Food Chemistry 114 (2009) 1056–1062

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Food Chemistry

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Phospholipids and poly(glutamic acid)/hydrolysed gluten: Interaction and kinetics

Abdellatif Mohamed a,*, Rogers E. Harry-O’Kuru b, S.H. Gordon a, Debra E. Palmquist c

a Cereal Products and Food Science, National Center for Agricultural Utilization Research, 1815 N. University Street, Peoria, IL 61614, United Statesb New Crops and Processing Technology Research Units, National Center for Agricultural Utilization Research, 1815 N. University Street, Peoria, IL 61614, United Statesc Area Statistician, National Center for Agricultural Utilization Research, 1815 N. University Street, Peoria, IL 61614, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 6 June 2008Received in revised form 3 September 2008Accepted 30 October 2008

Keywords:PhospholipidsWheat glutenInteractionsKineticsActivation energyPoly(glutamic acid)DSCFT-IR

0308-8146/$ - see front matter Published by Elsevierdoi:10.1016/j.foodchem.2008.10.069

* Corresponding author. Tel.: +1 309 681 6331; faxE-mail address: a.mohamed@ars.usda.gov (A. Moh

The effect of poly(glutamic acid) (PGA) and hydrolysed wheat gluten (HG) on the thermal and kineticsproperties of lysophosphatidylcholine (LPC) was determined using DSC. A model system containingPGA or HG was added to 40% LPC aqueous suspension. The results from the study showed reduced DHvalues as a function of PGA molecular weight, which signified some kind of penetration of PGA or HG intothe LPC bi-layer but not enough to minimise the order of LPC vesicle structure. The ability of LPC mole-cules to rearrange itself into a bi-layer again was evident in the emergence of crystallisation profiles inthe course of the cooling cycle. The calculated activation energy (Ea) of pure LPC vesicle disruption (heat-ing) and formation (cooling) was 696.6 and 520.4 kJ/mol, respectively. Overall, the trend of lower Ea as afunction of PGA molecular weight was apparent during both heating and cooling cycles. Although, bothPGA and HG reduced the Ea, HG was less effective in reducing Ea. The purported molecular interactionbetween phospholipids and gluten has been confirmed using FT-IR spectroscopy. The FT-IR results indi-cated that interactions occurred in LPC/HG blends and in model mixtures consisting of LPC/PGA with dif-ferent molecular weight.

Published by Elsevier Ltd.

1. Introduction

Wheat lipids are divided into two groups: non-polar and polar.Non-polar lipids are dominated by triglycerides, and they originatefrom the embryo and the endosperm liposomes (Morrison, Mann,Soon, & Coventry, 1975). The polar lipids are found mainly in thecell membranes and mostly consist of phospholipids such as lyso-phosphatidylcholine (LPC). Although, lipids and in particular phos-pholipids, are present in small quantities in wheat, they have asignificant effect on the final texture of food products. As indicatedin the literature reports, polar lipids, i.e., phospholipids and glyco-lipids interact primarily with wheat gluten protein (Morrison,1978). This is significant because wheat gluten network formationduring dough mixing and throughout baking, is the single mostimportant factor that determines dough qualities, in the form ofgas retention and bread loaf volume (Chung & Pomeranz, 1977;Chung, Pomeranz, Finney, & Shogren, 1978; Mecham, 1971;Pomeranz, 1971; Pomeranz, 1980).

Surfactants are amphyphilic compounds capable of containingboth hydrophilic and hydrophobic moieties. Polar lipids (surfac-tants) can self-assemble in clusters called ‘‘micelles”, which occurabove a critical concentration, referred to as CMC and form a bi-layer,where the polar ends face the aqueous environment ( Morrison,

Ltd.

: +1 309 681 6685.amed).

1978). Wheat phospholipids (micelles) are capable of interactingwith wheat gluten by means of both polar and non-polar ends. Phos-phorous NMR of wheat gluten showed that phospholipids are organ-ised into a lamellar liquid crystalline phase (Didier, Christine, Serge,Charles, &Daniel, 1987). The interaction between phospholipids andgluten, as shown by NMR, does not occur in the same way as in cellmembranes, additionally, they are sensitive to temperature andthe effect of mechanical work during dough mixing.

