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Research ArticleReceived: 17 February 2010 Revised: 16 May 2010 Accepted: 18 May 2010 Published online in Wiley Interscience: 1 July 2010

(www.interscience.wiley.com) DOI 10.1002/jsfa.4044

Critical moisture content for microbial growthin dried food-processing residuesFarzaneh Rezaei and Jean S VanderGheynst∗

Abstract

BACKGROUND: Food-processing residues are good feedstocks for biofuel and biochemical production because they have highenergy content and are abundant. Year-round biofuel and biochemical production requires proper storage to prevent microbialdecomposition and thermal runaway. In this study, microbial activity of tomato pomace (TP), grape pomace (GP), fermentedgrape pomace (FGP) and sugar beet pulp (SBP) was monitored at nine different moisture contents.

RESULTS: Maximum and cumulative respirations for each feedstock with respect to moisture content followed a sigmoidalrelationship. The critical moisture content below which no microbial activity was detected for SBP, TP, FGP and GP was 24–31,16–21, 23–33 and 43–46% (dry basis) respectively. A logarithmic relationship was observed (R2 = 0.94) between criticalmoisture content and initial water-soluble carbohydrate (WSC) content of the processing residues.

CONCLUSION: The critical moisture content below which no microbial activity was detected and the relationship between criticalmoisture content and initial WSC content were determined in this study for four food-processing residues. Both parameterspermit evaluation of the potential for deterioration of food-processing residues during storage based on moisture content andWSC content.c© 2010 Society of Chemical Industry

Keywords: food-processing residue; biomass storage; moisture content; respirometry

INTRODUCTIONApproximately 4 × 106 tons of food-processing residues areproduced each year in California. Among them, the non-meatportion is about 2.28 × 105 ton year−1, of which tomato pomaceand grape pomace account for 88%.1 Another residue is sugarbeet pulp, a by-product from beet sugar production. Sugarproduction in California results in 7.6 × 104 dry ton year−1

of sugar beet pulp. All these materials are suitable feedstocksfor bioenergy and biochemical production because they areabundant and low in lignin content, but they are currentlybeing wasted or used for lower-value products. Since thesematerials are produced seasonally, it is necessary to find anappropriate stabilisation method to allow year-round bioenergyand biochemical production. In addition, methods are needed toprevent decomposition associated with microbial activity that maylead to thermal runaway and spontaneous combustion.2 Thereare several critical factors that affect microbial activity, such aspH, temperature and moisture content.3,4 One way of minimisingmicrobial activity is to dry the material immediately after harvest.This approach is common in dry and warm climates. However, themoisture content of the material might increase owing to rainfall,floods or high-humidity conditions. Therefore it is important to beable to estimate the risk of microbial activity in stored materials atdifferent moisture contents.

Appropriate moisture contents for the storage of somelignocellulosic materials have been reported previously. Forinstance, a moisture content below 25–30% (wet basis) wasneeded to have little or no microbial activity in hay,5,6 while22–28% (wet basis) was required for rice straw.7 However,

moisture levels for dry storage of food-processing residues havenot been reported. The overall goals of this research were (1) toexamine the change in microbial activity with varying moisturecontent for four seasonally produced food-processing residues,(2) to determine the moisture content limit below which noor little microbial activity could be detected (critical moisturecontent) and (3) to determine if the critical moisture content forvarious feedstocks could be related to the initial water-solublecarbohydrate content of each feedstock.

MATERIALS AND METHODSFeedstock preparationFour different food-processing residues were examined, namelysugar beet pulp (SBP), tomato pomace (TP), grape pomace (GP)and fermented grape pomace (FGP). For each feedstock, air-dried material was used as the minimum moisture contenttreatment. The moisture content of the fresh feedstock wasused to set the maximum moisture content tested in this study.Seven moisture contents between the maximum and minimummoisture content were prepared by adding water to the air-dried materials and storing them in a refrigerator overnight to

∗ Correspondence to: Jean S VanderGheynst, Department of Biological andAgricultural Engineering, University of California at Davis, Davis, 95616 CA,USA. E-mail: jsvander@ucdavis.edu

