b�) and lightness (L�) colorimetric indi-ces were determined in the CIELAB colour system [23] using aChroma Meter CR 300 Minolta equipped with a pulsed xenonlamp and illuminant D65. For the determination of Tristimulusindices the lyophilized samples were placed in plastic cups(Fig. 1, bottom photo) and CI values were measured in differentportions of the surface so to reduce potential problems of nonhomogeneous samples and to warrant the accurate estimationof a�, b� and L�. Then the median value of these replicated mea-surements was determined for any sample. The brown indexwas calculated as the difference between 100 and the measuredL� value. Relative standard deviation is generally less than 2.0%

for the L� index, less than 5% for the b� index and less than 10%for the a� index. Colorimetric coefficients were saved as ASCII filesconstructing one spectral data matrix for each type of algal sam-ple used for mucilage production.

2.6. Application of SCCA to spectroscopic (FTIR and FTNIR) and DVISdata matrices

SCCA is a statistic tool used in this study to investigate the cor-relations between Tristimulus coefficients (i.e. the DVIS data set)and changes in the structural characteristics of mucilages (i.e. theFTIR or FTNIR data set) during the evolution of the aggregation pro-cess. SCCA derives from the multivariate technique called Statisti-cal Heterospectroscopy [24], describing the covariance betweentwo different spectroscopic measurements. The crosscorrelationmatrix C is determined by

C ¼ XYT=ðn� 1Þ ð1Þ

where X is the FTIR or the FTNIR spectral matrix and Y is the DVISmatrix. The X matrix has n�t dimension where n is the number ofFTIR and FTNIR samples (i.e. days of aggregate sampling) and twavelengths, while YT is the transposed DVIS data matrix withn � 3 dimension (i.e. the number of samples with the three Tristim-ulus coefficients).

The correct interpretation of spectral results when submitted toeach multivariate method requires the accurate selection of a pre-liminary standardization technique so to avoid potential false re-sults such as miscorrelations depending on greater amplitudes ofthe spectral intensities. After standardization, spectral absorptionsare independent on the sample amount and then the spectralintensities can describe correlation among bands with highaccuracy.

Spectral standardization needs an apart discussion. A widemethod of preprocessing spectra prior to any multivariate analysisis autoscaling, also called univariance scaling. If spectral data arestandardized by autoscaling, the C matrix becomes the correlationcoefficient matrix. Anyway, in high data dimension sets such asFTIR of FTNIR spectra, autoscaling has the major drawback to en-hance spectral noise and giving spectral artifacts like ghost peaks.To overcome this problem we applied the Pareto scaling methodwhich allows to improve the signal to noise ratio, enhancing thefine structure of spectra [29]. The selection of the Pareto prepro-cessing method can be considered as a valid support to the valida-tion and then the correct interpretation of spectral results. SCCAincluding Pareto scaling was performed by means of an in houseroutine written in Matlab (NATIK, WI, USA) language.

2.7. Two-dimensional hetero correlation spectroscopy (2DHCORR)

The 2D correlation spectroscopy in hetero mode uses two dif-ferent X and Y spectral sets (in this study FTIR and FTNIR) forstudying the dynamic evolution of any molecular system submit-ted to external factors or perturbations causing structure and/orcompositional changes [25]. By aligning spectra in columns, syn-chronous 2HDCORR (Syn) spectra are determined according to

Syn ¼ XYT=ðn� 1Þ ð2Þ

where n is the number of spectra in the X and Y matrices and YT isthe transposed Y matrix. Synchronous spectra describe the so calledin phase events which consist of structural changes in the molecularsystem under investigation occurring and following linearrelationships.

Asynchronous (Asyn) spectra are determined according to

Asyn ¼ XHYT=ðn� 1Þ ð3Þ

Table 1List of the FTIR band assignment. Bands reported with ‘‘�’’ are identified by means ofsecond derivative spectroscopy because poorly resolved in ordinary spectra.

FTIR

Wavenumber (cm�1) Functional group

3350–3450 OH carbohydrates proteins and polyphenols3200–3250 NH2 aminoacidic group3010–3020 CH alkene group3020–3060 CH of aromatic ring2850–2950 CH and CH2 aliphatic stretching group2100–2500 C@C conjugated and C„C1730–1740 C@O ester fatty acid group1700–1715 C@O fatty acid group1620–1670 C@O Amide I band1670� Beta turns Amide I band1650� Alpha helix Amide I band1635� Beta sheet Amide I band1610–1620 C@C unsaturated compounds1540–1550 CAN Amide II band1510� Lignin skeletal band (aromatic)1400–1460 Stretching AC@O inorganic carbonate1350–1440 CH and CH2 aliphatic bending group1240–1340 CAN Amide III band1120–1160 CAOAC polysaccharide1080 CAO carbohydrate1020 SiO2 silica897� Lignin skeletal band (aromatic)875 Bending AC@O inorganic carbonate690 CH out of plane aromatic band

M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83 77

where n, X and YT have the same meaning defined in the previousequation and H represents the Noda’s modification of the Hilbertorthogonalization operator. Asynchronous spectra describe the socalled out of phase events which consist of structural changes inthe molecular system occurring and following non linearrelationships.