The interruption of these interactions is the result of the expul-sion of the phospholipids into the water phase from the proteinnetwork due to extensive mechanical work or heating and cooling(Didier et al., 1987). Some of the free lipids become bound afterflour is wetted and formed into dough (Morrison, 1978). The inter-action between phospholipids and wheat gluten or poly(glutamicacid) can be estimated by determining the effect of the polypep-tides on the phospholipids peak temperature. The effect of proteinson the kinetics of phospholipids thermal properties may presentadditional information regarding the degree of the detected inter-action. Although, melting is not considered a kinetic process,Ozawa (1970) demonstrated that, non-isothermal DSC data canbe used to determine activation energy (Ea) with the assumptionthat peak temperature determined by DSC is also the temperatureof the maximum reaction rate. Reaction rate for a solid A to pro-duce a solid product B can be expressed as follows:

dxdt¼ Ze�Ea=RTð1� xÞn ð1Þ

A. Mohamed et al. / Food Chemistry 114 (2009) 1056–1062 1057

where x is the fraction reacted, Z is the pre-exponential factor, n isthe reaction order, Ea is the activation energy, T is the absolute tem-perature and R is the universal gas constant. A constant increase ofthe temperature of a first-order reaction (n = 1) by b and T = Tp (Tp isDSC peak temperature), will make possible the determination of theactivation energy of the material using DSC data. Eq. (1) was derivedaccording to Ojeda, Tolaba, and Suarez (2000) to yield the followingrelationship:

lnb

T2p

!¼ ln

RZEa

� �� Ea

R

� �1Tp

� �� �ð2Þ

where b is the constant rate of temperature rise, Tp is the peak tem-perature, R is the universal gas constant, Z is the pre exponentialfactor, and Ea is the activation energy. The natural logarithm trans-formation was used to linearise the equation. From Eq. (2), the plotof � lnðb=T2

pÞ against 1/Tp corresponds to a straight line with aslope = R/Ea, thus the activation energy (Ea) value can be calculated.The above-mentioned technique can be used to determine the ther-mal and kinetics properties of phospholipids–micelles disruption orformation as well as the blends using DSC.

In a previous publication, where the effect of vital gluten on LPCwas investigated, we reported an increase in activation energy as afunction of vital gluten content (Mohamed, Gordon, Harry-O’Kuru,& Palmquist, 2005). In addition, we reported that further increasein vital gluten content did not have a significant effect on the Ea

of LPC melting. These results generated interest in looking intothe effect of polypeptides with lower MW than vital gluten onthe thermal and kinetics properties of LPC in a model system.The model system included poly(glutamic acid) and protease-hydrolysed vital gluten. These LPC physicochemical changesoccurring during the heating of the mixture were monitored anddetermined using DSC and FT-IR spectroscopy.

2. Materials and methods

2.1. Materials

Poly(glutamic acid) with the following MW ranges: 2–15, 15–50, and 50–100, egg yolk L-a-lysophosphatidylcholine (LPC) 99%pure, and protease (500 units/g liquid) were purchased from SigmaChemicals Company (Sigma St. Louis, MO). Vital wheat gluten with75% protein content was obtained from Midwest Grain Products(Pekin, IL). The crude vital gluten was purified by removing thestarch contaminant via heating a 10% gluten/water slurry at95 �C for 20 min and centrifuging for 20 min (2000g) three sepa-rate times. The precipitate was freeze-dried and ground to passthrough an 80-mesh sieve. The ash and lipid content of the purifiedgluten was determined according to AACC approved methodsnumber 8–12 and 30–25 (Approved Methods of American Associ-ation of Cereal Chemists International, 2000), respectively, whilethe protein content was determined using LECO CHN-2000 instru-ment (3000 Lakeview Ave, St. Joseph, MI 49085). The carbohydratecontent was determined by difference. Purified vital gluten (5 g)was dispersed in 40 ml of 100 mM sodium phosphate buffer, pH6, and stirred at 37 �C for 30 min. Five millilitres of protease solu-tion was added, and the sample was stirred for 3 h. After 3 h, thereaction was quenched by adjusting the pH to 4.0 using concen-trated phosphoric acid. The sample was then dialysed (12,000MW membrane) against DI water overnight and freeze-dried. Theproduct of this reaction was called hydrolysed gluten (HG).

2.2. DSC analysis

A 40% LPC water suspension with 3%, 6%, or 10% PG or HG, basedon the LPC weight, was prepared. The suspension was stirred for

5 min and kept at room temperature for 1 h before it was freeze-dried. Five milligrams of mixture were placed in aluminium DSCpans. Samples were analysed using a TA Q2000 DSC instrument(TA Instruments Thermal Analysis and Rheology, New Castle, DE19720). Samples were heated at a range �5 to 80 �C with a heatingrate between 3 and 19 �C/min with an increment of 2 �C/min and1-min iso-track (holding) was implemented. Samples were sub-jected to heating and cooling cycles to test the ability of LPC toreorganize and show a transition after each cycle. Five heatingand cooling cycles were run, the second cycle was used for the ki-netic calculations. The second cycle was used in order to eliminatethe thermal history of LPC and establish a structure in conditionssimilar to the experimental conditions.

