Journal of the Science of Food and Agriculture J Sci Food Agric 85:757–769 (2005)DOI: 10.1002/jsfa.2042

Statistical analysis of effects of industrialprocessing steps on functional propertiesof pasteurised liquid egg whiteValerie Lechevalier,1∗ Emmanuel Perinel,2 Romain Jeantet,1 Capucine Lesaffre,1

Thomas Croguennec,1 Catherine Guerin-Dubiard1 and Francoise Nau1

1UMR Agrocampus Rennes-INRA Science et Technologie du Lait et de l’Œuf, CS 84215, 65 Rue de Saint Brieuc,F-35042 Rennes Cedex, France2UP Mathematiques Appliquees, CS 84215, 65 Rue de Saint Brieuc, F-35042 Rennes Cedex, France

Abstract: Egg white is widely used as an ingredient in the food industry owing to its excellent functionalproperties. The transformation of shell eggs into safe liquid, frozen, or spray-dried egg white with extendedshelf life requires many technological operations that result in modifications to the egg white’s functionalproperties. The present study highlights the critical steps affecting foaming and gelling properties duringa classical pasteurised liquid egg white process. The main source of variation in functional properties wasraw material quality, accounting for 70% of the variability. Part of the remaining 30% was explained bymechanical egg white–yolk separation, tank storage, pasteurisation and homogenisation that resulted indamage to the functional properties, whereas initial flow through pipes and pumping resulted in theirimprovement. The effects of these steps could be grouped according to the type of treatment undertaken.Dry matter content, pH and treatment intensity at each step contributed about 30% of the variabilityin functional properties due to processing steps. Relationships between the modifications of egg whitefunctional properties and protein conformation were established. Between 46 and 78% of the variability infunctional properties can be explained by protein denaturation, temperature and enthalpy changes. 2004 Society of Chemical Industry

Keywords: liquid egg white; shear rate; pasteurisation; homogenisation; interfaces; functional properties; proteinconformation

INTRODUCTIONEgg white has many useful functional properties suchas foaming, gelling, emulsifying, and binding adhesion.Because of these properties, egg white is a desirableingredient for many foods such as bakery products,meringues, meat products, cookies, etc. Industrialegg white processing involves many technologicaltreatments such as mechanical treatments (stirring,blending, pumping), thermal treatments (heating,cooling) as well as processing steps during which largeinterfacial areas are created, either with solid surfaces(transfer through pipes) or with gas phases (spraydrying). These treatments impair the egg white’sfunctional properties.1–3 Some of these treatmentshave been extensively studied, such as heating4–6

and spray drying.7,8 Functional properties dependon changes in egg white protein structure, essentiallydue to energy intake. Many studies have correlatedegg white functionalities with protein composition9,10

and also with modifications of protein structure.11,12

Most research has been performed on purified proteinon a laboratory scale. Hence the results of thesestudies were not directly transferable to complexindustrial situations. In contrast, the present studywas performed in an industrial plant using a complexprotein solution, liquid egg white. The aim of thisstudy was to assess the effects on the functionalproperties of each of the steps during a classicalpasteurisation process of liquid egg white. This studyalso aimed to identify relationships between functionalproperties and protein conformation.

MATERIALS AND METHODSSample collectionSamples were collected from a French egg productfactory (3 Vallees, Ambrieres les Vallees, France) ateach step during the pasteurisation process of liquid

∗ Correspondence to: Valerie Lechevalier, UMR Agrocampus Rennes-INRA Science et Technologie du Lait et de l’Œuf, CS 84215, 65 Ruede Saint Brieuc, F-35042 Rennes Cedex, FranceE-mail: lecheval@agrocampus-rennes.frContract/grant sponsor: Conseil Regional de BretagneContract/grant sponsor: Conseil Regional des Pays de la Loire(Received 10 October 2003; revised version received 13 June 2004; accepted 13 July 2004)Published online 14 December 2004

2004 Society of Chemical Industry. J Sci Food Agric 0022–5142/2004/$30.00 757

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Eggs

Breaking Packaging

Tank1 Pasteurizer/holder (56°C, 6 min)

Pump1 Homogenizer Homogenizer0 Pa

Filter Plate heat exchanger

Plate liquid cooler Pump3

Tank2 Tank3

Pump2 Filters

PIPES 1

PUMP 1

FILTER

COOLER

PIPES 2 PUMP 2

PASTEURIZATION

HOMOGENIZATION

PackagingMANUALBREAKING

MECHANICALBREAKING

5 106 Pa

TANKSTORAGE

Figure 1. Pasteurised liquid egg white process and sampling points (grey arrows). Step names are shown in bold.

Table 1. Information on eggs used

Trial 1 Trial 2 Trial 3

Hen breed ISA Brown ISA Brown ISA BrownHen age (weeks) 42 43 42Hen flock A A BEgg weight (g)a 65.78 ± 0.42 65.82 ± 0.21 65.83 ± 0.30Storage time between laying and breaking (days) 2–4 11–13 6–13pHa 9.11 ± 0.005 9.24 ± 0.001 9.24 ± 0.001Dry matter (g kg−1)a 115.9 ± 3.8 115.8 ± 4.9 116.8 ± 5.8

a Values are mean ± standard deviation.

egg white, from the initial breaking of the eggs tothe final packaging step. Process details and samplingpoints are given in Fig 1. Shell eggs were retained foruse as a ‘reference egg white’ and broken manually.Eleven steps were identified, some of which includedseveral unit operations as shown in Fig 1. The finalsteps involving ‘pasteurisation’ and ‘homogenisation’differed only in the pressure of 5 × 106 Pa appliedin the ‘homogenisation’ branch. No pressure wasapplied for the ‘pasteurisation’ branch. Informationregarding the shell eggs used is given in Table 1. Theexperimental design attempted to minimise, wherepossible, variations between the eggs used. Factorswhich were controlled included eggs from hens ofthe same breed, age and feeding, and of the sameweight, pH and dry matter content. All samplingand measurement of each functional property wereperformed in triplicate.

