Bioresource Technology 110 (2012) 330–337

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Bioresource Technology

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Assessment of the maturity and biological parameters of compostproduced from dairy manure and rice chaff by excitation–emissionmatrix fluorescence spectroscopy

Wei Tian, Lingzhi Li, Fang Liu, Zhenhua Zhang, Guanghui Yu, Qirong Shen, Biao Shen ⇑Jiangsu Key Lab for Organic Solid Waste Utilization, Nanjing Agricultural University, Nanjing 210095, China

a r t i c l e i n f o

Article history:Received 29 October 2011Received in revised form 7 January 2012Accepted 14 January 2012Available online 26 January 2012

Keywords:CompostingMaturityExcitation–emission matrix (EEM)Fluorescence regional integration (FRI)analysisRibotype evolution

0960-8524/$ - see front matter � 2012 Elsevier Ltd. Adoi:10.1016/j.biortech.2012.01.067

⇑ Corresponding author. Tel./fax: +86 25 84395212E-mail address: shenbiao@njau.edu.cn (B. Shen).

a b s t r a c t

The assessment of maturity and biological parameters is important in the composting process. In thisstudy, excitation–emission matrix (EEM) fluorescence spectroscopy was applied to evaluate the maturityand biological parameters of compost produced from the co-composting of dairy and rice chaff. Theresults from a Pearson correlation analysis between traditional physico-chemical maturity indices andfluorescence regional integration (FRI) parameters showed that among the FRI parameters, PV,n/PIII,n

and PV,n were suitable for the assessment of compost maturity. Moreover, the FRI parameters could beused to evaluate biological parameters including the germination index (GI) and ribotype evolutionwhich indicate the bacterial community structure and dynamics. PIV,n was the most suitable indicatorfor revealing the community structure and dynamics during the composting process. Fluorescence spec-troscopy combined with the FRI analysis could be used as a sensitive and efficient tool for assessing com-post maturity and biological parameters.

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1. Introduction

The intensity and concentrated development of animal hus-bandry has provided great benefits to farmers in China in the re-cent years. However, the overproduction of animal manure hascaused serious environmental problems, such as water contamina-tion, odor pollution and excessive occupation of land, because ofthe lack of effective technologies to treat manure waste and inap-propriate treatment. In the United States, the amount of animalmanure is 130 times greater than the quality of human waste,whereas in China, 3.19 billion tons of animal manure was producedin 2003 but more than 70% was directly discharged to the environ-ment (Wang et al., 2006). Composting promoted by various meso-philic and thermophilic microorganisms under aerobic conditionshas been widely applied because of its low environmental and eco-nomic impact (Castaldi et al., 2008).

The application of composted animal manure could increasesoil fertility, improve the yield and quality of crops and preservethe environment (Benitez et al., 1998). However, the applicationof immature or unstable compost will cause a number of problems,including redox potential, nitrogen starvation, phytotoxicity andoxygen competition (Go9mez-Brando9n et al., 2008). Thus, compostmaturity, which referring to the degree of decomposition of phyto-

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toxic compounds and the absence of plant or animal pathogens incompost, should be considered a crucial factor that affects the suc-cessful application of compost to agricultural fields. A large varietyof physico-chemical and biological parameters, including tempera-ture, pH, electrical conductivity (EC), water-soluble carbon (WSC),OM loss, humification index (HI, humic acid-like C/total organic C),the ratios of C/N and NHþ4 -N=NO�3 -N and germination index (GI),have been suggested to evaluate the stability and maturity of com-post derived from different original materials. Microorganisms andtheir enzymatic activities have also recently arisen as good indica-tors of the stability and maturity of compost because of their keyroles in organic matter decomposition during the composting pro-cess (Vargas-García et al., 2010). However, the maturity of compostis mainly evaluated by personal experience in small scale compostplants in China because of the lack of efficient and inexpensivetechniques.

A traditional composting process, which takes several weeks toseveral months, proceeds through the mesophilic, thermophilicand cooling phases (Dees and Ghiorse, 2001). Shortening the com-posting time is necessary because of land shortages and the largevolume of accumulated animal manure. A large number of differentmesophilic and thermophilic aerobic microorganisms play domi-nant roles during each successive phase, thus, analyzing the com-post community structure and dynamics is helpful for the efficienttreatment of animal manure. Culture-based and culture-indepen-dent approaches, such as denaturing gradient gel electrophoresis

W. Tian et al. / Bioresource Technology 110 (2012) 330–337 331

(DGGE) analysis of 16S rDNA gene, the analysis of phospholipid fattyacid patterns and 16S rDNA library-based analysis, are utilized to re-veal changes in community profiles (Amir et al., 2008; Das et al.,2007; Dees and Ghiorse, 2001). However, the application of thesemethods is expensive, time consuming and laborious.

Excitation–emission matrix (EEM) fluorescence spectroscopycombined with analysis techniques is widely used to determineprotein-like, fulvic acid-like and humic acid-like substances be-cause it is sensitive, reagent-free and efficient compared with theuse of physico-chemical and biological parameters. Moreover, ithas been applied to evaluate compost maturity during compostingprocess recently. Tang et al. (2011) found that fluorescence regio-nal integration (FRI) technique is more suitable to characterizethe maturity of pig manure compost than peak intensity and peakratio. While EEMs companied with parallel factor (PARAFAC) anal-ysis has been demonstated to be a sententive and selective tool toassess compost maturity by Yu et al. (2010b). In the present study,the physico-chemical, biological and spectroscopic changes duringthe composting of dairy manure and rice chaff were monitored.The purpose of this investigation was to evaluate the potential ofEEM spectroscopy and its analysis techniques in assessing compostmaturity and biological parameters.