A 50:50 blend of LPC and Gluten and model systems consistingof LPC and PG in three molecular weights (2–15, 15–50, and 50–100 kDa) were prepared and tested by FT-IR spectroscopy(Mohamed & Gordon, 2007). The blends were dispersed in distilledwater at 40% concentration and held at 30 �C for 16 h to allow thegluten or polypeptide to interact completely with the phospholipidmultilayers. The dispersions were freeze-dried and pulverised un-der liquid nitrogen (�196 �C) to give fine powders that were usedto obtain the test spectra. Gluten, LPC and PG were each treatedseparately, as above, to prepare control spectra of the componentsalone.

2.3. FT-IR spectroscopy analysis

Samples of the blends and their components were pulverisedand pressed into transparent KBr disks for analysis by FT-IR spec-trometry. For each test, a sample (2.50 mg) was pulverised at li-quid nitrogen temperature in a sealed stainless steel vialcontaining a stainless steel ball bearing for 30 s on a Wig-L-Bugamalgamator (Crescent Dental Manufacturing, Lyons, IL). The vialwas allowed to warm to room temperature before KBr (900 mg)was added. The KBr/sample mixture was pulverised in a vial con-taining a ball bearing on the amalgamator in liquid nitrogen for10 s. The vial was again allowed to warm to room temperaturebefore a sample (300 mg) of the mixture was transferred to aKBr die (Perkin–Elmer Corporation, Norwalk, CT) and pressed un-der vacuum at 110 MPa on a laboratory press (Fred S. Carver,Menominee Falls, WI).

Spectra were measured on a Varian 3100 FT-IR spectrometer(Varian Inc., Walnut Creek, CA) equipped with a DTGS detector.Absorbance spectra were acquired at 4 cm�1 resolution and sig-nal-averaged over 32 scans. Interferograms were Fourier trans-formed using triangular apodization for optimum linear response.Spectra were baseline corrected, truncated to span only the meth-ylene and methyl band range (3000–2800 cm�1), and scaled to ad-just for small differences in sample weights.

2.4. Experimental design and statistical analysis

A completely random design (CRD) was used in a general linearmodel approach for modelling LPC vesicles melting (disruption) orcrystallisation (formation) kinetics of LPC mixed with PGA and HG.The first-order reaction equation was derived from the Ozawamodel and reported by Ojeda et al. (2000) for determining rateconstants and activation energies from differential thermal analy-sis is presented as Eq. (3).

Rewriting Eq. (2) in standard simple linear regression form,Y = mX + b, where m is the slope and b is the intercept, we obtainequation

1Tp

� �¼ R

Ea

� �� ln

b

T2p

!" #þ R

Ea

� �ln

RZEa

� �ð3Þ

Effect of poly (glutamic acid) with 2-15 KD and hydrolysed gluten on the ΔH of lysophosphatidylcholine (LPC)

% poly (glutamic acid) (2-15 KD) and hydrolysed gluten

0% 3% PGA 6% PGA 10% PGA 3% HG 6% HG 10% HG

H (

J/g)

Δ

0

20

40

60

80

100

Heating Cooling

a

b Effect of poly (glutamic acid) with 15-50 KD and 50-100 KD on the DH of lysophosphatidylcholine (LPC)

% poly (glutamic acid)3% PGA (15-50) 6% PGA (15-50) 10% PGA (15-50) 3% PGA (50-100) 6% PGA (50-100) 10% PGA (50-100)

ΔH (

J/g)

0

20

40

60

80

100

Heating Cooling

Fig. 2. (a) Effect of poly(glutamic acid) with 2–15 kDa and hydrolysed gluten on theDH of lysophosphatidylcholine. (b) Effect of poly(glutamic acid) with 15–50 and50–100 kDa on the DH of lysophosphatidylcholine.

Disruption and formation of pure LPC-micelles3.16

1058 A. Mohamed et al. / Food Chemistry 114 (2009) 1056–1062

where Y ¼ 1Tp

� �, X ¼ � ln b

T2p

� �, slope m ¼ R

Ea

� �, and intercept

b ¼ REa

� �ln RZ

Ea

� �.

Thirteen separate regression equations were obtained consist-ing of phospholipids treatments with 0%, 3%, 6%, and 10% addedPGA and HG. The form of the dependent Y-variable and the formof the independent X-variable was as defined in Eq. (3), for bothmelting and crystallisation phases of the thermal reaction (SASInstitute, 1991). Two separate general linear model (GLM) F-testfor full and reduced models were used to test for differences be-tween the phospholipid treatments equations for the melting andcrystallisation phases of the experiment (Neter, Wasserman,Kutner, & Inc., 1974). If a significant GLM F-test was obtained, indi-cating that at least one of the equations was different from the rest,distance metrics were used as a multiple comparison test for deter-mining which treatment equations were different from the others(Palmquist, 1993).

The Z-values were calculated and compared for the melting andcrystallisation phases by using the slope m and intercept b valuesfrom Eq. (3) to obtain

Z ¼ 1m

ebm ð4Þ

PROC REG from SAS PC Windows Version 8.2 was the statisticalsoftware used for most of the analyses (SAS).