Type and intensity of treatment encountered byegg whiteFor each step the dry matter (DM) content and pHof the product were measured. The quantity andtype (mechanical or thermal) of energy transferredto the product and the nature and area of interfaces(air or stainless steel) encountered by the product

were calculated using the following equations, and theresults are given in Table 2.

Thermal energy (Q/m, J kg−1) was calculated asfollows:

Qm

= Cp�θ (1)

where Cp is the egg white specific heat capacity(3936 J kg−1 K−1),13 which is considered constant overthe temperature range used in this study, and �θ isthe temperature increase during passage through theheat exchanger (K).

Mechanical energy (Q/m, J kg−1) was calculated asfollows:

Qm

= �Pρ

(2)

where �P is the differential pressure (Pa) and ρ is theegg white density (1035 kg m−3).14

Stainless steel–product interfaces for pipes werecalculated as follows:

SV

= 2πRL

πR2L= 2

R(3)

where S is the surface area of stainless steel cylindricalpipes (m2), V is the volume of product in contact with

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Table 2. Dry matter (DM) and pH of egg white, type and intensity of energy transferred to egg white, nature and area of interface encountered by

egg white and presence/absence of shear for each step

Measurements Theoretical calculations

Step DM (g kg−1)a pHa Energy (J kg−1) Energy type Interface (m2 m−3) Interface type Shearb

Mechanical breaking 124 ± 2 9.23 ± 0.10 0 — 0 — −Pipes 1 118 ± 3 9.21 ± 0.07 0 — 54 Stainless steel −Pump 1 118 ± 1 9.24 ± 0.06 193 Mechanical 65 Stainless steel +Filter 118 ± 2 9.23 ± 0.06 0 — 5000 Stainless steel +Cooler 117 ± 1 9.25 ± 0.06 0 — 652 Stainless steel +Pipes 2 116 ± 1 9.22 ± 0.05 0 — 80 Stainless steel −Tank storage 111 ± 3 9.19 ± 0.07 0 — ND Air −Pump 2 111 ± 3 9.16 ± 0.10 434 Mechanical 10 161 Stainless steel +Pasteurisation 112 ± 1 9.15 ± 0.05 211 750 Thermal 2 292 Stainless steel +Homogenisation 111 ± 3 9.15 ± 0.06 4 830 Mechanical 0 — +a Values are mean ± standard deviation.b +, present; −, absent.ND, not determined.

stainless steel (m3), R is the pipe radius (m) and L isthe pipe length (m).

Stainless steel–product interfaces for plate sectionswere calculated as follows:

SV

= 2L(l + e)Lle

= 2(l + e)le

(4)

where S is the surface area of stainless steel (m2), Vis the volume of product in contact with stainless steel(m3), L is the plate length (m), l is the plate width (m)and e is the channel thickness between two plates (m).Equation (4) can be simplified for heat exchangerswhere l � e; hence

SV

= 2e

(5)

The air–product interface was taken into accountonly for tank storage, as this was the only step involvingprolonged contact between air and product and henceconsidered to be significant. A Reynolds numbergreater then 105 during continuous stirring of theproduct in the storage tank was calculated (data notshown), indicating turbulent flow. This implies thatall of the product was in contact with the air at somestage during storage and hence must be considered.

Functional properties

Foaming propertiesFoam density was calculated from the mass of a givenvolume of foam, and foam stability as the percentageof liquid held in the foam after 1 h, as suggested byNau et al.15 Foam was obtained at room temperatureby whipping 200 g of egg white in a Hobart N-50 mixer(Hobart Foster, Kampenhout, Belgium) at speed 3 for2 min.

Gelling propertiesEgg white was poured into 2.9 cm diameter (d) and45 cm high plastic tubes (Krehalon, Deventer, The

Netherlands), sealed and tempered in a water bath for1 h at 80 ◦C. Sample containers were then cooledto room temperature for at least 4 h. The tubeswere removed and the egg white gels were cut intosections of 2 cm (h) using two parallel metal wires.Gel rheological measurements were performed using aTA-XT2 texture analyser (Rheo, Champlan, France).Small cylinders were compressed at 1 mm s−1 with a1.7 cm diameter sensor to a depth of 1.5 cm. The force(N) and distance (mm) applied for gel breakdownwere called ‘gel breaking strength’ and ‘gel breakingdistance’ respectively. Gel resistance to the strain (Pa)was deduced from the measurement of gel breakingforce versus distance, as explained by Hammershojet al.16 Gel water retention capacity was calculatedfrom the gel water release, as described by Croguennecet al.3 A section of gel was placed vertically on apreviously weighed plate containing a 90 mm diameterWhatman filter paper (Whatman 541, Polylabo,France): mass M1. The TA-XT2 5 cm diameter platewent down at a constant speed of 1 mm s−1 to adistance of 5 mm and the deformation was maintainedfor 30 s. After compression the plate and the wetWhatman filter paper were weighed again: mass M2.Water release (WR, kg m−3) was calculated as follows:

WR = M2 − M1

π(d/2)2h(6)

Differential scanning calorimetry (DSC)DSC was performed using a Q1000 differentialscanning calorimeter (TA Instruments, Paris, France).Hermetic stainless steel pans were used for samplemeasurements. A 90 µl sample was pipetted intothe pan, and water was used in the referencepan. Pans were accurately weighed before and aftertreatment using a BP121S four-point calibrationbalance (Sartorius, Gottingen, Germany). The heatingrate was 2 ◦C min−1 and the temperature wasscanned from 4 to 120 ◦C. The peak endothermtemperatures (◦C) and the denaturation enthalpies

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(J g−1) for ovalbumin (ova), S-ovalbumin (S-ova) andovotransferrin (ovt) were measured.