2. Methods

2.1. Composting process and sample collection

Composting experiments were conducted in the Lian Ye wastetreatment plant, Jiang Yin, China from September 2009 to February2010. Rice chaff was thoroughly mixed with dairy manure to adjustthe initial C/N ratio to about 33. The initial pH and moisture con-tent were about 7.5 and 65%, respectively. A compost windrowwith a volume of approximately 15 m3 (length �width �height = 5 m � 2 m � 1.5 m) was constructed. During the firsttwo weeks of composting, the windrow was turned every two daysto provide oxygen and to promote the homogeneity of the materi-als. After two weeks, it was turned every five days.

The composting was carried out under aerobic conditions for112 days, and on days 0, 5, 12, 22, 32, 42, 52, 62, 82 and 112, subs-amples were randomly collected from four equidistant cross-sec-tions and three different depths, surface (5–10 cm below thesurface of the pile), middle (50–60 cm below the surface of thepile) and bottom (100–120 cm below the surface of the pile). Afterbeing pooled and mixed, the entire sample was divided into threeparts, one of which was stored at �80 �C and the other two werestored at 4 �C and air-dried immediately.

2.2. Physico-chemical and phytotoxic analysis of the compost

The temperature at a depth of 60 cm was recorded at 16:00every day during the composting process using a mercury ther-mometer. The moisture content of fresh samples was determinedby oven-drying to a constant weight at 105 �C. The total carbonand total nitrogen content were measured by an auto elementalanalyzer (Vario EL III, Elementar, Germany) and were used to cal-culate the C/N ratio. The ash content of the compost samples wasanalyzed by the dry combustion method at 540 �C (Nelson andSommers, 1982). Losses of organic matter (OM) were calculatedaccording to the following equation (Benito et al., 2003):

OM loss ð%Þ ¼ 100� 100½Aið100� AfÞ�=½Afð100� AiÞ�

where Ai and Af are the initial and final ash contents, respectively.Water extracts of the fresh compost samples were prepared by

shaking the fresh sample with distilled water at 1:10 (w/v) using ahorizontal shaker for 24 h at room temperature. The extracts were

centrifuged at 12,000 rpm for 10 min and filtered using 0.45-lmpolytetrafluoroethylene (PTFE) filters for further analysis. The freshextracts were subjected to measurements of pH and electrical con-ductivity (EC) using a pH electrode (PB-10, Sartorius, Germany)and a Conductivity Indicator (LF91, Wiss. Techn. Werkstatten, Ger-many). Water-soluble carbon (WSC) was determined by a TOC/TNanalyzer (multi N/C 3000, Analytik Jena AG, Germany). The contensof water-extractable metal were determined with an ICP-OES(Spectroflame Modula E Spectro Analytical Instruments, Kleve,Germany). The concentrations of NHþ4 -N and NO�3 -N were mea-sured by an AutoAnalyzer (AA3, Bran and Luebbe, Germany).

A phytotoxicity text was performed by seed germination withLepidium sativum L. seeds. Five milliliters of compost water extract(1:10 w/v) of each sample was dropped into a plastic petri dishcontaining a filter paper, and 20 seeds were distributed on the filterpaper and incubated in the dark for 24 h at 25 �C. Deionized waterwas used as a control, and three replicates were performed for eachtreatment. The GI was calculated according to the following for-mula by Yu et al. (2010b): GI (%) = Seed germination numbers �root length of treatment � 100/seed germination numbers � rootlength of control.

2.3. Microbiological analysis

The populations of mesophilic and thermophilic bacteria, fungiand actinomycetes at various stages of the composting processwere determined. Thus, 10 g of each fresh sample was mixed with90 mL of sterilized water, shaken with horizontal shaker for 30 minand then left to stand for 10–15 min; serial dilutions (10�2–10�7)were then prepared in sterile water. Suitable media were usedfor the enumeration of the different groups of microorganisms.Bacteria were counted by plating on nutrient agar for two days;fungi and actinomycetes were counted by plating on Martin’s RoseBegal medium for 4 days and sodium caseinate agar for 6 days,respectively. The incubation temperature was 30 �C for mesophilicand 55 �C for thermophilic microorganisms.

2.4. PCR–DGGE analysis

The compost samples stored at�80 �C were thawed, and DNA ofeach sample was extracted with a PowerSoil� DNA Isolation Kit(Omega). The crude extracts were purified using a Wizard� DNAClean-Up Kit (Promega) as recommended by the manufacturer.

The primers GC341F (50-CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCGCCTACGGGAGGCAGCAG-30) and 534R (50-ATTA-CCGCGGCTGCTGG-30) were used to amplify the V3 region of thebacterial 16S rRNA gene for DGGE analysis (Muyzer et al., 1993).The PCR program was referred to Xiao et al. (2010). Controls lack-ing compost DNA were included for each PCR reaction. PCR wasperformed using a PTC-100 Programmable Thermal Cycler (MJ Re-search, Watertown, MA). The PCR products were run on a 2% (w/v)agarose gel, stained with ethidium bromide and then visualizedusing a Gel Doc Imaging System (Bio-Rad). Next, 50 lL of eachPCR product was purified using a PCR Cleanup Kit (Axygen) accord-ing to the manufacturer’s instructions, and DGGE was performedaccording to Das et al. (2007). Images of the gel were then collectedusing a Gel Doc Imaging System, and the PCR products were ana-lyzed by the DCode System (Bio-Rad).