3. Results and discussion

The DSC analysis of pure lysophosphatidylcholine (LPC), heatedand cooled at 3 �C/min, revealed an endothermic (heating) andexothermic (cooling) peak transitions at 51.4 and 47.6 �C, respec-tively. Although, the data presented in Fig. 1 represent only oneblend of LPC with PGA (15–50 kDa), other blends (PGA 2–15 kDa,50–100 kDa, and HG) were also scanned and discussed in this sec-tion. The DSC transition profiles of LPC mixture with PGA[poly(glutamic acid)] and HG (hydrolysed gluten) was gradually in-creased in size at elevated PGA (Fig. 1) or HG content, as reflectedon the higher DH values (Fig. 2a and b). The NMR data reported byDidier et al. (1987) revealed that phospholipids are immobilised inthe gluten matrix and become mobile as the system is over mixedor cooled. The DSC data reported from the study showed that the

LPC profiles mixture with 3, 6, and 10% Poly (glutamic) acid with MW range between 15,000 and 50,000.

Temperature (ºC)

0 20 40 60 80

Hea

t Flo

w (

W/g

)

-1

0

1

2

LPC LPC + 3% PG (15-50K) LPC + 6% PG (15-50K) LPC + 10% PG (15-50K)

Fig. 1. LPC profiles mixture with 3%, 6%, and 10% poly(glutamic acid) with MWrange between 15 and 50 kDa.

-ln b / Tp 2 K 10-5

1/ T

p K

10-3

3.04

3.06

3.08

3.10

3.12

3.14 Y = -0.0198 X + 3.32 (R2 = 0.88)

Y = 0.0127 X + 0.02.95 (R2 = 0.95)

micelle formation

micelle disruption

8.0 8.5 9.0 9.5 10.0 10.5 11.0

Fig. 3. Pure LPC vesicles disruption and formation.

effect of PGA and HG on the LPC thermal properties was not re-versed by cooling because the increase in peak size during the cool-ing cycle was sustained for all blends (Fig. 2a and b). The cause ofthe discrepancy in the results is that, the NMR-tested sample was adough system versus a model system presented here in addition tothe work we have presented in a previous publication (Mohamed,Gordon, Harry-O’Kuru, & Palmquist, 2005). The limiting factor ofthe model system used in this study was the protein, where LPC

Disruption and formation of LPC vesicles mixed with 3% (2-15 KD)

-ln β / Tp2 (K) 10-5

8.0 8.5 9.0 9.5 10.0 10.5 11.0

1/T

p (K

) 10

-3

3.04

3.06

3.08

3.10

3.12

3.14

3.16

3.18

3.20

disruption (melting)

formation (crystallysation)

Y = 0.0160 X + 2.91 (R 2 = 0.95)

Y = -0.0217 X + 3.36 (R 2 = 0.96)

slope

R Ea =

Fig. 4. Disruption and formation of LPC vesicles mixed with 3% (2–15 kDa).

Effect of PG MW and hydrolysed gluten on the Activation energy of LPC during the Heating cycle

Poly (glutamic acid)

LPC 2-15 15-50 50-100 Hydrolysed Gluten

Act

ivat

ion

Ene

rgy

(K.J

/mol

)

300

400

500

600

700

800

3% blend

6% blend

10% blend

Fig. 5. Effect of PGA and HG on the activation energy of LPC vesicle disruption.

Poly (glutamic acid)LPC 2-15 15-50 50-100 Hydrolysed Gluten

Act

ivat

ion

Ene

rgy

(K. J

/mol

)

250

300

350

400

450

500

550

600

Effect of PG and hydrolysed gluten on the activation energy of LPC during cooling cycle

3%blend

6% blend

10% blend

Fig. 6. Effect of PGA and HG on the activation energy of LPC vesicle formation.