Statistical analysisAn experimental design which incorporated the 11steps and three trials was used for analysis of theresponse variables relating to the functional propertiesof egg white. Analysis of variance (ANOVA) of theexperimental design was carried out using a multi-way ANOVA procedure of STATGRAPHICS Plus

(Manugistics, Rockville, MD, USA) where the effectsof steps, trials and interaction between trials and stepswere estimated for all response variables. The Stu-dent’s t test was used as a guide for pair comparisonsof the trial means. Differences among trials that aredescribed subsequently as being significant were deter-mined at P < 0.05. Response variables which differedsignificantly between trials were subsequently analysedusing principal component analysis (PCA). PCA wascarried out using SPAD (Decisia, Pantin, France).

To minimise the trial effect, data were centredand reduced for each trial. The resulting valuesare termed ‘treated data’ for the remainder of thestudy. ANOVA of these treated data was used toestimate the effects of steps, trials and interactionbetween trials and steps for each of the responsevariables. The Student’s t test was used as a guidefor pair comparisons of the step means. Differencesamong steps that are described subsequently as beingsignificant were determined at P < 0.05. Responsevariables which differed significantly between stepswere subsequently analysed using PCA. Responsevariables which discriminated significantly betweensteps were also analysed by hierarchical cluster analysis(HCA) carried out using SPAD.

Supplementary variables highlight subgroups ofsteps defined by common treatment: energy, interfacesand shear rates. Multiway ANOVA was used to testthe effect of these treatments for each functionalproperty. Multiple linear regression analysis (MLRA)was performed to link the intensity of these treatmentsas well as pH and DM content to functional properties.MLRA was carried out using STATGRAPHICSPlus. Response variables which differed significantlybetween treatments were analysed using PCA.

MLRA was performed to link denaturation tem-peratures and enthalpies of ova, S-ova and ovt tofunctional properties. These variables were also anal-ysed using PCA.

RESULTSResults for functional properties of each processingstep for each trial are given in Table 3. Protein denatu-ration temperature and enthalpy results obtained fromDSC analysis are given in Table 4.

Influence of trial effectANOVA of the raw data (Table 5) showed significantdifferences due to trial, step and the interaction of trial

and step for foaming properties, gel breaking strengthand gel strain resistance. In the case of gel waterrelease and gel breaking distance, step effects were notsignificantly different. These results show that, despiteprecautions undertaken to minimise variation betweenraw materials for each trial, data were nonethelesssignificantly different from one trial to another.

PCA of the data is shown in Fig 2A. The first twoprincipal components (PCs) explained a cumulativevariation of 70%. PC1 (41%) was correlated withfoam density, gel breaking strength and distance andgel water release, and PC2 (29%) with foam stabilityand gel strain resistance (Fig 2B). PC1 separated trials1 and 2 (positive axis) from trial 3 (negative axis). Themain difference between trials 1 and 2 and trial 3was the flock of hens used (Table 1). Hence PC1could be associated with the hens used, despite similarbreed, diet and age. The eggs from flock A gave lessdense foams and gels with higher breaking strengthand distance but lower gel water retention capacity.PC2 separated trial 1 (positive axis) from trials 2and 3 (negative axis). Depending on the trial, eggs

Figure 2. PCA showing first two principal components of functionalproperties on raw data: A, similarity map determined by PCs 1 and 2for samples (circles, trial 1; squares, trial 2; triangles, trial 3); B,correlation circle of continuous variables (functional properties) forPCs 1 and 2.

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Table 3. Functional property resultsa for each processing step and each trial

Step/trial

Foamdensity(g l−1)

Foamstability

(%)

Gel waterrelease

(kg m−3)

Gel breakingstrength

(N)

Gel breakingdistance

(mm)

Gel strainresistance

(Pa)

Manual breakingTrial 1 96.6 ± 9.8 48.1 ± 8.4 6.1 ± 0.2 9.3 ± 0.6 8.8 ± 1.0 1090.8 ± 33.0Trial 2 95.0 ± 3.9 63.7 ± 5.5 6.9 ± 0.8 11.8 ± 0.1 9.1 ± 0.1 1401.7 ± 3.3Trial 3 84.9 ± 6.1 57.7 ± 2.3 5.5 ± 0.9 9.1 ± 0.9 7.7 ± 0.9 1240.0 ± 152.2

Mechanical breakingTrial 1 118.9 ± 4.3 38.7 ± 3.2 5.7 ± 0.4 9.5 ± 0.4 7.5 ± 0.4 1282.6 ± 70.0Trial 2 92.5 ± 4.7 62.4 ± 2.9 7.2 ± 1.0 10.7 ± 0.5 8.8 ± 0.7 1248.1 ± 57.8Trial 3 109.7 ± 4.9 55.4 ± 2.6 5.4 ± 0.1 7.6 ± 0.8 7.5 ± 0.8 1107.7 ± 107.6

Pipes 1Trial 1 113.9 ± 6.5 44.8 ± 11.1 6.3 ± 0.2 9.4 ± 1.3 8.0 ± 1.5 1129.2 ± 172.3Trial 2 87.7 ± 9.4 60.8 ± 3.5 8.9 ± 0.9 9.9 ± 0.7 9.1 ± 0.4 1214.4 ± 63.1Trial 3 107.9 ± 3.9 50.6 ± 6.2 2.6 ± 1.7 6.8 ± 0.3 6.8 ± 0.04 1057.8 ± 43.7