2.5. Fluorescence spectroscopy determination and its analysis

Fluorescence determination and its analysis techniques referredto those of Yu et al. (2010a) and Tang et al. (2011). Water-extract-able organic matter (WEOM) was prepared by shaking the compostsamples with distilled water at 1:10 w/v using a horizontal shakerfor 24 h at room temperature; it was then centrifuged at

332 W. Tian et al. / Bioresource Technology 110 (2012) 330–337

12,000 rpm for 10 min and filtered using 0.45-lm polytetrafluoro-ethylene (PTFE) filters. Prior to the fluorescence analysis, the com-post extracts were diluted to dissolved organic carbon(DOC)<10 mg/L. Fluorescence EEMs were measured on a VarianEclipse fluorescence spectrophotometer in scan mode. Scanningemission (Em) spectra from 250 to 600 nm were obtained at 2-nm increments by varying the excitation (Ex) wavelength from200 to 500 nm at 10-nm increments. The spectra were recordedat a scan rate of 1200 nm min�1 using excitation and emission slitbandwidths of 5 nm. The voltage of the photomultiplier tube (PMT)was set at 800 V, and the temperature of the samples was main-tained at room temperature during the analyses. In current study,the FRI technique was performed to analyze the five excitation–emission regions of EEM spectroscopy, and the percentage of fluo-rescence response (Pi,n = Ui,n/UT,n) was calculated according toChen et al. (2003), where Ui,n and UT,n were the normalized excita-tion–emission area volumes referring to value of region i and theentire region, respectively.

2.6. Statistical analysis

Data were reported as the means of at least three replicates. Thestatistical analysis was conducted using SPSS software version 11.0for Windows (SPSS, Chicago, IL). Pearson’s correlation coefficient(r) was used to evaluate the linear correlation between two param-eters. DGGE bands were matched, and a dendrogram was con-structed by the unweighted pair group method with arithmeticmean (UPGMA) incorporating Jaccard’s coefficient of similarityusing Quantity One software (Bio-Rad).

3. Results and discussion

3.1. Physico-chemical and biological parameter evolution duringcomposting

Variations in temperature at the different stages are shown inFig. 1A. The temperature increased from ambient to approximately

Fig. 1. Physico-chemical and phytotoxic parameters evolution during composting proce

45 �C within 3 days and was maintained at 55–65 �C for about50 days. Then, the temperature gradually decreased to below45 �C, marking the end of the thermophilic phase. A range of 52–60 �C has been considered to be most favorable for keeping thehighest microbial activity and material biodegradation, provingthe functionality of the current composting process. The moisturecontent continuously decreased from 64.54 ± 0.06% to36.4 ± 0.30% during the composting process (Fig. 1B) because ofthe evaporation of water as a consequence of microbial heat gener-ation, which controlled the temperature of the windrow. Addition-ally, the continuous decline in moisture content could reflect thedegradation of organic matter (Miller and Finstein, 1985); asshown in Fig. 1C, the OM loss was 43.80% during the first 62 days,during which most water was evaporated, whereas it was only0.21% during the last 50 days, when very little drying occurred.

The C/N ratio and NHþ4 -N=NO�3 -N ratio have been extensivelyused as criteria to evaluate compost maturity (Mathur et al.,1993; Sánchez-Monedero et al., 1996). As the composting processprogressed, the C/N ratio decreased continuously (Fig. 1D), reach-ing the lowest value of 20.74 by the end of the composting process.A C/N ratio below 20 was considered to be a marker of compostmaturity by Mathur et al. (1993). However, the final C/N value de-pends on the materials being composted. Nitrogen-rich materialsmay result in a value within the range of mature compost beingobtained at the beginning of composting (Go9mez-Brando9n et al.,2008). As Fig. 1E shows, after a brief increase during the first12 days, the NHþ4 -N=NO�3 -N ratio significantly declined to 5.82 atday 42, then decreased gradually to 0.25 by the end of the com-posting process. Similar results were reported by Tiquia and Tam(2000). Decreasing NHþ4 -N combined with an increase of NO�3 -N re-sulted in a NHþ4 -N=NO�3 -N ratio below 1.0 by the end of compostingprocess, indicating the maturity of the final compost.

GI has been proven to be a reliable and sensitive biological indi-cator to reflect phytotoxicity and the degree of compost maturity.However, it is difficult to satisfy a wide range of composts preparedfrom various organic wastes. As shown in Fig. 1F, the GI value de-clined rapidly from the initial 32.60 ± 6.20% to 21.80 ± 4.13% at day5 because of a high accumulation of ammonia and low-molecular-

ss. (A) Temperature; (B) Water content; (C) OM loss; (D) C/N; (E) NHþ4 =NO�3 ; (F) GI.