A. Mohamed et al. / Food Chemistry 114 (2009) 1056–1062 1059

was the limiting factor of the dough system. The common featurebetween the two systems was the presence of both components(LPC and peptides) and the mixing process that facilitated theinteraction. The presence of other ingredients such as shorteningand sugar, in addition to the vigorous dough mixing are also pointsof difference between the two systems. The amount of water wassimilar between the two systems during the time of sample prep-aration. Except for PGA at 2–15 kDa and HG, the reduction in theDH of LPC transition, at higher PGA content within the identicalMW, indicate some sort of penetration of PGA molecules into theLPC bi-layer via mixing in the presence of water (Fig. 2a and b).The lower DH signifies molecular disorder of LPC bi-layers as theamount of PGA and HG increased (Fig. 2b). The HG effect on DHwas mixed, where the 3% and 6% stayed the same during heatingand exhibited high values during the cooling cycle (Fig. 2a). Appar-ently, the difference in the MW and amino acid make up of PGAand HG are the cause of their different effect on the LPC DH.Although, ribonuclease had no effect on LPC (neutral phospholip-ids) peak temperature or DH, it affected the thermal propertiesof di-palmitoylphosphatidylglycerol (charged phospholipids) asshown by higher peak temperature and DH (Papahajopoulos,Moscarello, Eylar, & Isac, 1975). The same authors reported thatgramicidin, a hydrophobic protein, reduced the DH of both phos-pholipids but had less of an effect on the peak temperature. The ef-fect of PGA and HG on the DH of LPC resembles the effect ofgramicidin on neutral and charged phospholipids. It is logical to in-fer from this comparison that PGA and HG are hydrophobic, butthis assumption will not hold because PGA is highly soluble inwater (Stanley & Strey, 2008). The fact remains that, DSC and FT-IR indicates the presence of interaction between PGA and HG withLPC. Lower DH values, without change in the peak temperature,suggest some kind of penetration of PGA or HG into the LPCbi-layer but not enough to minimise the order of LPC vesicle struc-ture. The evidence for that is clear in the ability of LPC to form ves-icles in the course of cooling cycle. LPC could be immobilised bysome proteins or strongly bound to it (Didier et al., 1987). A consid-erable change in LPC peak temperature is definite evidence of ves-icle structure disturbance, despite their densely packed structure.By comparing the heating and cooling cycles of LPC, it is evidentthat PGA and HG have a significant effect on LPC peak temperature,as is shown by the statistical analysis of the data. The peak temper-ature of LPC was 4–6 �C lower during the cooling cycle compared toheating in the presence of different levels of gluten (Figs. 5 and 6).The change in peak temperature is reflected in the activation en-ergy (Ea) because the peak temperature at different heating rates

is the principal factor of the Ea calculation. Possibly, interactionsbetween the molecules restrict molecular mobility, allowing LPCvesicles to rearrange at lower temperature (took a longer time,since this is the cooling cycle).

The DSC kinetic data of LPC vesicles disruption and formationwas calculated according to Ozawa (1970). Although, the kineticsdata of LPC alone is shown in Table 1 and Fig. 3 as an example,the LPC/PGA or HG blends were calculated using Eqs. (2) and (3).The calculated activation energy (Ea) of vesicles disruption(heating) and formation (cooling) of LPC alone was 696.6 and520.4 kJ/mol, respectively (Fig. 4). Higher LPC Ea values duringheating, in a hydrophilic environment, point to slower vesicle dis-ruption compared to the Ea during cooling, where vesicle formationrequired lower Ea values. The lower Ea indicated that the systemhad reached the most thermodynamically-stable state (lowest en-ergy level) by removing less kJ/mol than what was put in. In thepresence of 3% PGA (2–15 kDa), the Ea dropped by 232 kJ/mol(696–464 kJ/mol) during vesicles disruption (Fig. 5), while thesame PGA increased the Ea by 34 kJ/mol during vesicles formationcycle (cooling) as presented in Fig. 6. Overall, the trend of lowerLPC Ea during heating or cooling cycles in the presence of PGA isobvious in Figs. 5 and 6, where PGA with higher molecularweight further reduced the Ea of LPC, except PGA with 2–15 and

Table 1Kinetics relationship between heating or cooling rates and peak temperature forlysophosphatidylcholine according to Ozawa (9).

Heatingrate (b)

PeakTp (�C)

PeakTp (K)

(1/Tp) � 10�3 ðb=T2pÞ � 10�6 ð� lnðb=T2

pÞÞ � 10�5

Micelles melting (heating)3 51.0 324.2 3.08 28.55 10.465 51.4 324.5 3.08 47.47 9.967 51.7 324.9 3.08 66.33 9.629 52.1 325.3 3.07 85.08 9.37

11 52.4 325.6 3.07 103.79 9.1713 52.8 325.9 3.07 122.36 9.0115 53.0 326.2 3.07 141.01 8.8717 53.3 326.5 3.06 159.52 8.7419 53.6 326.8 3.06 177.96 8.63

Micelles crystallisation (cooling)3 48.0 321.2 3.11 28.73 10.465 47.6 322.8 3.12 47.94 9.957 47.0 320.2 3.12 67.16 9.619 46.6 319.8 3.13 86.40 9.36

11 46.2 319.4 3.13 105.60 9.1613 45.9 319.1 3.13 124.80 8.9915 45.4 318.6 3.14 144.09 8.8517 45.0 318.2 3.14 163.30 8.7219 44.8 318.0 3.15 182.51 8.61

Table 2The best-fit regression equations used for predicting Y1 ¼ 1

Tpof melting and Y2 ¼ 1

Tpof

crystallisation for X ¼ � ln b

T2p

� �, as defined in Eq. (2).