Like SCCA, FTIR and FTNIR spectral matrices were also standard-ized by the Pareto method. 2HDCOOR was applied by means of inhouse routines written in Matlab language.

2.8. Chemical reagents

All the reagents were of analytical reagent grade and only ultra-pure MilliQ water was used for all the pretreatments of aggregatesamples.

3. Results and discussion

3.1. General characteristics of mucilage aggregates in FTIR and FTNIRspectra

The FTIR spectral characteristics of natural and mucilage aggre-gate formation have already been described in our previous studies[3,5,9]; we therefore report only the example of FTIR mucilagespectra during the evolution of the aggregation for the greenbrown algal mixture (Fig. 2) and for the brown alga Cystoseira bar-bata (Fig. 4). Table 1 reports the list of the identified bands. InFigs. 3 and 5 we report the corresponding FTNIR spectra of theabove samples in Figs. 2 and 4, while Table 2 reports the identifiedbands.

Looking at the FTIR and FTNIR couples of spectra (Figs. 2 and 3,Figs. 4 and 5), we could suppose some aspects of the role playedby carbohydrates and proteins in the browning development. Infact, FTIR spectra of Fig. 2 show the modified shapes of the poly-saccharide band between 1120 and 1180 cm�1 (i.e. the C–O–Cgroup) and of the protein bands between 1620 and 1680 cm�1

(i.e. the Amide I band) and between 1530 and 1560 cm�1 (i.e.the Amide II band). These structural changes occurr during theevolution of the aggregation process. As browning also evolvesduring the aggregation, it is evident that structural reactionsand polymerizations of carbohydrates and proteins are involved

Fig. 2. Examples of FTIR spectra of artificial mucilages obtained by the mixture of greereported from the first appearance of aggregates (upper plot) to the tenth (bottom) day. A

in browning development (see Fig. 1) but this information is inany case too generic because we do not retrieve details concern-ing the colour components and potential reactions involved. Thecontribution of FTNIR spectroscopy is even scarcer because themodifications of FTNIR spectra bands (example of Figs. 3 and 5)seem to be not very significant. We can conclude that the conven-tional examination of FTIR and FTNIR spectra describe only gener-ic relationships between the constituents of organic matter andthe browning colour only.

We can perform more accurate investigations of the relation-ships between the constituents of organic matter and the browningdevelopment by means of the covariance among FTIR and FTNIRspectra and colour components.

n and brown algae at constant time of sampling (i.e. two days each). Spectra aredditional spectra for the same algal samples are not reported for the sake of brevity.

Fig. 3. Examples of FTNIR spectra of artificial mucilages corresponding to the same samples of FTIR spectra in Fig. 2.

Fig. 4. Examples of FTIR spectra of artificial mucilages obtained by the brown alga Cystoseira barbata at constant time of sampling (i.e. two days each). Spectra are reportedfrom the second day (upper) to the tenth day (bottom) plot of aggregate appearance.

78 M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83

3.2. Results of SCCA

Fig. 6 (FTIR vs. DVIS) and Fig. 7 (FTNIR vs. DVIS) show twoexamples of the results obtained using SCCA. The L� index plots(i.e. the black plots), show a shape that reproduces the typical FTIRand FTNIR spectra of mucilages of algal mixtures (Figs. 6 and 7,upper plots) and of mucilages from brown algae (Figs. 6 and 7, bot-tom plots); we can see that all the FTIR and FTNIR bands, listed inTables 1 and 2, are present. The L� index results are common in allthe algal typologies used to produce synthetic mucilage. In factthey show that the structural changes of carbohydrates, proteinsand lipids occurring during the aggregate evolution, are stronglyinvolved in the reduction of lightness and, as a consequence inbrowning development observed in the aggregates.