W. Tian et al. / Bioresource Technology 110 (2012) 330–337 333

weight organic acids (Wong, 1985; Ko et al., 2008), which can beexplained by the significant correlations between GI and the pHand NHþ4 -N concentration (pH, r = �0.79, P < 0.01; NHþ4 -N,r = �0.86, P < 0.01). The GI then gradually increased and reacheda maximum of about 130% at day 62; after that, the GI was steadyuntil the end of the composting process. Zucconi et al. (1985) rec-ommended that compost with a value of above 60% could be con-sidered mature whereas Ko et al. (2008) suggested that GI shouldbe greater than 110%. In this study, at day 22, the GI value was82.35 ± 3.32%, but the composting was obviously progressingaccording to other parameters for evaluating compost maturitysuch as temperature, C/N, and NHþ4 =NO�3 . Therefore, a GI greaterthan 110% should be used as a maturity indicator for dairy manureand rich chaff compost in the current study, indicating that after52 days, the compost was phytotoxic-free.

The evolution of some water-extractable metal is shown in Ta-ble 1. It is interesting to note that in spite of concentrations of Zn,Fe, Mg, Mn, Cu fluctuated, they generally decreased significantlyduring composting process, resulting in the least concentrationswere obtained in the compost sampled on day 112. This mightbe explained by the increasing content of humic substances. Inaddition, low concentrations of Pb were determined in compostscollected on day 22, 32 and 42, while Cr was not detected in WEOMof each sample.

3.2. Microbiological analysis

Composting is a spontaneous biological process with a succes-sion of microbial populations and communities involved in thenutrient cycles (Beffa et al., 1996). The populations of mesophilicand thermophilic aerobic microorganisms at various stages weredetermined by the traditional dilution plating technique for evalu-ating the composting process. As shown in Table 2, populations ofall mesophilic microorganisms increased after the beginning ofcomposting since the presence of readily available carbon sub-strates and prevalence of mesophilic temperatures (Vargas-Garcíaet al., 2010). Mesophilic fungi reached the highest number at day12 while bacteria and actinomycetes peaked at day 22. Windrowtemperatures above 55 �C indicated that the thermophilic phaseof the composting was in progress at days 12 and 22. Some meso-philic microorganisms might exist in spores and not influence thedecomposition of organic matters, but their existence may also re-sult in the overestimation of mesophilic microorganisms by platecounting. The numbers of mesophilic microorganisms droppedgradually until day 52. Next, the temperature began to decrease,and mesophilic microorganism numbers increased again duringthe last 50 days of the composting process. Similar results hadbeen reported by Hassen et al. (2001). As decomposition and heataccumulation progressed, the windrow temperatures and popula-tions of thermophilic microorganisms began to increase, reachingthe highest levels at day 32 for bacteria and actinomycetes andat day 22 for fungi, and then declined until the end of the compost-

Table 1Evolution of water-extractable metal during composting process.

Samples Zn (mg kg�1 dw) Fe (mg kg�1 dw) Mg (mg kg�1 dw

0 3.00 ± 0.20 25.78 ± 0.70 517.6 ± 11.4012 2.63 ± 0.05 19.74 ± 0.10 630.8 ± 27.4022 2.68 ± 0.10 15.6 ± 0.04 242.1 ± 5.5032 2.92 ± 0.10 12.98 ± 0.18 209.2 ± 10.2042 2.54 ± 0.12 8.96 ± 0.20 177.3 ± 6.7062 2.78 ± 0.10 10.23 ± 0.29 225.6 ± 4.6082 2.61 ± 0.11 13.61 ± 0.13 283.4 ± 5.40

112 2.37 ± 0.10 7.8 ± 0.20 133.38 ± 1.38

Cr was not detected in water-extractable organic matter (WEOM) of each sample.

ing process. Contradictory results have been reported about thesuccession of fungal populations during composting. Tiquia(2002) reported that most fungi were eliminated at temperaturesabove 50 �C and recovered below 45 �C whereas Goyal et al.(2005) found high numbers of fungi during the thermophilic phaseof composting (109 cfu g�1 dw). In the current study, populationsof mesophilic and thermophilic fungi both remained at low levelsduring the thermophilic phase of composting (Table 2), whichwas in accordance with Tiquia (2002).

Bacteria are usually considered to be the main decomposers inthe composting process (Hassen et al., 2001). The findings in thepresent study supported this idea, as shown in Table 2. DGGEwas applied to investigate community structure and dynamicsduring the composting process because of its advantages of com-paring many samples at the same time and being more easily ana-lyzed than 16S rDNA library-based methods. The band numbers inthe denaturing gel indicate the relative diversity of the microbialcommunity, and each band can be considered to be a discrete rib-otype according to Muyzer et al. (1993) and Das et al. (2007). DGGEband patterns shown in Fig. 2A illustrate the profile of bacterialcommunities during the composting process. Complex bandingpatterns indicated the high diversity of bacteria during the com-posting process, and continuous variation was observed with thetemperature increase during the first 12 days. As shown inFig. 2A, the cluster analysis indicated the same result because thesimilarity between samples at day 0 and samples at day 5 and 12was only about 0.34. Moreover, the changes of microbial diversitywere in accordance with those of the composting phases. Threesamples in the thermophilic phase (on day 22, 32 and 52) wereclustered into one subgroup, and three samples in the coolingphase (on day 62, 82 and 112) were clustered into another sub-group. In addition, the microbial diversity of the thermophilicphase showed a higher similarity with that of the cooling phasethan of the mesophilic phase. The result was inconsistent with thatof Klammer et al. (2005), who found that very fresh and very ma-ture samples clustered together regardless of the storage condi-tions of the compost samples. This difference may be ascribed tothe original materials and the processing parameters, such aswater content and oxygen supply, which seriously affect the com-munity structure and the dynamics of microorganisms during thecomposting process (Beffa et al., 1996). Ribotypes of each sampleare calculated as shown in Fig. 2B. The microbial diversity in-creased after the beginning of composting, reaching the highest le-vel on day 12, and then declined gradually during the thermophilicphase, reaching the lowest level on day 52. During the coolingphase, the microbial diversity increased slightly, and the numberof ribotypes was maintained at a relatively steady level (25–27).A Pearson correlation analysis between ribotype evolution andmaturity indices was performed. It is interesting to note that theevolution of ribotypes was significantly correlated with C/N,NHþ4 =NO�3 and GI (C/N, r = 0.87, NHþ4 =NO�3 , r = 0.84, GI, r = �0.93,P < 0.01). Thus, the ribotype evolution of bacteria could be consid-