%LPC treatment Equations R2 p-Value

Melting0% Y1 = 1326.8 � X � 19.4 0.957 <.00013% PGA (2–15)a Y1 = 3281.9 � X � 58.1 0.947 <.00013% PGA (5–50) Y1 = 1115.6 � X � 14.8 0.948 <.00013% PGA (50–100) Y1 = 1011.0 � X � 12.8 0.957 <.00016% PGA (2–15) Y1 = 1628.2 � X � 25.0 0.957 <.00016% PGA (5–50) Y1 = 1152.2 � X � 15.3 0.935 <.00016% PGA (50–100) Y1 = 1230.1 � X � 17.6 0.926 <.000110% PGA (2–15) Y1 = 1243.4 � X � 17.8 0.960 <.000110% PGA (5–50) Y1 = 1046.3 � X � 13.4 0.933 <.000110% PGA (50–100) Y1 = 953.90 � X � 11.5 0.947 <.00013% HGb Y1 = 1337.5 � X � 19.4 0.937 <.00016% HG Y1 = 1585.0 � X � 24.7 0.947 <.000110% HG Y1 = 1904.6 � X � 31.7 0.963 0.0120

Crystallisations0% Y2 = �1386.6 � X + 34.8 0.893 <.00013% PGA (2–15) Y2 = �1301.4 � X + 34.3 0.975 <.00013% PGA (5–50) Y2 = �913.80 � X + 25.7 0.964 <.00013% PGA (50–100) Y2 = �839.40 � X + 24.4 0.968 <.00016% PGA (2–15) Y2 = �1005.2 � X + 27.8 0.950 <.00016% PGA (5–50) Y2 = �857.60 � X + 24.6 0.967 <.00016% PGA (50–100) Y2 = �1100.4 � X + 30.1 0.978 <.000110% PGA (2–15) Y2 = �956.50 � X + 27.1 0.964 <.000110% PGA (5–50) Y2 = �736.80 � X + 22.1 0.962 <.000110% PGA (50–100) Y2 = �684.20 � X + 21.0 0.949 <.00013% HG Y2 = �942.40 � X + 26.4 0.978 <.00016% HG Y2 = �975.00 � X + 27.3 0.976 <.000110% HG Y2 = �873.20 � X + 25.1 0.924 <.0001

a PGA, poly(glutamic acid).b HG, hydrolysed gluten.

1060 A. Mohamed et al. / Food Chemistry 114 (2009) 1056–1062

50–100 kDa (Fig. 6). In the presence of 3% HG, the drop in Ea was106 and 152 kJ/mol for the heating and cooling cycles, respectively.This showed the different behaviour of LPC in the presence of HGversus PGA, where a lower drop in Ea when compared to PGA forboth heating and cooling cycles was observed (Figs. 5 and 6). Inour previous work (Mohamed et al., 2005), where LPC was reactedwith vital gluten (un-hydrolysed) and 2–15 kDa PGA, a noticeableincrease in Ea was reported indicating LPC interaction with gluten,PGA, or HG is different. From Figs. 5 and 6 one can infer that, PGAMW is a significant factor that influenced the Ea of LPC. It was alsoobvious, that there was a different effect of HG when compared toPGA.

Generally, higher PGA content further reduced the Ea (Figs. 5and 6). The increase in HG had a mixed effect on Ea, where duringthe heating cycle the Ea of the 3% and 6% were the same, whilethe 10% exhibited 35% increase in Ea relative to the Ea of the 3%and 6% [745 (for the 10%)–550 (for the 3%) (kJ/mol)]. The 10%HG exhibited the highest Ea of all samples including the LPCalone. For the duration of cooling, HG concentration showed aninsignificant effect on Ea value (Fig. 6). Hammes and Schullery(1970) reported a reduction in Ea compared to the Ea of pureLPC in the presence of (poly)-lysine, which could mean that thePGA peptide changed structure during the heating cycle in a sim-ilar way to (poly)-lysine, as reported by the authors. The changein the structure seemed to facilitate faster vesicles formation asindicated by a lower Ea.

The best-fit regression equations used for predicting Y1 ¼ 1Tp

� �of melting and Y2 ¼ 1

Tp

� �of crystallisation for X ¼ � ln b

T2p

� �, as de-

fined in Eq. (3), are included in Table 2. All equations during melt-ing and crystallisation were statistically significant at p < .0001except 10% HG during the melting where p 6 0.012. The slopeand interaction coefficients for the models were statistically signif-icant as well. During melting, the 10% HG showed p = 0.0245 andp = 0.012 for intercept and slope, respectively, while the rest ofthe treatments showed p 6 .0001. This indicates true, non-zerocontributions to the prediction equations.