Department of Biological and Agricultural Engineering, University of Californiaat Davis, Davis, 95616 CA, USA

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equilibrate. The moisture content of each sample was obtainedby measuring the loss of water after drying the sample at 105 ◦Cin a convection oven for 24 h. All moisture contents are reportedon a dry basis (d.b.). Water-soluble carbohydrate (WSC) contentwas determined in extracts of SBP, TP, GP, FGP and rice straw.The rice straw source and its handling are described in detailelsewhere.7 Extracts were prepared at 10 : 1 (water:sample) massdilution and stirred for 30 min prior to measurement. Extracts werecentrifuged at ∼11, 000 g for 10 min and then filtered through0.22 µm polytetrafluoroethylene filter paper. The WSC level ineach filtrate was estimated using a colorimetric phenol/sulfuricacid method.8 Neutral detergent fibre (NDF), acid detergent fibre(ADF) and acid detergent lignin (ADL) were determined by theprocedures outlined by Vogel et al.9 Hemicellulose and cellulosewere calculated as NDF - ADF and ADF - ADL respectively.

Feedstock incubationFor each feedstock, materials at different moisture contentswere transferred to individual bioreactors made from 250 mLhigh-density polyethylene containers (Nalge Nunc International,Rochester, NY, USA), where the feedstock was supported on astainless steel mesh screen with four 2.5 cm screws for legs,as described elsewhere,7 and incubated at 35 ◦C. The reactorswere aerated continuously with humidified air at 7–10 mL min−1

to avoid oxygen limitations and prevent drying. The air flowrate (5–50 mL air min−1) was regulated using polycarbonaterotameters (Dwyer Instruments, Inc., Michigan City, IN, USA) andmonitored using a mass flow meter (Model GFM17, Aalborg,Orangeburg, NY, USA) with a flow range of 0–50 mL min−1. Carbondioxide concentration was measured using an infrared CO2 sensor(Vaisala, Newmarket, UK). Carbon dioxide and air flow rate datawere recorded every 5 h using a data acquisition system. Carbondioxide evolution rates (CERs, mmol CO2 h−1 g−1 dry biomass)were calculated from mass balances on each reactor according tothe equation

CER = F([CO2]out − [CO2]in) (1)

where F is the air flow rate (mmol air h−1 g−1) and [CO2]out and[CO2]in are the concentrations of carbon dioxide in the effluentand influent air respectively (mmol CO2 mmol−1 air).

Data analysisTo analyse the relationship between moisture content andmicrobial activity, two microbial activity metrics were used:peak respiration (PR) and cumulative respiration (CR) over 6 daysof incubation. For each feedstock, both peak and cumulativerespirations were plotted versus the natural logarithm of moisturecontent to determine the critical and maximum moisture contentswith respect to microbial activity. A sigmoidal relationship basedon prior analysis with compost and rice straw7,10 was used to fitthe data:

y = a exp[b(x − x0)]/(1 − (a/c){1 − exp[b(x − x0)]}) (2)

where y is PR (mg CO2 day−1 g−1 dry biomass) or CR (mg CO2 g−1

dry biomass), x is the natural logarithm of moisture content, x0

represents the natural logarithm of the lowest moisture contentexamined in the study and a, b and c are constant parameters.Parameter a represents a baseline for y and was found by takingthe average of the PR or CR values for measured moisture contentsbelow 30% for SBP and FGP and below 20% for TP. Parameters b

and c represent the rate of increase in PR or CR with increasingmoisture content and the maximum PR or CR respectively. Bothparameters were estimated by nonlinear regression of PR and CRdata to Eqn (2) using Kaleidagraph Version 4 (Synergy Software,Reading, PA, USA).

Critical moisture content was defined as the range of moisturecontent below which no or little activity was detected (respirationrate negative or close to zero) and was determined by examinationof respiration rate data for each feedstock. An empirical model wasdeveloped to relate critical moisture to initial WSC content. It wasassumed that critical moisture would be proportional to moisturecontent on either a linear or a logarithmic basis. The model andparameters were determined by regression of critical moistureand WSC data using Kaleidagraph. To test the predictive power ofthe empirical model, the model was used to determine the criticalmoisture content for rice straw and the result was compared withmeasured values reported in the literature.