Pump 1Trial 1 97.6 ± 4.9 49.6 ± 5.0 6.1 ± 0.3 10.9 ± 0.7 9.5 ± 0.5 871.9 ± 39.2Trial 2 93.3 ± 7.3 59.5 ± 7.2 9.4 ± 0.4 9.6 ± 0.2 8.6 ± 0.5 1212.9 ± 116.8Trial 3 105.2 ± 3.3 51.6 ± 5.5 2.4 ± 1.8 6.7 ± 0.3 6.5 ± 0.6 1137.3 ± 80.7

FilterTrial 1 100.5 ± 3.4 45.8 ± 1.7 5.5 ± 0.3 9.9 ± 0.6 9.0 ± 0.8 724.8 ± 93.9Trial 2 99.3 ± 2.2 58.6 ± 3.8 9.9 ± 0.6 10.1 ± 0.4 8.7 ± 0.6 1193.6 ± 158.4Trial 3 100.0 ± 6.1 60.4 ± 3.0 4.6 ± 0.3 7.7 ± 1.0 8.0 ± 1.2 1141.8 ± 95.9

CoolerTrial 1 96.2 ± 2.7 49.2 ± 3.3 5.7 ± 0.6 10.8 ± 0.4 9.4 ± 0.1 813.5 ± 149.2Trial 2 95.7 ± 5.1 52.8 ± 4.1 10.8 ± 0.9 9.8 ± 0.1 8.8 ± 0.2 1192.6 ± 118.0Trial 3 102.7 ± 2.0 53.6 ± 2.4 4.4 ± 0.9 7.2 ± 0.4 6.7 ± 0.1 1218.2 ± 35.6

Pipes 2Trial 1 98.9 ± 0.0 46.1 ± 0.0 5.3 ± 0.6 10.8 ± 0.3 9.4 ± 0.4 815.5 ± 19.7Trial 2 103.2 ± 1.1 58.4 ± 3.1 11.6 ± 2.0 10.0 ± 0.5 9.1 ± 0.9 1104.3 ± 203.3Trial 3 99.6 ± 1.7 52.7 ± 2.5 1.3 ± 0.7 7.1 ± 0.6 6.8 ± 0.8 1122.1 ± 33.9

Tank storageTrial 1 100.3 ± 3.9 43.7 ± 3.3 6.0 ± 0.4 9.9 ± 0.2 9.2 ± 0.2 796.8 ± 40.7Trial 2 103.7 ± 2.2 53.1 ± 2.1 10.7 ± 1.0 8.6 ± 0.5 8.1 ± 0.4 1242.2 ± 95.6Trial 3 98.8 ± 2.5 50.5 ± 1.6 3.5 ± 1.3 6.5 ± 0.4 7.9 ± 0.8 980.8 ± 28.1

Pump 2Trial 1 94.3 ± 1.2 48.5 ± 1.3 6.4 ± 0.3 10.0 ± 0.2 9.6 ± 0.8 800.5 ± 227.7Trial 2 103.5 ± 4.9 50.9 ± 1.8 11.4 ± 0.6 8.9 ± 0.3 8.5 ± 0.1 1237.6 ± 132.9Trial 3 101.1 ± 1.0 50.0 ± 1.3 3.2 ± 2.5 6.8 ± 0.8 7.5 ± 1.2 1092.1 ± 9.0

PasteurisationTrial 1 112.4 ± 1.7 43.5 ± 1.2 5.1 ± 0.6 9.5 ± 0.1 9.2 ± 0.3 679.8 ± 9.6Trial 2 92.4 ± 3.0 50.6 ± 2.1 11.9 ± 1.7 8.6 ± 0.3 8.2 ± 0.2 1153.1 ± 39.2Trial 3 116.9 ± 1.2 49.2 ± 3.6 2.5 ± 2.1 7.7 ± 0.5 7.9 ± 0.4 1147.8 ± 20.5

HomogenisationTrial 1 119.0 ± 2.9 45.3 ± 3.2 5.3 ± 0.7 9.4 ± 0.5 9.2 ± 0.7 635.1 ± 179.3Trial 2 73.1 ± 6.8 53.2 ± 0.7 13.7 ± 1.8 8.8 ± 0.1 9.2 ± 0.4 894.7 ± 198.7Trial 3 121.7 ± 6.5 46.6 ± 1.9 3.6 ± 2.3 7.6 ± 0.8 8.1 ± 0.7 1069.3 ± 74.6

a Values are mean ± standard deviation.

were not stored for the same length of time beforebreaking (Table 1). Hence PC2 could be associatedwith storage time, as eggs of trial 1 were stored for ashorter period than those of trials 2 and 3. Increasesin egg storage time resulted in increased foam stabilityand gel strain resistance.

To eliminate the trial effect and focus purely onthe processing step effect, all data were centred andreduced. Results of ANOVA of the treated data aregiven in Table 6. A step effect was thus highlighted forboth foam density and stability and for gel breakingstrength and gel strain resistance. No step effectwas found for gel breaking distance or gel water

release. However, the interaction between step andtrial remained significantly different. In the followingsubsections, gel breaking distance and gel water releasewere not considered further.

Influence of processing stepsPCA was repeated excluding data on gel breakingdistance and gel water release, as these effects werenon-significant.