The results of the yellow (b�) and red (a�) indices obtainedusing SCCA are completely different if we consider those (Figs. 6and 7) given by the L� index. In mucilages from algal mixtures

(Figs. 6 and 7, upper plots), the b� index shows some characteris-tics in common with the plots observed in the L� index. This isdue to the SCCA shapes which also reproduce the shape of FTIRand FTNIR spectra. This result shows that carbohydrates, proteinsand lipids also contribute to the development of the b� colourcomponent, even with a lower correlation if compared with thatof the L� index.

On the other hand, in the aggregates obtained from the brownalgae samples, b� has negligible correlations with the FTIR (Fig. 6,bottom plots) and the FTNIR (Fig. 7, bottom plot) bands. The sameconclusion can be extended to the a� index (red plots). In this case,the non significant correlations of FTIR and FTNIR spectra with a�

and b� indices can depend on the different algal composition ofthe red, brown and green samples. All these results show thatthe SCCA method is a reliable tool to study the potential relation-ships between colour and structural changes in biomolecules pres-ent and involved in the evolution of the aggregation process.

Fig. 5. Examples of FTNIR spectra of artificial mucilages corresponding to the same samples of FTIR spectra in Fig. 4.

Table 2List of the FTNIR band assignment.

Wavelength (cm�1) Functional group

11,100–11,140 CH third overtone10,050–10,300 OH second overtone8400–8800 CH second overtone6600–6900 OH first overtone6500–6600 NH amino acid first overtone5100–5200 Combination Amide I and OH4800–5000 Combination AOH and Amide II4200–4800 Combination NH, CH, CAC and OH

M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83 79

3.3. Colour development in MOM related to non enzymatic (i.e.Maillard) reactions

Cell breakage, organism excretion, phytoplankton contributionand river inputs determine the colour characteristics in dissolvedand colloidal marine organic matter [15,16,30]. However, onlyfew studies have investigated and discussed colour characteristicsin MOM aggregates occurring in anoxic conditions [17,22]. In foodproducts and humic substance, the browning development is oftenrelated to a cascade of non enzymatic reactions called Maillardreactions [31] which occur when carbonyl groups of reducing sug-ars condense with free amino acids, peptides and proteins. Theproducts are a variety of molecules ranging from low molecularweight compounds to high and insoluble compound called mela-noidins [32].

The great interest of MR in the humification processes lies in theprecursor molecules (sugars and amino acids) which are widelyspread in the terrestrial and aquatic environment [6–8,18]. Thesestudies describe the organic matter aggregation as a process basedon the polymerization and condensation of carbohydrates, amino-acids and proteins which form insoluble hydroaromatic and hydro-xy aromatic acids (i.e. melanoidins) characterized by a browncolour. In fact, melanoidins play an important role within thehumification process that leads to the formation of recalcitrantMOM assemblage present in anoxic environments [22].

If we consider the natural pH of seawater ranging from 8 to 8.5[33] and the correlations observed in all the mucilage typologiesamong the spectral bands of carbohydrates and proteins with theL� and b� indexes (Figs. 6 and 7, upper plots) and with the L� index

(Figs. 6 and 7, bottom plots), the hypothesis that browning can de-pend on MR products is reasonable. In fact, the SCCA results dem-onstrate that carbohydrates and proteins play a basic role inbrowning reactions (i.e. L� index), a role consisting of the conden-sation and polymerization reactions between reducing carbohy-drates with proteins and/or amino acids [18]. Table 3 reports theexample of colour modifications measured for mucilage from thered and brown algal mixtures, related to browning. Please notethat the decreasing L� value always corresponds to the increasing100-L� value (i.e. browning).

These reactions which occur in the advanced step of organicMOM aggregation, can show a high texture of samples (Fig. 1,upper plot), a texture which recent studies show to be correlatedto the organic matter characteristics of soils [34]. So the above cor-relations shown by SCCA results are in accordance with specificstudies involving the development of non enzymatic browning inhumic substance [35] and the yellow–grey colour observed inamorphous and non living organic matter [17,36].

3.4. Colour development in MOM enzymatic browning reactions

Enzymatic browning is another potential mechanism involvedin browning development of vegetal materials. It consists in abioticformations of humic substances depending on the so called poly-phenol model [37]. Enzymatic browning of vegetable materials isdue to the polyphenol oxidase supported by the phenolic contentof samples and specific chemico-physical conditions such as pH,temperature and oxygen availability [21,38]. As far as the marineenvironment concerns, an interesting case of enzymatic browningin the marine environment has been already observed in the syn-thesis of the polyphenolic proteins which determine the adhesivecharacteristic of mussel exudates [39].