RESULTSMicrobial activity of the different feedstocks stored under aerobicconditions was monitored at different moisture contents. Therespiration rate and cumulative respiration for SBP are presentedin Figs 1 and 2 respectively. Microbial activity measured as eitherrespiration rate or cumulative respiration increased as the moisturecontent increased. After reaching a peak, microbial activitydeclined. In general, trends in respiration observed for SBP weresimilar to those observed for other the processing residues (datanot shown). Also, the time to reach maximum activity decreasedas the moisture content increased.

Peak and cumulative respiration levels for the differentfeedstocks were examined using Eqn (2). As illustrated in Figs 3and 4, all feedstocks except GP followed the sigmoidal relationship(Eqn (2)). For GP, adding water to the air-dried sample did noteffectively wet this feedstock, and most GP samples had a moisturecontent of about 40%. The poor distribution of moisture contentsprevented regression of the respiration data to Eqn (2). Peakrespiration data for SBP, TP and FGP had fits to Eqn (2) with R2

values of 0.97, 0.94 and 0.8 respectively after removing the last datapoint (Table 1). Cumulative respiration data for SBP, TP and FGPhad fits to Eqn (2) with R2 values of 0.98, 0.99 and 0.92 respectively(Table 2).

Parameters b and c in Eqn (2) were estimated by nonlinearregression of peak respiration data and are reported in Table 1.Since the GP data did not fit the equation, parameters b and c werenot determined for this material. Parameter c, which representsthe predicted maximum peak activity for each feedstock, washighest for TP (275 mg CO2 day−1 g−1 dry biomass) followedby SBP (111 mg CO2 day−1 g−1 dry biomass) and FGP (60 mgCO2 day−1 g−1 dry biomass), (Table 1). Parameter b represents therate of change in peak activity with moisture. This parameter variedlittle between feedstocks, ranging from 2.63 to 4.32.

Parameter values obtained by regression of cumulative respi-ration data to Eqn (2) are presented in Table 2. For cumulativerespiration, parameter c was highest for TP (424 mg CO2 g−1 drybiomass), followed by SBP (327 mg CO2 g−1 dry biomass) andFGP (260 mg CO2 g−1 dry biomass). Parameter b varied betweenfeedstocks, being highest for TP and SBP (7.68) and lowest for FGP(2.8).

The critical moisture content varied for different feedstocks.For SBP, TP, FGP and GP it was determined as 24–31, 16–21,23–33 and 43–46% d.b. respectively (Fig. 5). Growth of mould was

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0.1

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0 20 40 60 80 100 120 140

176%105%59%32%10% (Field Dried)

Res

pira

tion

rate

(mm

ol C

O2

h−1 g

−1 d

ry b

iom

ass)

Time (h)

Figure 1. Respiration rate associated with microbial activity on sugar beetpulp at different moisture contents.

Figure 2. Cumulative respiration associated with microbial activity onsugar beet pulp at different moisture contents.

observed on all feedstocks at moisture contents above the criticalmoisture content.

The WSC content of SBP, TP, GP and FG was determined as5.0, 3.2, 44.6 and 1.9% d.b. respectively (Table 3). An empiricalrelationship between initial WSC content and critical moisturecontent of the four selected feedstocks was obtained to be able topredict the critical moisture content for a variety of feedstocks. Asillustrated in Fig. 6, the critical moisture content increased as theWSC content increased logarithmically. The following empiricalequation was found to represent this relationship:

y = 16.0 + 18.9 log(x) (3)

0

20

40

60

80

100

120

2 2.5 3 3.5 4 4.5 5 5.5

Pea

k re

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(mg

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ry b

iom

ass)

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(a)

(b)

Figure 3. Peak respiration rate with respect to natural logarithm ofmoisture content for (a) sugar beet pulp, (b) tomato pomace and(c) fermented grape pomace. The curves represent y = aexp[b(x−x0)]/(1−(a/c){1 − exp[b(x − x0)]}) using the parameters in Table 1.