) Pb (mg kg�1 dw) Mn (mg kg�1 dw) Cu (mg kg�1 dw)

0 4.82 ± 0.12 4.05 ± 0.050 4.15 ± 0.13 3.06 ± 0.100.01 ± 0.001 1.58 ± 0.08 2.52 ± 0.120.06 ± 0.02 1.81 ± 0.15 2.06 ± 0.100.01 ± 0.01 1.34 ± 0.04 2.13 ± 0.l90 1.83 ± 0.03 1.74 ± 0.140 1.51 ± 0.11 1.38 ± 0.180 0.71 ± 0.11 0.86 ± 0.02

Table 2Changes in mesophilic and thermophilic microbial population during composting.

Samples Mesophilic microbial population (log10 CFU g�1 dw) Thermophilic microbial population (log10 CFU g�1 dw)

Bacteria Actinomy cetes Fungi Bacteria Actinomy cetes Fungi

0 7.44 ± 0.30 5.57 ± 0.22 3.40 ± 0.22 4.65 ± 0.53 3.58 ± 0.39 0.005 8.40 ± 0.22 6.28 ± 0.17 4.21 ± 0.14 5.88 ± 0.19 4.62 ± 0.35 3.91 ± 0.31

12 8.99 ± 0.13 6.94 ± 0.14 5.12 ± 0.06 6.69 ± 0.18 5.09 ± 0.20 4.76 ± 0.3422 9.33 ± 0.10 7.18 ± 0.14 4.92 ± 0.21 7.05 ± 0.13 5.51 ± 0.22 3.24 ± 0.4432 8.84 ± 0.17 6.78 ± 0.13 4.11 ± 0.14 7.14 ± 0.32 5.78 ± 0.15 3.20 ± 0.2642 8.27 ± 0.17 6.44 ± 0.11 3.64 ± 0.15 7.17 ± 0.21 5.66 ± 0.20 2.92 ± 0.1452 8.21 ± 0.23 6.42 ± 0.27 3.48 ± 0.17 6.22 ± 0.35 5.56 ± 0.21 2.33 ± 0.2262 8.59 ± 0.30 6.72 ± 0.28 4.03 ± 0.15 5.63 ± 0.41 5.15 ± 0.15 2.20 ± 0.1782 8.80 ± 0.21 6.91 ± 0.17 4.31 ± 0.11 4.96 ± 0.67 4.48 ± 0.36 2.15 ± 0.19

112 8.53 ± 0.19 6.54 ± 0.34 4.30 ± 0.15 4.46 ± 0.45 4.09 ± 0.49 2.30 ± 0.26

Fig. 2. DGGE profiles of 16S rDNA gene, UPGMA dendrogram constructed from similarity matching data generated by using quantity one and ribotypes evolution of bacteriaduring composting process. (A) DGGE profiles and UPGMA dendrogram; (B) Ribotypes evolution of bacteria during composting.

Fig. 3. EEM contours of WEOM from compost during composting process. Peak A, peak B and peak C represent protein-like, fulvic-like and humic-like substances,respectively. (A) 0 d; (B) 12 d; (C) 22 d; (D) 32 d; (E) 42 d; (F) 62 d; (G) 82 d; (H) 112 d.

334 W. Tian et al. / Bioresource Technology 110 (2012) 330–337

Table 3FRI parameters evolution during composting process.

Samples PI,n (%) PII,n (%) PIII,n (%) PIV,n (%) PV,n (%) PV,n/PIII,n

0 15.58 12.83 30.89 8.07 33.63 1.0212 7.92 11.26 31.83 7.70 41.29 1.3022 4.79 8.72 33.44 5.30 47.74 1.4332 4.54 4.27 34.64 3.22 53.33 1.5442 3.91 3.66 34.38 3.23 54.81 1.5962 2.23 4.27 32.57 3.27 57.66 1.7782 1.56 4.09 32.58 3.68 58.11 1.78

112 2.12 4.34 35.02 3.57 54.94 1.57

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ered to be an index to evaluate the maturity of compost preparedby dairy manure and rice chaff.

3.3. EEM fluorescence spectra analysis

EEM fluorescence spectra combined with various analysis tech-niques, such as ratios of peaks intensities and fluorescence regionalintegration (FRI), has been extensively applied to characterize dis-solved organic matter in water, compost and soil samples (Goneet al., 2009; Richard et al., 2009; Yu et al., 2010b). Recently, someinvestigators have used EEM fluorescence spectra to monitor thematurity of compost, because it is time saving and requires mini-mal sample pretreatment compared with typical physico-chemicalmethods (Yu et al., 2010b; Tang et al., 2011).