The general linear model (GLM) F-tests for determining meltingphase equation differences (Y1) was based on the full models:SSEF = 2.516 and dfF = 91 or the reduced model: SSER = 22.707 anddfR = 115. The test statistics used was

F� ¼ ððSSER � SSEFÞ=ðdfR � dfFÞÞSSEF=dfF

¼ 30:04 ð5Þ

where the values for Fða;dfR—dfF;dfFÞ are F(.01, 24, 91) = 2.04 andF(.05, 24, 91) = 1.66. In addition, when F* > F.01 it signifies that at leastone of the regression lines is not equal to the other at the 1% a-level,while F* > F.05 indicates at least one of the regression lines is notequal to the other at the 5% a-level. The different levels of LPC treat-ments exhibited different 1

Tpresponses from one another in the

melting phase (Table 1).The GLM F-tests for determining crystallisation phase equation

differences (Y2) for the full models: SSEF = 1.738 � 10�3 anddfF = 91 or the reduced model: SSER = 12.917 and dfR = 115. The teststatistic: F * = 24.53 using the same F values as above showedF * > F.01, at least one of the regression lines is not equal to the oth-ers at the 1% a-level and F * > F.05: at least one of the regressionlines is not equal to the others at the 5% a-level. The LPC treat-ments at different levels showed different 1

Tpresponses from one

another in the crystallisation phase as well (Table 1).Based on the different responses of LPC during both melting and

crystallisation, the distance metric test was done to determine dif-ferences between the treatments regression-equations (Table 3).The data in Table 3 shows the significant effect of PGA at differentmolecular weight and the HG on the melting of LPC. In addition,the data highlighted the reduced effect of PGA and HG on theLPC at 6% and 10% compared to the 3%. This data indicates the abil-ity of peptides and hydrolysed gluten to influence LPC thermalproperties when compared to un-hydrolysed vital gluten as re-ported in our first work (Mohamed et al., 2005).

The calculated Z-value using Eq. (4) with slope and interceptvalue from the above regression model is shown in Table 4. TheZ-value is the frequency factor in the Arrhenius equation as shownin Eq. (1). The crystallisation data values in Table 4 are shown as

Table 3Results of use of distance metric for determining treatment equation differences.

%LPC treatment Phase

Melting Crystallysation

0% dea a3% PGA (2–15)b a ab3% PGA (5–50) ef bd3% PGA (50–100) f cd6% PGA (2–15) bc ad6% PGA (5–50) ef cd6% PGA (50–100) ef ac10% PGA (2–15) ef bd10% PGA (5–50) ef cd10% PGA (50–100) f d3%HGc ce bd6% HG cd bd10% HG b cd

a LPC (lysophosphatidylcholine) treatments followed by the same letter(s) withina column indicates no significant differences between equations based on distancemetric calculations.

b PGA, poly(glutamic acid).c HG, hydrolysed gluten.

Table 4Calculated Z-value using Eq. (4) with slope and intercept values from the regressionmodels.

%LPC Treatments Phase

Melting Crystallysation

0% 0.00074 �0.000703% PGA (2–15)a 0.00030 �0.000753% PGA (5–50) 0.00088 �0.001063% PGA (50–100) 0.00098 �0.001166% PGA (2–15) 0.00060 �0.000976% PGA (5–50) 0.00086 �0.001136% PGA (50–100) 0.00080 �0.0008810% PGA (2–15) 0.00079 �0.0010110% PGA (5–50) 0.00094 �0.0013210% PGA (50–100) 0.00104 �0.001423% HGb 0.00074 �0.001036% HG 0.00062 �0.0010010% HG 0.00052 �0.00111

a PGA, poly(glutamic acid).b HG, hydrolysed gluten.

Table 5FT-IR absorbance intensity ratios of hydrolysed gluten/LPC and PG/LPC blends withBeer’s law addition of the pure component spectra.

Components Additiona spectrum IRa Blend spectrumb IRb IRb/IRa

HGc and LPC 2.354 2.471 1.05PGAd (2–15 K) and LPC 2.078 2.287 1.10PGA (15–50 K) and LPC 2.207 2.253 1.02PGA (50–100 K) and LPC 2.228 1.199 0.99

a IRa intensity ratio of peaks in addition spectrum.b IRb intensity ratio of peaks in blend spectrum.c PGA, poly(glutamic acid).d HG, hydrolysed gluten.

0

0.1

0.2

0.3

0.4

0.5

28002850290029503000

Wavenumber (cm-1)

Abs

orba

nc

Gluten/LPC blend

Gluten+LPC sum

Fig. 7. Comparison of FT-IR spectrum of hydrolysed gluten/LPC blend with theBeer’s law addition spectrum hydrolysed gluten + LPC sum.