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Figure 4. Cumulative respiration with respect to natural logarithm ofmoisture content for (a) sugar beet pulp, (b) tomato pomace and(c) fermented grape pomace. The curves represent y = aexp[b(x−x0)]/(1−(a/c){1 − exp[b(x − x0)]}) using the parameters in Table 2.

Table 1. Parameter estimatesa and R2 value for peak respiration datafitted to sigmoidal relationship y = aexp[b(x − x0)]/(1 − (a/c){1 −exp[b(x − x0)]})

b c

Feedstock a Value Error Value Error R2 value

Sugar beet pulp 0.15 3.99 0.08 111 5 0.99

Tomato pomace 0.64 4.32 0.38 275 31 0.96

Fermented grape pomace 3.15 2.63 0.87 60 15 0.80

a Units: a, mg CO2 day−1 g−1 dry biomass; b, unit-less; c, mg CO2day−1 g−1 dry biomass.

Table 2. Parameter estimatesa and R2 value for cumulative respira-tion data fitted to sigmoidal relationship y = aexp[b(x − x0)]/(1 −(a/c){1 − exp[b(x − x0)]})

b c

Feedstock a Value Error Value Error R2 value

Sugar beet pulp 0.09 4.94 0.23 327 24 0.98

Tomato pomace 0.1 7.68 0.30 424 20 0.99

Fermented grape pomace 1.8 2.8 0.35 260 59 0.92

a Units: a, mg CO2 g−1 dry biomass; b, unit-less; c, mg CO2 g−1 drybiomass.

Figure 5. Cumulative respiration associated with microbial activity ondifferent feedstocks with varying moisture content.

where y is the critical moisture content (% d.b.), assumed to bethe midpoint of the ranges given above, and x is the initial WSCcontent (% d.b). The R2 value for the relationship was 0.94.

The WSC content of rice straw was determined as 5.5–9.2% d.b.Using Eqn (3), the predicted critical moisture content is therefore29.9–34.0% d.b. This is within the range of critical moisturecontents reported for rice straw (29–40% d.b.).7

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Table 3. Composition of feedstocks (% d.b)

Feedstock Cellulose Hemicellulose LigninWater-solublecarbohydrate

Grape pomacea 9.2 4.0 11.6 44.6

Sugar beet pulp 19.4 23.3 1.1 5.0

Tomato pomace 15.5 16.2 25.3 3.2

Fermented grapepomace

14.5 17.1 10.3 1.9

a Grape pomace was not included in the analysis and conclusionsowing to its inconsistency while making different moisture contents.

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25

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35

40

45

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y = 16.0 + 18.9log(x) R2 = 0.94

Crit

ical

moi

stur

e co

nten

t (%

d.b

.)

WSC content (% d.b.)

Figure 6. Critical moisture content for each feedstock versus initial water-soluble carbohydrate (WSC) content.

DISCUSSIONMicrobial activity increased with increasing moisture content untilit reached a maximum, then declined with further increase inmoisture content. This decline was not observed in a previous studywith rice straw, where microbial activity increased with increasingmoisture content.7 However, it was mentioned previously thatthere is an upper limit of moisture content beyond which microbialactivity will not increase further owing to lack of substrate oroxygen limitations.7 Excess growth of mould on feedstocks atthe upper moisture content likely created a microenvironmentwith limited nutrition and oxygen mass transfer. These limitingconditions and lower mass transfer of oxygen may havesuppressed microbial activity at high moisture contents, and thismay have been more pronounced with higher-density food wastescompared with rice straw.

Peak respiration rate and cumulative respiration for FGP, SBPand TP followed the previously reported sigmoidal relationshipwith increasing moisture content.10 Rapid activity, indicated bya large peak in respiration and a large value of parameter c,would suggest a greater risk of thermal runaway during storagebecause of the difficulty in managing rapid heat generation in alarge pile of biomass. In this study the highest peak was predictedfor TP, which had the lowest hemicellulose content of the three

feedstocks tested. The lowest peak was predicted for FGP, whichhad the lowest WSC content. Parameter b represents the rateof change in peak activity with moisture. The similarity in thisparameter among feedstocks indicates that, once feedstocks wereabove their critical moisture content, peak respiration increasedin a similar manner with increasing moisture.