Fluorescence EEM contours of WEOM from dairy manure com-post are shown in Fig. 3. Three distinct peaks defined as Peak A,Peak B and Peak C were obtained at Ex/Em of 220–230/300–350(Peak A), 230/420–440 (Peak B) and 340/420–440 (Peak C). Ascomposting progressed, the intensity of Peak A, representing pro-tein-like substances, became weaker and weaker until it disap-peared on day 62. However, the intensities of Peak B and Peak C,representing fulvic-like and humic-like substances, respectively,became increasingly stronger. Dynamics of the three peak intensi-ties directly reflected the decomposition of protein-like substancesand the formation of humic-like substances during the compostingprocess, which demonstrated the potential of EEM fluorescencespectra to assess the maturity of dairy manure compost.

The FRI technique was applied to analyze the fluorescence EEMquantitatively in the present study. According to Chen et al. (2003)and Marhuenda-Egea et al. (2007), the EEM was delineated into

Table 4Pearson correlation between physico-chemical and biological indices and FRI parameters.

PI,n PII,n,

Physico-chemical parametersa

C/N 0.920** 0.959**

NHþ4 =NO�3 0.659 0.836**

EC �0.737* �0.733*

pH �0.888** �0.97**

WSC 0.681 0.694Water content 0.859** 0.883**

TC 0.966** 0.957**

TN �0.862** �0.925**

Volatile solids 0.923** 0.879**

OM loss �0.943** �0.917**

Biological parametersGI �0.870** �0.949**

Mesophilic bacteria �0.629 �0.257Thermophilic bacteria �0.136 �0.069Ribotype evolution 0.711* 0.907**

* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).

a Evolution of some physico-chemical parameters were not shown in this paper.

five excitation–emission regions relating to simple aromatic pro-teins (Regions I and II), fulvic acid-like materials (Region III), solu-ble microbial by-product-like materials (Region IV) and humicacid-like compounds (Region V). Changes of Pi,n from differentsamples are displayed in Table 3. PI,n, PII,n and PIV,n decreasedgreatly during the first 32 days, and then, they all stayed at rela-tively steady levels, suggesting the main decomposition of simplearomatic proteins and soluble microbial by-product-like sub-stances in the first 32 days. The value of PIII,n increased slightly,but the value of PV,n increased significantly during the compostingprocess, which indicated that higher molecular weight and morestable humic-like substances were produced preferentially to ful-vic acid-like materials. The initial Pi,n values of the five regionswere dependent on the source of raw materials for composting,and the material transformations indicated by the evolution ofPi,n were in accordance with the changes in peak intensities. Theratio of PV,n/PIII,n increased gradually during composting andreached the highest level (1.78) at day 62. Ko et al. (2008) reportedthat CHA/CFA was one of the most reliable parameters to evaluatecompost maturity, and compost with a CHA/CFA higher than 1.6could be considered mature. Therefore, in this experiment, thehigher ratio of PV,n/PIII,n (1.78) at day 62 suggested that the com-post should be mature. Moreover, the determination of PV,n/PIII,n

was simpler and more sensitive than CHA/CFA, whose determinationrequires proper separation of the non-humic fraction from the ful-vic acid fraction.

To investigate the potential of fluorescence EEM for evaluatingthe maturity of dairy manure and rice chaff compost, a Pearsoncorrelation between maturity indices and FRI parameters was con-ducted. As Table 4 shows, PV,n and PV,n/PIII,n were all significantlycorrelated with the physico-chemical parameters, PII,n, and PIV,n

were not significantly correlated with WSC, and PI,n was not signif-icantly correlated with NHþ4 =NO�3 and WSC. The WSC contained thewater-soluble carbon including that both from original compostingmaterials and microbial metabolism, whereas the PIV,n referred tothe soluble microbial by-product-like substance that from micro-bial metabolism no matter WSC or WSN except that from originalcomposting materials, which might be the reason why PIV,n wasnot correlated with WSC. Additionally, PIII,n was significantly corre-lated with C/N, TC and pH, but this result may be due to the slightchanges of fulvic acid-like materials during the composting pro-cess. The correlations above between physico-chemical and FRIparameters suggested that the evolutions of Pi,n (except for PIII,n)

PIII,n PIV,n PV,n PV,n/PIII,n

�0.693* 0.934** �0.970** �0.929**

�0.438 0.804* �0.808* �0.774**

0.359 �0.684 0.785* 0.789*

0.676* �0.977** 0.973** 0.931**

�0.298 0.618 �0.734* �0.747*

�0.605 0.842** �0.903** �0.868**

�0.719* 0.929** �0.983** �0.951**

0.639 �0.892** 0.927** 0.885**

�0.641 0.834* �0.922** �0.895**

0.626 �0.876** 0.960** 0.943**

0.637 �0.954** 0.957* 0.918**

0.352 �0.281 0.416 0.4580.294 �0.114 0.060 0.043�0.800* 0.949* �0.832* �0.725*

Fig. 4. Linear regression analysis between GI and FRI parameters. (A) PI,n; (B) PII,n; (C) PIII,n; (D) PV,n; (E) PIV,n.

336 W. Tian et al. / Bioresource Technology 110 (2012) 330–337

were appropriate for evaluating dairy manure compost maturity,especially PV,n and PV,n/PIII,n. The results were in accordance withthat of Tang et al. (2011) in pig manure compost.

