State diagram of tuna meat-Freezing curve and glass transition

See discussions, stats, and author profiles for this publication at: https://www.360docs.net/doc/0a10995524.html,/publication/236982707 State diagram of tuna meat: Freezing curve and glass transition

ARTICLE in JOURNAL OF FOOD ENGINEERING · MAY 2003

Impact Factor: 2.58 · DOI: 10.1016/S0260-8774(02)00346-1

CITATIONS 41DOWNLOADS

253

VIEWS

158

4 AUTHORS

, INCLUDING:

Mohammad Shafiur Rahman Sultan Qaboos University

197 PUBLICATIONS 2,103 CITATIONS

SEE PROFILE Stefan Kasapis

RMIT University

181 PUBLICATIONS 2,099 CITATIONS

SEE PROFILE

Nejib Guizani

Sultan Qaboos University

85 PUBLICATIONS 486 CITATIONS

SEE PROFILE

Available from: Mohammad Shafiur Rahman

Retrieved on: 09 July 2015

State diagram of tuna meat:freezing curve and glass transition

M.Sha?ur Rahman *,Stefan Kasapis,Nejib Guizani,Omar Saud Al-Amri

Department of Bioresource and Agricultural Engineering and Department of Food Science and Nutrition,College of Agriculture and Marine

Sciences,Sultan Qaboos University,P.O.Box 34,Al-Khod 123,Oman

Received 26April 2002;accepted 12August 2002

Abstract

The state diagram of tuna meat was developed by measuring and modeling its glass transition temperatures and freezing points.Fresh tuna meat was dried in a freeze drier to vary the moisture content from 73.3%to 6.0%(wet basis).Small deformation dynamic oscillation was employed to identify changes in the viscoelastic properties of tuna as a function of solids.Cooling curve method was used to measure the freezing point and end point of freezing.The state diagram yielded maximally freeze-concentrated solutes at 61%solids with the characteristic temperature of glass formation being )54.2°C.The freezing curve and glass transition lines were developed using the Clausius–Clapeyron equation adjusted with unfreezable water and Gordon–Taylor model,respectively.ó2003Elsevier Science Ltd.All rights reserved.

1.Introduction

There is an ever-increasing demand for fresh and processed ?sh in the developed and developing countries due to its nutritional value.Freezing and drying are the main preservation methods of ?sh.Safety and spoilage of processed foods are the major concerns of both manufacturers and consumers.The stability of frozen or dried foods strongly depends on the state of water,which a?ects the characteristics of the products.These characteristics or states could be easily identi?ed by developing the state diagram of ?sh meat,thus indicat-ing the stability of the product during storage.

Glass formation is a nature of second-order thermo-dynamic transition,generally characterized by a discon-tinuity in physical,mechanical,electrical,and thermal properties of a material (Rahman,1995;Rahman,1999).Biological materials are rigid and brittle below the glass transition temperature.Nevertheless,they are not crystalline with regular structure but retain the dis-order of the liquid or amorphous state (Schenz,1996).The physical state of foodstu?s is very stable below the glass transition temperature because compounds in-volved in deterioration reactions take many months or even years to di?use over molecular distances,and ap-proach each other to react (Slade &Levine,1991).

Furthermore,water molecules become kinetically im-mobilized within the concentrated phase,thus being unable to support or participate in the reactions causing deterioration (Mitchell,1998).

Detailed reviews of the application of vitri?cation phenomena on the stability of biological materials have been prepared by Rahman (1999)and Roos (1995).Glassy and freezing characteristics of pure substances are more commonly reported than real foods,which are complex multicomponent mixtures (Rahman,1995).However,the glass transition temperatures of freeze-dried strawberry and cabbage are available and were measured as a function of water content by Roos (1987)and Paakkonen and Plit (1991),respectively,using dif-ferential scanning calorimetry (DSC).Brake and Fennema (1999)and Inoue and Ishikawa (1997)also measured the glass transition of fresh muscle tissue by DSC.A basic understanding of physical properties in foodstu?s begins with the state diagram,which brings together a combination of equilibrium and metastable processes (Ablett,Clark,Izzard,&Lillford,1992).Re-cently the state diagram of dates and apples were de-veloped by Kasapis,Rahman,Guizani,and Al-Aamri (2000a)and Bai,Rahman,Perera,Smith,and Melton (2001).In the literature,it is di?cult to ?nd the state diagram of protein based foods,such as ?sh and seafood.In the present study,the state diagram of tuna meat was constructed using a combination of freezing exper-iments and small-deformation dynamic-oscillatory tests.The state diagram of tuna could be used to

determine

Journal of Food Engineering 57(2003)