For cumulative respiration, parameter c was highest for TP andlowest for FGP. This result suggests that TP has higher potential forsustained decomposition and biomass deterioration when storedunder aerobic and high-moisture conditions. Also for cumulativerespiration, parameter b varied between feedstocks: TP and SBPhad the highest values. This finding indicates that the sustaineddecomposition of TP and SBP increases at a greater rate withincreasing moisture content and thus supports the susceptibility ofTP and SBP to long-term decomposition with increasing moisturecontent. The differences observed among different feedstocks arelikely due to differences in their plant cell wall composition andinitial WSC level.

Critical moisture contents above which micro-organisms wouldbecome active in stored feedstocks were also determined. Thecritical moisture content varied for different feedstocks. This vari-ation was likely due to differences in feedstock composition andpredominant microbial communities. Although critical moisturecontents have been identified for various agricultural residuessuch as hay and rice straw, no attempt has been made to predictthe critical moisture content from an easily measureable trait of thefeedstock. Microbial growth is affected by various environmentalfactors, including moisture content and WSC concentration. Highsugar concentration has been shown to be associated with lower-ing water activity and preventing microbial growth.11 Since sugarsare present in the aqueous phase, their concentration will increasewith decreasing moisture content.12 Hence, even in fresh materi-als in which micro-organisms can easily grow, at lower moisturecontent the higher WSC concentration might inhibit growth. Asimple empirical model (Eqn (3)) was obtained here that relatesthe initial WSC content of the feedstock to its critical moisturecontent. The relationship indicates that, as the WSC content in-creases, the feedstock will have an extended range of moisturecontents at which there is no or limited microbial activity. In thisempirical model, 94% of the measured value was described by thelogarithmic model for WSC levels between 2 and 50% d.b. Usingthe WSC content of rice straw and Eqn (3), the critical moisturecontent for rice straw was predicted and found to be comparableto that reported previously.7 To improve the model, more exper-imental data are needed for a variety of feedstocks with a widerrange of WSC contents. Examination of other components in feed-stocks that might degrade rapidly, such as pectin and starch, mayalso improve the prediction of the risk of biomass deteriorationduring storage.

CONCLUSIONAppropriate storage conditions such as ideal moisture contentand WSC content are needed to prevent deterioration ofpolysaccharides in feedstocks and to better manage feedstockhandling after harvest for year-round supply for bioenergyand biochemical production. Trends in microbial activity withincreasing moisture content varied with different feedstocks. Themoisture content threshold below which there was no or limitedmicrobial activity was determined for different feedstocks. Sincethe critical moisture content for different feedstocks was different,there was a need to have a simple relationship to be able to

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predict the critical moisture content based on feedstock initialcomposition. The WSC content of feedstocks appears to be areasonable metric for estimating the critical moisture content inthe range between 2 and 50% d.b. There is a need to test a widerrange of WSC levels to be able to confirm the validity of therelationship for a variety of feedstocks.

ACKNOWLEDGEMENTSSupport for this research was provided by Chevron TechnologyVentures. The authors would like to thank Dr Yi Zheng, ChaoweiYu, Yu-Shen Cheng and Todd Dooley for material collection andprocessing and Dr Amitha Reddy for assistance with respirationmeasurements and suggestions for the study.

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11 Chirife J, Herszage L, Joseph A and Kohn ES, In vitro study of bacterialgrowth inhibition in concentrated sugar solutions: microbiologicalbasis for the use of sugar in treating infected wounds. AntimicrobAgents Chemother 23:766–773 (1983).

12 Darbyshire B, Muirhead WA and Henry RF, Water-soluble polysaccha-ride in nine commercial sweet corn cultivars and its suitability forestimating kernel maturity. Aust J Exp Agric Anim Husb 19:373–376(1979).

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