In our samples, we cannot link the browning process to thepresence of enzymes and we need to verify the polyphenol contentin MOM aggregates as potential substrate of polyphenol oxidase.As brown algae have generally a significant polyphenol content[40], we considered the two MOM aggregates from brown algalsamples in which we verified the presence of polyphenols. Weused the second derivative FTIR spectroscopy, a well establishedmethod for verifying aromatic and lignin content in vegetal mate-rials [41]. Fig. 8 reports the second derivative FTIR spectra of the

Fig. 6. Results of SCCA applied to FTIR and DVIS measurements. Black, yellow and red SCCA spectra maps correspond to L (lightness), b� (yellow) and a� (red) indicesrespectively. Upper plot is related to mucilages from algal mixtures and it is highly comparable for all the other mucilage from algal mixture. Bottom plot is related tomucilage from the brown algal sample Padina pavonica, comparable with the other mucilage from Cystoseira barbata. (For interpretation of the references to colour in thisfigure legend, the reader is referred to the web version of this article.)

Fig. 7. Results of SCCA applied to FTNIR and DVIS measurements. The order of the plots and the colour of SCCA spectra maps are the same of Fig. 3. The arrows show thecorrelations of the band ranging between 5100 and 5200 cm�1 with all the CI indices. (For interpretation of the references to colour in this figure legend, the reader is referredto the web version of this article.)

80 M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83

synthetic mucilage from the brown algae Cystoseira barbata wherewe observed the specific bands of lignin and aromatic structures at897 and 1510 cm�1 [41]. We can find the same bands (data not re-ported for the sake of brevity) in the mucilage from Padinapavonica.

This information obtained by second derivative FTIR spectracould be considered as a qualitative information related to MOMcomposition without the contribution of the SCCA map of Fig. 6(upper plot). Conversely, when we consider SCCA results joint with

second derivative ones, we can suppose that the presence of poly-phenols reasonably supports the development of enzymaticbrowning reactions. In fact, the enzymatic browning has beenalready evidenced in the synthesis of polyphenolic proteins inmussels [39].

On the other hand the results of SCCA for mucilages producedfrom the red-brown and the green–brown algal mixtures shownegligible correlations among the b� index and the FTIR and FTNIRspectra (Figs. 6 and 7, upper plots). In this case, the second

Table 3CIELAB indices (L�, b� and a�) and brown (100-L�) index measured on aggregates fromthe red and brown algal mixtures, applied for SCCA applications. The values are themedian of three repeated measurements performed on each pellet. This is a casewhere we suppose MR presence.

L� b� a� 100-L

96.91 2.35 �0.26 3.09101.16 1.43 �0.85 �1.16

97.67 2.28 �0.53 2.3373.17 2.57 �0.42 26.8377.41 4.36 0.22 22.5975.74 3.68 0.43 24.2676.27 4.1 1.05 23.7377.58 2.3 �0.02 22.4279.23 0.32 �0.19 20.7777.25 2.3 �0.28 22.7578.17 2.87 �0.71 21.8374.27 2.18 �1.04 25.7381.4 4.68 �0.97 18.6

M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83 81

derivative FTIR spectra (data not reported) do not show aromaticand polyphenol structures (i.e. absence of the bands at 897 and1510 cm�1), not supporting enzymatic browning for green brownand red brown algal mixtures.

All results show that MR and enzymatic reactions can occur inthe browning development observed in the formation of amor-phous and non living MOM aggregates such as mucilages. Wecan also assume that the heterogeneous composition of MOM sup-port these two reactions not separately but together because ami-no acids, proteins, carbohydrates and polyphenols are naturalcomponents of the recalcitrant aggregates of organic matter[18,22].

3.5. Characteristics of 2HDCORR spectra

Fig. 9 reports two contour plots of artificial mucilage from thegreen and brown algal mixture and the brown alga samples respec-tively, resulting from the application of Two-Dimensional HeteroCorrelation Analysis to FTIR and FTNIR spectra. Though 2HDCORRmaps in mesh mode are generally used for detecting the sign ofthe correlation, we do not report them here because we can showthe sign of the correlation using the ‘‘ad hoc’’ colobars of the con-tour plots.

-0.00035

-0.0002

-0.00005

0.0001

0.00025

0.0004

2427503050335036503950

Wav

Abs

orba

nce

(A.U

.)

Fig. 8. Example of second derivative spectrum for the brown alga Padina pavonica. The arsamples from all the other algal mixtures.

Contour plots of Fig. 9 show positive crosspeaks for all theexamined samples, describing the aggregation of MOM as a processcharacterized by in phase events.