In addition, a Pearson correlation analysis between biologicaland FRI parameters was performed. All of the FRI parameters ex-cept PIII,n were significantly correlated with GI (Table 4); however,no significant correlations were founded between the populationchanges of mesophilic and thermophilic bacteria. In contrast, allof the FRI parameters were significantly correlated with the evolu-tion of ribotypes that indicated the bacterial community structureand dynamics from DGGE analysis, indicating their potential usefor evaluating community structure and dynamics.

Complex and insoluble organic materials cannot be directlymetabolized by microbes; the microbes must secrete hydrolyticextracellular enzymes to depolymerize the larger compound intowater-soluble substances and then assimilate them to form newmetabolic products. Therefore, the analysis of soluble microbialby-product-like materials during the composting process may bea useful tool for understanding microbial community structureand dynamics. The correlations between FRI parameters and ribo-types that indicate the community structure and dynamics wereexamined further through linear regression analysis. Significantcorrelations were established between ribotypes and each of theFRI parameters. The R2 value of the PIV,n was significantly higherthan the other four FRI parameters (Fig. 4), which demonstratedthat PIV,n was a suitable indicator of the community structureand dynamics during the composting process.

DGGE and 16S rDNA library-based analysis are the main ap-proaches to investigate the community succession of different con-ditions. However, they both require DNA extraction and PCRamplification, which are expensive, are laborious and can resultin incorrect estimations, especially for compost containing about10- to 100-fold higher concentrations of humic acid than mineralsoil (Pfaller et al., 1994). Huge amounts of humic acid may compli-cate DNA extraction and inhibit PCR amplification. In contrast,fluorescence EEM is a reagent-free technique with minimal samplepretreatment conducted prior to analysis, and its application will

afford a convenient, inexpensive and sensitive method to evaluatethe community succession of composting or other processes.

4. Conclusion

In the current study, fluorescence EEM combined with FRI tech-nique was used to assess maturity and biological parameters dur-ing the composting process, and the results demonstrated that FRIparameters, especially PV,n and PV,n/PIII,n, were correlated withmany physico-chemical characteristics, proving their suitabilityfor assessing the maturity of compost produced from dairy manureand rice chaff; Additionally, It is interesting to found that PIV,n

showed significant correlation with ribotypes in the present study,which may afford a valuable tool for the future study of commu-nity succession during composting process.

Acknowledgements

This work was funded by the National Basic Research Programof China (No. 2011CB100503), the National Natural Science Foun-dation of China (Nos. 21007027 and 40871126), the Specialized Re-search Fund for the Doctoral Program of Higher Education (No.20100097120015), the China Postdoctoral Science Foundation(No. 20100481156), the Agricultural Ministry of China (Nos.2011-G27 and 201103004), the Key Agricultural Project of JiangsuProvince (SX(2010)220), and the Chinese Ministry of Science andTechnology (2010AA10Z401).

References

Amir, S., Merlina, G., Pinelli, E., Winterton, P., Revel, J.C., Hafidi, M., 2008. Microbialcommunity dynamics during composting of sewage sludge and straw studiedthrough phospholipid and neutral lipid analysis. J. Hazard. Mater. 159, 593–601.

Beffa, T., Blanc, M., Lyon, P.F., Vogt, G., Marchiani, M., Lott Fischer, J., Aragno, M.,1996. Isolation of Thermus strains from hot composts (60–80 �C). Appl. Environ.Microbiol. 62, 1723–1727.

Benitez, C., Bellido, E., Gonzales, G.L., Medina, M., 1998. Influence of pedalogical andclimatic factors on nitrogen mineralization in soils treated with pig slurrycompost. Bioresour. Technol. 63, 147–151.

W. Tian et al. / Bioresource Technology 110 (2012) 330–337 337

Benito, M., Manager, A., Moliner, A., Arrigo, N., Palma, R.M., 2003. Chemical andmicrobiological parameters for the characterization of the stability andmaturity of pruning waste compost. Biol. Fertil. Soils 37, 184–189.

Castaldi, P., Garau, G., Melis, P., 2008. Maturity assessment of compost frommunicipal solid waste through the study of enzyme activities and water-solublefractions. Waste Manage. 28, 534–540.

Chen, W., Westerhoff, P., Leenheer, J.A., Booksh, K., 2003. Fluorescence excitation–emission matrix regional integration to quantify spectra for dissolved organicmatter. Environ. Sci. Technol. 37, 5701–5710.

Das, M., Royer, T.V., Leff, L.G., 2007. Diversity of fungi, bacteria, and actinomyceteson leaves decomposing in a stream. Appl. Environ. Microbiol. 73, 756–767.

Dees, P.M., Ghiorse, W.C., 2001. Microbial diversity in hot synthetic compost asrevealed by PCR-amplified rRNA sequences from cultivated isolates andextracted DNA. FEMS Microbiol. Ecol. 35, 207–216.

Go9mez-Brando9n, M., Lazcano, C., Domínguez, J., 2008. The evaluation of stabilityand maturity during the composting of cattle manure. Chemosphere 70, 436–444.

Gone, D.L., Seidel, J.L., Batiot, C., Bamory, K., Ligban, R., Biemi, J., 2009. Usingfluorescence spectroscopy EEM to evaluate the efficiency of organic matterremoval during coagulation–flocculation of a tropical surface water (Agboreservoir). J. Hazard. Mater. 172, 693–699.