PC1, explaining 76% of the variability, was stronglycorrelated with foaming and gelling properties (r >

0.78) (Fig 3B). PC2, which accounted for only 14% ofthe variability, was correlated with foam density (r =

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Table 4. Differential scanning calorimetry results for each processing

step and each trial: temperature (T) and enthalpy (H) of denaturation

of ovalbumin (ova), S-ovalbumin (S-ova) and ovotransferrin (ovt)

Step/trialTova

(◦C)Hova

(J g−1)TS−ova

(◦C)HS−ova

(J g−1)Tovt

(◦C)Hovt

(J g−1)

ManualbreakingTrial 1 80.62 1.15 84.88 0.01 66.24 0.29Trial 2 79.85 1.01 84.65 0.01 66.16 0.31Trial 3 80.98 0.49 85.11 0.02 66.97 0.22

MechanicalbreakingTrial 1 80.51 1.08 87.69 0.07 66.22 0.29Trial 2 80.39 1.02 88.31 0.07 66.65 0.29Trial 3 80.63 0.84 85.13 0.03 66.54 0.28

Pipes 1Trial 1 80.62 1.06 88.10 0.10 66.36 0.28Trial 2 79.68 1.07 87.03 0.05 65.91 0.29Trial 3 80.52 0.69 85.27 0.02 66.59 0.26

Pump 1Trial 1 80.65 1.04 88.07 0.08 65.81 0.25Trial 2 79.69 1.01 84.5 0.001 65.95 0.29Trial 3 80.53 0.70 85.34 0.01 66.66 0.26

FilterTrial 1 80.65 1.10 87.98 0.05 64.34 0.24Trial 2 80.26 0.96 87.62 0.04 66.45 0.28Trial 3 80.78 0.80 85.32 0.02 66.36 0.27

CoolerTrial 1 80.81 1.04 87.34 0.004 63.19 0.11Trial 2 80.21 0.98 87.77 0.06 66.52 0.29Trial 3 81.15 0.87 85.59 0.02 66.30 0.26

Pipes 2Trial 1 81.02 0.98 88.75 0.07 66.53 0.26Trial 2 79.78 0.91 84.77 0.02 65.94 0.26Trial 3 80.39 0.70 85.05 0.02 66.43 0.26

Tank storageTrial 1 80.86 1.00 88.04 0.05 65.90 0.25Trial 2 80.25 0.92 87.67 0.05 66.50 0.26Trial 3 80.66 0.76 85.25 0.03 66.30 0.27

Pump 2Trial 1 81.00 0.95 88.32 0.01 66.15 0.22Trial 2 80.29 0.89 87.89 0.07 66.54 0.26Trial 3 80.68 0.73 85.15 0.02 65.86 0.27

PasteurisationTrial 1 80.66 0.89 88.38 0.05 66.67 0.25Trial 2 80.31 0.90 87.97 0.07 66.56 0.27Trial 3 80.39 0.69 85.07 0.03 66.49 0.27

HomogenisationTrial 1 80.82 0.67 85.6 0.02 66.98 0.22Trial 2 80.22 0.96 88.06 0.08 66.46 0.27Trial 3 80.34 0.63 84.93 0.03 66.43 0.26

0.59). Separation between the individual processingsteps is shown in Fig 3A. HCA identified four differentgroupings of the processing steps, as indicated by thedifferent symbols used. Manual breaking was locatedwell away from the other clusters. As shown in Table 7,manual breaking scored highest for foam stability andgel breaking strength and lowest for foam densitycompared with all other processing steps.

As manual breaking was significantly different, PCAwas again repeated without these data (Fig 4) to

Table 5. Effects of sampling trial and processing step on egg white

functional properties with raw data: Fisher ratio (first entry) and P

value (second entry) obtained from ANOVA (n = 99)a

Functional property Trial Step Trial × step

Foam density 50.7 7.9 14.7<10−4 <10−4 <10−4

Foam stability 61.1 4.1 2.34<10−4 0.0002 0.0052

Gel water release 286.0 1.8 5.9<10−4 0.0755 <10−4

Gel breaking strength 216.0 7.5 5.3<10−4 <10−4 <10−4

Gel breaking distance 50.9 1.4 2.3<10−4 0.1915 0.0070

Gel strain resistance 76.7 8.6 3.6<10−4 <10−4 <10−4

a Significant effects (P ≤ 0.05) are shown in bold type.

Table 6. Effects of sampling trial and processing step on egg white

functional properties with treated data: Fisher ratio (first entry) and P

value (second entry) obtained from ANOVA (n = 99)a

Functional property Trial Step Trial × step

Foam density 0 7.4 14.71.0 <10−4 <10−4

Foam stability 0 4.1 2.321.0 0.0002 0.0057

Gel water release 0 1.3 4.61.0 0.2591 <10−4

Gel breaking strength 0 5.9 4.81.0 <10−4 <10−4

Gel breaking distance 0 1.2 2.31.0 0.2773 0.0065

Gel strain resistance 0 6.8 2.71.0 <10−4 0.0015

a Significant effects (P ≤ 0.05) are shown in bold type.

highlight differences between the remaining steps.PC1, explaining 61% of the variability, was correlatedwith foam stability (r = −0.94), gel breaking strength(r = −0.86) and gel strain resistance (r = −0.74)(Fig 4B). PC2, explaining 24% of the variability, wasstrongly correlated with foam density (r = −0.83).As before, HCA identified different groupings of theprocessing steps, as indicated by the different symbolsused. Processing steps clustered in different groups aresignificantly different, as highlighted by the Student’st test shown in Table 7. Hence it is possible to identifythe critical steps responsible for significant changes inegg white functional properties, along the processingline: pipes 1, pump 1, tank storage and pasteurisation,these being grouped in different clusters.

The Student’s t test specified the significance ofimpact of each processing step on each functionalproperty (Table 7). The ‘total’ variation was calcu-lated between the mechanical breaking step and boththe pasteurisation and homogenisation steps. Foamdensity was considered not to be significantly modi-fied between the beginning and end of the processing

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Functional properties of pasteurised liquid egg white

Figure 3. PCA showing first two principal components of functional properties on treated data: A, similarity map determined by PCs 1 and 2 forprocessing steps; B, correlation circle of significant continuous variables (functional properties) for PCs 1 and 2. Steps with different symbolsbelong to different clusters determined by HCA.