For the sake of brevity we do not report asynchronous mapsbecause they consist of noise essentially, characterized by verylow intensity and negligible crosspeaks. This result is any caserelevant because it underlines therefore that the whole aggrega-tion process is characterized only by synchronous reactionsoccurring simultaneously (i.e. in phase events) following linearrelationships [25].

Now, let us examine the results of synchronous maps, reportedin Fig. 9. All plots show intense crosspeaks close to 5100 cm�1 vs.3400, 1650, 1550 e 1150 cm�1 and in Table 2 we can see that theNIR band lies between 5100 and 5200 cm�1 e.g. to the interactionof hydroxyl groups with the Amide I band (i.e. the peptidic AC@Ogroup). This NIR 5100 cm�1 band is correlated to 1650, 1550 cm�1

(i.e. proteins) and 1150 cm�1 bands of carbohydrates and itdescribes inter and intra molecular hydrogen bond interactionsbetween carbohydrates and proteins [42]. We can point out thatthe FTNIR band between 5100 and 5200 cm�1 has intense corre-lation peaks with L� and b� and lower but significant with a� too(Fig. 6). These results suggest that hydrogen bond interactionsbetween AC@O and AOH groups also play an important role in sup-porting browning development, a role which is complementary tothe condensation reactions between carbohydrates and proteinswhich determine MR reactions. The importance of hydrogen bondinteractions in supporting colour characteristics of organic matterin environmental samples is a further contribution of NIR spectros-copy to describe the relationships between colour and chemicalcharacteristics of other environmental samples such as soils; in fact,NIR spectroscopy has described already the relationships among or-ganic matter contents and texture of samples with their NIR spectradetermining their colour characteristics [43,44]. In any case, the spe-cific role played by hydrogen bond interactions in the developmentof colour characteristics can be hardly inferred by means of FTNIRspectra (Figs. 3 and 5). In fact, SCCA results show the high correla-tions between the NIR band at 5100 cm�1 with L� index (Fig. 7, bothplots). Moreover, synchronous spectra of Fig. 9 also depict the rele-vance of this band describing hydrogen bond interactions that areconnected with all the biomolecules (carbohydrates, proteins andlipids) present in mucilages.

650950125015501850215050

enumber (cm-1)

1510 897

row show the presence of the typical polyphenolic bands, not observed for mucilage

Wavenumber (cm-1)

Wav

enum

ber (

cm-1

)

40005000600070008000900010000

1000

1500

2000

2500

3000

3500

4000 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Wavenumber (cm-1)

Wav

enum

ber (

cm-1

)

40005000600070008000900010000

1000

1500

2000

2500

3000

3500

4000 0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Fig. 9. Some examples of 2HDCORR synchronous spectra of artificial mucilage samples as contour plots. Upper plot is the map from the mixture of green and red algae,bottom plot is the map from the alga Padina pavonica. The colourbar on the right side shows the intensity of the correlation, here expressed in arbitrary unit due to theapplication of the Pareto scaling technique. The arrows show the correlation of the FTNIR band ranging between 5000 and 5200 cm�1 with the FTIR band of the Amide I,related to hydrogen bond interaction between carbohydrates and proteins. (For interpretation of the references to colour in this figure legend, the reader is referred to theweb version of this article.)

82 M. Mecozzi et al. / Infrared Physics & Technology 63 (2014) 74–83

4. Conclusion

This study is a first approach to investigate the chemical mech-anisms of browning in MOM aggregates by means of vibrationalspectroscopic techniques supported by multivariate statistic tech-niques such as SCCA and 2HDCORR instead of using the more com-mon visible and fluorescence spectroscopy. The results of SCCAshow the significant correlations between carbohydrates, proteinsand aromatic compounds with Colour Indices. In more details, car-bohydrates and proteins show significant correlations with thebrown (100-L�) and yellow (b�) indices potentially related to MRproducts, while phenolic molecules seem to be related to enzy-matic browning. In addition, in the mucilages from brown algaltypologies, MR and enzymatic browning seem to coexist. Eventu-ally we must stress out that, as evidenced of the HDCORR results,hydrogen bonds between carbohydrates and proteins also contrib-ute to the colour development in MOM. This role supports themore common role played by condensation and oxidation reac-tions in browning development.

Acknowledgments

This work had the financial support by Sapienza University ofRome, Research Project 2011 – C26A1135RW. The authors aregrateful to Dr. E. Sturchio for her kind availability to the use ofthe FTNIR spectrophotometer placed at the INAIL laboratory inRome. At last, the authors wish to thank two anonymous reviewersfor their suggestions and comments to the manuscript.

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