Goyal, S., Dhull, S.K., Kapoor, K.K., 2005. Chemical and biological changes duringcomposting of different organic wastes and assessment of compost maturity.Bioresour. Technol. 96, 1584–1591.

Hassen, A., Belguit, K., Jedidi, N., Cherif, A., Cherif, M., Boudabous, A., 2001. Microbialcharacterization during composting of municipal solid waste. Bioresour.Technol. 80, 217–225.

Klammer, S., Mondini, C., Insam, H., 2005. Microbial community fingerprint ofcomposts store under different conditions. Ann. Appl. Microbiol. 55, 299–305.

Ko, H.J., Kim, K.Y., Kim, H.T., Kim, C.N., Umeda, M., 2008. Evaluation of maturityparameters and heavy metal contents in composts made from animal manure.Waste Manage. 28, 813–820.

Marhuenda-Egea, F.C., Martinez-Sabater, E., Jorda, J., Moral, R., Bustamante, M.A.,Paredes, C., Perez-Murcia, M.D., 2007. Dissolved organic matter fractionsformed during composting of winery and distillery residues: evaluation of theprocess by fluorescence excitation–emission matrix. Chemosphere 68, 301–309.

Mathur, S.P., Owen, G., Dinel, H., Schnitzer, M., 1993. Determination of compostbiomaturity: literature review. Biol. Agric. Hortic. 10, 65–85.

Miller, F.C., Finstein, M.S., 1985. Materials balance in the composting of wastewatersludge as affected by process control. J. Wat. Pollut. Contr. Fed. 57, 122–127.

Muyzer, G., Dewaal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial-populations by denaturing gradient gelelectrophoresis analysis of polymerasechain reaction-amplified genes coding for 16S ribosomal-RNA. Appl. Environ.Microbiol. 59, 695–700.

Nelson, D.W., Sommers, L.E., 1982. Total carbon and organic carbon and organicmatter. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Method of Soil Analysis.American Society of Agronomy, Madison, pp. 539–579.

Pfaller, S.L., Vesper, S.J., Moreno, H., 1994. The use of PCR to detect a pathogen incompost. Compost. Sci. Util. 2, 48–54.

Richard, C., Guyot, G., Trubetskaya, O., Trubetskoj, O., Grigatti, M., Cavani, L., 2009.Fluorescence analysis of humic-like substances extracted from composts:influence of composting time and fractionation. Environ. Chem. Lett. 7, 61–65.

Sánchez-Monedero, M.A., Bernal, M.P., Roig, A., Cegarra, J., García, D., 1996. Theeffectiveness of the Rutgers system and the addition of bulking agent inreducing N-losses during composting. In: Van Cleemput, O., Hofman, G.,Vermoesen, A. (Eds.), Progress in Nitrogen Cycling Studies. Kluwer AcademicPublishers, Dordrecht, pp. 133–139.

Tang, Z., Yu, G.H., Liu, D.Y., Xu, D.B., Shen, Q.R., 2011. Different analysis techniquesfor fluorescence excitation–emission matrix spectroscopy to assess compostmaturity. Chemosphere 82, 1202–1208.

Tiquia, S.M., Tam, N.F.Y., 2000. Co-composting of spent pig litter and sludge withforced-aeration. Bioresour. Technol. 72, 1–7.

Tiquia, S.M., 2002. Microbial transformation of nitrogen during composting. In:Insam, H., Riddech, N., Klammer, S. (Eds.), Microbiology of Composting andOther Biodegradation Processes. Springer-Verlag, Berlin, Heidelberg, pp. 237–245.

Vargas-García, M.C., Suárez-Estrella, F., López, M.J., Moreno, J., 2010. Microbialpopulation dynamics and enzyme activities in composting processes withdifferent starting materials. Waste Manage. 30, 771–778.

Wang, F., Ma, W., Dou, Z., Ma, L., Liu, X., Xu, J., Zhang, F., 2006. The estimation of theproduction amount of animal manure and its environment effect in China.China Environ. Sci. 26, 614–617.

Wong, M.H., 1985. Phytotoxicity of refuse compost during the process ofmaturation. Environ. Pollut. 40, 127–144.

Xiao, Y., Zeng, G.M., Yang, Zh.H., Ma, Y.H., Huang, C., Xu, Zh.Y., Huang, J., Fan, Ch.Zh.,2010. Changes in the actinomycetal communities during continuousthermophilic composting as revealed by denaturing gradient gelelectrophoresis and quantitative PCR. Bioresour. Technol. 102, 1383–1388.

Yu, G.H., He, P.J., Shao, L.M., 2010a. Novel insights into sludge dewaterability byfluorescence excitation–emission matrix combined with parallel factor analysis.Water Res. 44, 797–806.

Yu, G.H., Luo, Y.H., Wu, M.J., Tang, Z., Liu, D.Y., Yang, X.M., Shen, Q.R., 2010b.PARAFAC modeling of fluorescence excitation–emission spectra for rapidassessment of compost maturity. Bioresour. Technol. 101, 8244–8251.

Zucconi, F., Monaco, A., Forte, M., Bertoldi, M., 1985. Phytotoxins during thestabilization of organic matter. In: Gasser, J.K.R. (Ed.), Composting ofAgricultural and Other Wastes. Elsevier Applied Science, London, New York,pp. 73–88.