Table 7. Variation in functional properties expressed as percentage of value obtained for immediately previous stepa

StepFoam

densityFoam

stabilityGel breaking

strengthGel strainresistance

Manual breaking Ref Ref Ref RefMechanical breaking +16.1∗∗∗ −7.7∗ −7.8∗ —Pipes 1 — — −6.8∗ —Pump 1 −4.3∗ — — —Filter — — — —Cooler — — — —Pipes 2 — — — —Tank storage — — −10.6∗∗∗ —Pump 2 — — — —Pasteurisation +7.6∗∗∗ — — —Homogenisation — — — −12.8∗∗

Total — −8∗ −7∗ −18∗ (pasteurisation) to −29∗∗∗ (homogenisation)

a Ref, reference. Student’s t test significant at level ∗ 0.05, ∗∗ 0.01 or ∗∗∗ 0.001; —, non-significant.

line. Initially, foam density between mechanical break-ing and pump 1 decreased by 4.3%. However, afterpasteurisation, foam density increased by 7.6%, thusminimising differences over the entire process. Foamstability decreased by 8% between the start andend of the processing line. However, this decreasewas progressive and none of the steps was signifi-cantly different (P < 0.05). Gel breaking strength alsodecreased along the processing line by 7%. Tank

storage accounted for the largest decrease (10.6%).Gel strain resistance decreased progressively by 18%between mechanical breaking and pasteurisation with-out homogenisation. Including the homogenisationstep, gel strain resistance decreased by 29%.

Influence of type and intensity of treatmentFigures 5A and 5B show the plots obtained fromPCA of type and intensity of treatment respectively.

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Figure 4. PCA showing first two principal components of functional properties on treated data without manual breaking step: A, similarity mapdetermined by PCs 1 and 2 for processing steps; B, correlation circle of significant continuous variables (functional properties) for PCs 1 and 2.Steps with different symbols belong to different clusters determined by HCA.

ANOVA of the data is shown in Table 8. Foam densitywas significantly higher after thermal treatment thanafter mechanical treatment or no treatment. Foamstability was significantly lower after thermal treatmentthan after no treatment. As pasteurisation is the mainthermal processing step, these results reflect the effectof pasteurisation on foaming properties. On the otherhand, gel breaking strength was significantly decreasedby both mechanical and thermal treatments.

The effect of interface type differs depending onthe functional property considered. Foam densitydecreased after exposure to a stainless steel interface.Foam stability and gel strain resistance were signifi-cantly decreased by air interface exposure comparedwith stainless steel interface exposure. Gel breakingstrength was significantly decreased after air interfaceexposure compared with both stainless steel interfaceand no interface exposure.

Shear rate had no significant effect on any of thefunctional properties measured.

Table 8. Effects of energy, interface and shear rate applied to egg

white during processing on egg white functional properties.

Treatments with different letters are statistically different, the degree

of significance being given by the number of asterisksa

Foamdensity(g l−1)

Foamstability

(%)

Gel breakingstrength

(N)

Gel strainresistance

(Pa)

EnergyNo energy 101.9a 51.3b 8.9b 1038.5aMechanical 102.1a 49.0ab 8.4a∗ 994.4aThermal 110.1b∗ 45.7a∗∗ 8.3a∗ 984.6a

InterfaceNo interface 108.5b 48.8ab 8.8b 1028.7abStainless steel 103.2a∗ 50.5b 8.8b 1055.2bAir 102.3ab 46.8a∗ 8.0a∗∗ 933.6a∗

Shear rateNo shear rate 106.1a 48.3a 8.4a 1046.4aShear rate 103.3a 49.0a 8.6a 965.0a

a Student’s t test significant at level ∗ 0.05 or ∗∗ 0.01.

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Figure 5. PCA showing first two principal components of functionalproperties, pH, DM and intensity of treatment on type of treatment: A,similarity map determined by PCs 1 and 2 for type of treatment; B,correlation circle of significant continuous (functional properties) andillustrative (pH, DM, energy, interfacial area) variables for PCs 1 and 2.

Table 9. Equation coefficients and model significance for impact of

pH, dry matter (DM), energy and interfacial area (IA) on functional

properties obtained by multiple linear regression analysis (n = 99).

The number of asterisks gives the significance of each coefficienta

Foamdensity

Foamstability

Gel breakingstrength

Gel strainresistance

pH 0.51∗∗∗ 0.71∗∗∗DM 0.35∗∗∗Energy 3.22∗∗ 3.09∗IA −6.87∗∗ −6.63∗pH × DM −0.48∗∗ 0.34∗∗ −0.38∗∗pH × energy −0.16∗pH × IA −0.27∗DM × energyDM × IA −0.29∗ −0.30∗∗Energy × IA −20.2∗∗ −20.0∗Constant 1.12 −0.22 0.81 0.09

P 0.0035 <10−4 0.0001 <10−4

R2 (%) 18.6 24.9 25.6 28.8

a Student’s t test significant at level ∗ 0.05, ∗∗ 0.01 or ∗∗∗ 0.001.

Figure 5B shows the contribution of variablespH, DM, interfacial area and energy to egg whitefunctional properties. pH and DM were positivelycorrelated with foam stability and gel firmness.However, the quantity of energy applied to theproduct was positively correlated with foam density.No conclusion can be drawn from interfacial area,as this variable was not well correlated with anyproperty, in particular in the plane described byPC1 versus PC2. The relationship between variablesand functional properties was clarified by MLRA(Table 9). The models obtained explained between 19and 29% of the variability depending on the functionalproperty considered. Foam density increased withthe application of energy to egg white. On theother hand, foam density decreased with increasinginterfacial area. Foam density also decreased as theinteraction between pH and DM, between DM andinterfacial area or between energy and interfacialarea increased. Foam stability decreased as pH orthe interaction between pH and DM decreased. Gelbreaking strength increased with the application ofenergy to egg white. On the other hand, gel breakingstrength decreased with pH decrease, interfacial areaincrease or an increase in the interaction between pHand interfacial area or between energy and interfacialarea. Gel strain resistance decreased with DM decreaseor an increase in the interaction between pH and DM,between pH and energy or between DM and interfacialarea.