A. Mohamed et al. / Food Chemistry 114 (2009) 1056–1062 1061

negative because crystallisation occurs during the cooling cycle(samples gave off heat). The Z-value also reflected stronger interac-tion between LPC and PGA or HG as presented in Table 3, where thedistance metric test showed more diverse values during crystalli-sation. The crystallisation Z-values were higher than the meltingZ-values (Table 4). High Z-value indicates greater frequency ofmolecular collision or interaction between LPC and PGA or HG.

Evidence of molecular interaction between LPC and gluten wasobtained by FT-IR spectroscopy. Since molecular conformations aremanifested in the infrared spectra of proteins and lipids (Painter &Koenig 1976), the molecular interactions of polypeptides withphospholipids have been extensively studied using FT-IR as wellas FT–Raman spectroscopy (Bertoluzza, Bonora, Fini, Morelli, &Simoni, 1983; Carrier & Pézolet, 1984; Pézolet, Duchesneau, Bou-gis, Faucon, & Dufoureg, 1982). A modification of an establishedinfrared method was used in the study to investigate the effectof PGA and HG on the conformation of LPC in aqueous dispersion.By using a conventional spectral addition-method (Koenig, 1983),molecular interactions between polymeric materials in a blendcould be detected as a difference between the spectrum of theblend and the sum of the spectra of the pure components. Thiswell-known Beer’s law spectral addition technique is acceptablefor two component systems. When applied to the study, the spec-tral addition technique showed a significant difference betweenthe spectrum of the blend and the sum of the spectra of the purePG and LPC components. Spectral addition was confirmed in thestudy by a technique (Gordon, Cao, Mohamed, & Willett, 2005)using spectra of 50:50 mixtures of PGA and LPC prepared cryogen-ically in KBr. The comparison of the FT-IR spectrum (LPC/HG blend)with the Beer’s law addition spectrum (HG + LPC sum) is shown inFig. 1. The FT-IR absorbance intensity ratios of PGA/LPC and HG/LPC blends and the Beer’s law additions of the pure componentspectra are compared in Table 5 (see Fig. 7).

In this work the spectra were normalised at a mutual absor-bance minimum (2880 cm�1) and plotted over the 3000–2800 cm�1 range to make comparisons based on the absorbanceintensity ratio, IR ¼ I2920 cm�1 I2850 cm�1= , defined by Verma, Wallach,and Sakura (1980) and Wallach, Verma, and Fookson (1979). Themeasured areas of the absorbance bands centered at 2920 and2850 cm�1 were taken as the peak intensities. According to Berto-luzza et al. (1983), the intensity ratio, IR, is related to the mobilityof –CH3 terminal groups involved in conformation changes inphospholipids caused by interaction with protein. From the peaksshown in Fig. 1, the absorbance intensity ratio for the HG + LPCaddition spectrum is IRa = 2.354, while the intensity ratio for the

HG/LPC blend is IRb = 2.471. The results from the study were qual-itatively similar to the ones reported by Bertoluzza et al. (1983),who used a now obsolete dispersive Raman spectrometer. The ra-tio of these two peak intensity ratios, IRb/IRa = 1.05, can be consid-ered as a measure of the degree of interaction between thecomponents in the blend, where a value of IRb/IRa equal to unitywould indicate zero interaction.

As seen in Table 1 the model PGA/LPC blends containing PGwith the lower molecular weights show the highest degrees ofmolecular interaction. The HG/LPC blend showed IRb/IRa = 1.05,which was closest to that of the PGA/LPC model blend with

1062 A. Mohamed et al. / Food Chemistry 114 (2009) 1056–1062

IRb/IRa = 1.02. The results indicated that hydrolysed gluten wasmore similar to the PGA of 15–50 kDa in its molecular surface-properties or in its manner of interaction with LPC. These results,obtained with state-of-the-art FT-IR spectrometry, indicate theconformation of the lipid bi-layer may be changed by penetrationof poly(glutamic acid) into the lysophosphatidylcholine mem-brane. The FT-IR analysis of gluten or HG/LPC and model PGA/LPC blends provide good evidence that phospholipid–gluten sur-face interactions occurred at their interface in aqueous systemsand probably have an important effect on dough rheology andproperties.

4. Conclusion

Differential scanning calorimetry is a good tool for the determi-nation of the thermal properties of lysophosphatidylcholine (LPC),as well as the determination of the kinetics of phospholipids mi-celle disruption. Poly(glutamic acid) (PGA) with different degreesof polymerisation and hydrolysed wheat gluten (HG) significantlyreduced the DH and the activation energy (Ea) of LPC interactions.The FT-IR analysis of the blend indicated the existence of surfaceinteraction between PGA or HG with LPC at their interface in ahydrophilic environment. This interaction between phospholipidsand vital wheat gluten was reported in the literature to have a sig-nificant effect on the dough rheology and bread loaf volume.

Acknowledgement

The authors would like to thank Jason Adkins for his technicalsupport.

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