Correlation between protein conformation andfunctional propertiesOva, S-ova and ovt conformational changes in eggwhite were determined by the temperature andenthalpy of denaturation. Changes in denaturationtemperature highlight modifications to the nativeconformation of the protein. Changes in denaturationenthalpy may be caused by the same phenomenon,but also by protein concentration changes due topartial precipitation of proteins onto pipes, plateheat exchangers, etc. Figure 6 shows the correlationbetween these measurements and egg white functionalproperties. Ova and S-ova denaturation temperatureswere not well correlated with any egg white functionalproperty in the plane described by PC1 versusPC2. Thus no conclusion can be drawn fromthese two measurements. On the other hand, foamdensity was positively correlated with ovt denaturationtemperature and S-ova denaturation enthalpy. Gelproperties were positively correlated with ova and ovtenthalpies of denaturation.

Variations in functional properties were linked to eggwhite protein conformational changes using MLRA,which explained between 46 and 78% of the varia-tion (Table 10). Variations in foam density were duemainly to ova and ovt conformational changes. Inter-actions between ova and S-ova conformations andbetween ovt and S-ova conformations also explainedpart of the foam density modifications. Variations in

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Figure 6. Correlation circle of continuous (functional properties) andillustrative (protein denaturation temperatures and enthalpies)variables for PCs 1 and 2.

foam stability were explained by modifications of ovaand S-ova denaturation enthalpies. Variations in gelbreaking strength were due mainly to ovt conforma-tional changes, as well as simultaneous modificationsof ova and S-ova denaturation temperatures. The bestmodel was obtained for gel strain resistance, but nearlyall principal factors and many interactions were sig-nificant. Ova, S-ova and ovt conformational changeswere thus implied in gel strain resistance variations.

DISCUSSIONInfluence of trial effectThe influence of hen egg production on egg whitecomposition and functional properties has been knownfor many years.17 Many factors may be responsiblefor these changes, but especially hen age and eggweight. In this study, these two parameters werewell controlled and did not change from one trialto another. On the other hand, the flock effect couldnot be controlled. Such effects have been described bymany authors.16,18,19

Storage time is another source of variation inegg white functional properties. This parameter isimportant owing to the many modifications that occurduring this period. Firstly, the loss of CO2 throughthe egg shell results in an increase in pH duringthe first 3 days of storage.17 However, in this study,no significant pH differences between sampling trialswere detected. The pH of all samples had alreadyreached 9.1. Another known modification is egg whiteliquefaction, described as being strongly correlatedwith increases in pH, resulting in foam densitydecrease.15,18 The results of this study agree with thoseauthors, since, in trial 2 (longer storage of shell eggs),foam density was 9.5% lower than that in both trials 1and 3 (P < 0.001). In this study, however, the majoreffect of storage time was on foam stability (ratherthan foam density), which increased by 12.5 and 7.3%

Table 10. Equation coefficients and model significance for impact of

protein denaturation temperature and enthalpy on functional

properties obtained by multiple linear regression analysis (n = 99).

The number of asterisks gives the significance of each coefficienta

Foamdensity

Foamstability

Gel breakingstrength

Gel strainresistance

Tova −0.31∗ −0.83∗∗∗Hova −0.81∗∗∗ 0.45∗∗∗TS−ova 1.19∗∗∗HS−ova −0.25∗ −0.93∗∗∗Tovt 0.50∗∗Hovt 0.63∗∗ 0.34∗∗ 0.47∗∗Tova × Hova −1.84∗∗∗TS−ova × HS−ovaTovt × Hovt 0.34∗∗∗ −0.89∗∗∗Tova × TS−ova 0.21∗Tova × HS−ova 0.53∗Hova × TS−ova 0.20∗ 1.49∗∗∗Hova × HS−ova −0.52∗ −2.59∗∗∗Tova × Tovt −0.62∗Tova × Hovt

Hova × Tovt 1.73∗∗∗Hova × Hovt

Tovt × TS−ovaTovt × HS−ovaHovt × TS−ovaHovt × HS−ova 0.39∗ 1.88∗∗∗Constant −0.20 0.06 −0.15 0.02P 0.0007 0.0002 0.0013 0.0048R2 (%) 62.3% 49.0% 46.1% 78.5%

a Student’s t test significant at level ∗ 0.05, ∗∗ 0.01 or ∗∗∗ 0.001.

between trials 1 and 3 and between trials 3 and 2respectively (P < 0.001). The results in the literaturefor this property are ambiguous.15,18,20,21 Increases ingel strain resistance of 21.7% (P < 0.001) and 6.0%(P < 0.01) were also observed between trials 1 and 3and between trials 3 and 2 respectively. These resultsagree with Nau et al15 and Hammershoj et al.21 Thisstudy highlights the fact that much research remainsto be done to identify the main parameters responsiblefor raw material variability.

Influence of processing steps on functionalpropertiesThebaudin and Lefebvre22 showed differencesbetween functional properties due to manual ormechanical egg white breakage. Many hypotheses havebeen proposed, eg higher contamination of egg whiteby egg yolk, or thin/thick egg white ratio modifications.The first hypothesis is unlikely, since egg yolk spotsare visible in egg white and hence, if sufficient careis used in sampling, can be minimised or eliminated.The second hypothesis is perhaps more likely, as eggproduct manufacturers admit that mechanical break-ing does not completely separate thick egg white fromthe yolk. One of the main differences between the twotypes of egg white is the content of ovomucin, whichis four times greater in thick egg white than in thin eggwhite.23 Kato24 showed that the addition of ovomucinimproved egg white foaming properties. In this study

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the difference between the two types of breaking wasless significant for trial 2 (data not shown). This resultreinforces the second hypothesis, as a long storagetime decreases the thick egg white content. However,further research to optimise the thin/thick egg whiteratio in mechanical breaking is necessary.

In agreement with observations of Cunningham25

and Baniel et al,26 foam properties (inverse of foamdensity and foam stability) and gelling properties(gel breaking strength and gel strain resistance) werepositively correlated. All decreased as both pH andDM decreased along the processing line. However,variations in pH along the processing line were verysmall (≤0.1). Previous studies of the pH effect onegg white functional properties were carried out usinglarger pH variations.27–31 A loss of gel strength and anincrease in foam density and stability were describedwith decreases in pH. Variations in DM were moreextreme, with a loss of 13 g l−1 between the beginningand end of the process. The effects of these variationson functional properties are hence not surprising.

One of the treatments most damaging to eggwhite foaming and gelling properties is thermaltreatment (ie pasteurisation). Many authors havepreviously described the increase in foam densitydue to thermal treatment. Lorient et al32 found thatheat treatment of egg white at 53 ◦C for 2 min,milder conditions than used in this study, increasedfoam density. Garibaldi et al5 attributed the loss ofegg white foaming properties after pasteurisation tothe irreversible denaturation of ovomucin–lysozymecomplex by heat treatment. However, the DSCresults suggest that foam density increased when ovadenaturation temperature and enthalpy decreased orovt denaturation enthalpy increased. Pasteurisationinduced the unfolding of ova and the aggregation of ovtas suggested by the DSC results. Aggregate formationdue to egg white thermal treatment has been describedpreviously.33 The loss of soluble proteins, due toprotein denaturation, is responsible for increases infoam density.34

The observed effect of thermal treatment was asexpected. However, the deterioration of functionalproperties such as foam stability and gelling propertiesby air–product interface interaction was unexpected(Table 8). Lechevalier et al35 showed that isolatedova and ovt, the two major egg white proteins, weredenatured after contact with an air–product interface,and that ova aggregated through intermoleculardisulfide bond creation. When both proteins arepresent, aggregation of ova, ovt and ova with ovt canoccur, as in the case of thermal treatment.36 Proteinaggregation due to protein denaturation after contactwith an air–product interface is thus responsiblefor the loss of foam stability and gel firmness.Gel formation, rather than coagulum formation,depends on the relative rates of protein unfolding andaggregation.37 If aggregation occurs before unfolding,then an unstable fragile coagulum is formed.

The decrease in foam density after contact at astainless steel–product interface was also unexpected.This treatment also decreased gel breaking strengthand gel strain resistance. Various protein structuralmodifications occur after contact with a stainlesssteel surface and have been described previously, butthey depend on the protein considered. Omanovicand Roscoe38 showed the partial unfolding of β-lactoglobulin whereas Rubio et al39 showed a morecompact form of bovine serum albumin due toadsorption onto stainless steel surfaces. Each egg whiteprotein has its own functional properties dependingon the degree of protein denaturation. However, fewstudies regarding stainless steel interfacial effects onprotein conformational changes have been reported.Further research on this topic would be of interest.

Shear rate effects did not significantly modify func-tional properties (Table 8). However, pump 1 signifi-cantly decreased foam density, while homogenisationstrongly decreased gel strain resistance (Table 7). Theeffects of these steps thus cannot be explained solelyby the presence of shear. Decreases in foam den-sity in the first two steps of the processing line maybe due to a more homogeneous product resultingfrom these steps, as suggested by Hammershoj andLarsen.40 Moreover, Forsythe and Bergquist9 andThapon et al41 showed an improvement in foamingproperties at low shear rates, an effect which is simi-lar to slight homogenisation of the product. It shouldbe pointed out that homogenisation damage on gelstrain resistance occurred in warm egg white (46 ◦C).The decrease in gel strain resistance was associatedwith an increase in ova denaturation temperature anda decrease in S-ova and ovt denaturation tempera-tures. The increase in ova denaturation temperaturesuggests the stabilisation of the protein through com-plex formation or aggregation. Oliva et al42 showedhuman serum albumin trimer formation at high shearrates. Homogenisation was associated with thermaltreatment in this study, so protein aggregation likelyexplains the gel strain resistance loss.

CONCLUSIONThe main cause of variability in egg white functionalproperties found in this study was the quality ofraw material, despite attempts made to control thisparameter. Further experiments to clarify these resultswould be of great interest.

During the first steps of the process (shell eggbreaking and transfer to the storage tank) the criticalstep was the breaking of shell eggs resulting inreduced functional properties. This result was dueto the lower proportion of thick egg white frommechanical breakage compared with manual breakage.Nevertheless, further studies on this particular pointshould be performed. The first processing stepsdecreased egg white foam density. This decrease wasprobably due to the homogeneity of egg white resultingfrom these steps. As expected, larger decreases were

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observed in the second part of the process: tankstorage, pasteurisation and homogenisation. The lossof functional properties is thus associated with proteinaggregation. It is highlighted that heat treatmentis not the only critical point of the process. Thuspasteurised liquid egg white manufacturers may havemore possibilities to improve their process.

Nevertheless, the understanding of the mechanismsresponsible for variations in functional properties isdifficult when working with such a complex productas whole egg white. Further experiments are currentlyin progress to try to separate the effects of temperaturefrom those of shear rate, pH and ionic strength.

ACKNOWLEDGEMENTSThe authors would like to thank Josiane Fegar andGael Lebrun from 3 Vallees for their collaborationin this study and for agreeing to publication of theresults, and Stephanie Prestavoine for her invaluablehelp during the sampling campaigns. The authors arevery grateful to John Hannon for English corrections.This work was supported by the Conseil Regional deBretagne and the Conseil Regional des Pays de laLoire.

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