321–326

https://www.360docs.net/doc/0a10995524.html,/locate/jfoodeng

Corresponding author.Tel.:+968-515-236;fax:+968-513-418.E-mail address:sha?ur@https://www.360docs.net/doc/0a10995524.html,.om (M.Sha?ur Rahman).

the stability of dried or frozen?sh during storage. Moreover,it could explain other structural and ther-modynamic properties of the dried?sh products,such as unfreezable water,e?ective molecular weight of solutes,

T0 m and T0

(conditions of maximally freeze concentra-

tion)and the Gordon–Taylor?s parameter.

2.Materials and method

2.1.Materials

Whole fresh long tail tuna(Thunnus tongol)(local name,Sahwa)was purchased from the local?sh market (Muscat)in the month of October.The mass and length of the whole?sh were from2.3to2.6kg and0.60to0.65 m,respectively.The whole tuna was washed with tap water and?lleted with a sharp knife.Portion of the red meat was removed from the?llets,and strips were pre-pared having14cm length,3cm width,and1.5cm thickness,respectively.The strips of tuna were frozen in a freezer at)40°C for at least24h,and then placed in an automatic controlled freeze drier(SP Industries Com-pany,New York).The plate temperature was set at)20°C with a vacuum of800mTorr(108Pa)in the chamber while condensing plate temperature was set at)65°C, and drying continued up to72h.

2.2.Water content and freezing point

The water content and total solids were measured gravimetrically by drying samples(?ve replicates)in air convection drier at105°C for at least20h.The freezing points of freeze-dried tuna meat powder at di?erent moisture content were measured by the cooling curve method.Details of the cooling curve method for mea-suring freezing points of food are given by Rahman (1995).Freeze-dried tuna strips were ground in a lab scale grinder(600W Jaipan,Mombai)to form powder, and samples were homogenized with the required amount of distilled water and left to equilibrate for24h in air-sealed containers at4°C.The stored samples were ?lled in a stainless steel cylinder(internal diameter,2.2 cm;height,4.5cm;wall thickness,1mm)and placed in a programmable freezer at)50°C for cooling.The cool-ing rate was determined from the initial rate of cooling as de?ned by Rahman et al.(2002).During experimen-tation,the temperature change as a function of time was logged by an automatic thermocouple positioned deep into the sample.The initial freezing point was deter-mined from the cooling curve as used by Rahman (1995),Rahman and Driscoll(1994)and Kasapis et al. (2000a).The end point of freezing(maximally freeze

concentration)T0

m was estimated from cooling curve as

used by Rahman et al.(2002).2.3.Glass transition

Rheological analysis was performed using the ARES, the advanced rheometric expansion system(Rheometric Scienti?c,Piscataway,NJ,USA),which is a controlled strain rheometer.ARES has two force rebalance trans-ducers(FRT)covering a torque range of0.02–2000 g cm.FRT transducers are air-lubricated and essentially non-compliant,but particular care was taken to estab-lish that any inherent machine compliance was insu?-cient to signi?cantly o?set measured values from the measured systems.This was achieved by progressive adjustment of geometry settings whilst measuring sam-ples of low and high known modulus,namely,poly-dimethylsiloxene(PDMS)at30°C(G c?2:5?104Pa) and ice at)5°C(G0?109Pa).Thus maximum plate diameter(5mm)and minimum gap(4mm)consistent with accurate results could then be determined.In doing so,glass transitions recorded as a function of tempera-ture were reproduced within a3%margin typically on three replicates.

For precise control of sample temperature,an air convection oven was used.The oven has a dual element heater with counter-rotating air?ow covering a wide temperature range of)60to160°C.Samples were loaded on the plate of the rheometer and were cooled rapidly to)40°C.Then they were subjected to an os-cillation of a set frequency(1rad/s)and strain(0.005%) whilst heating at a rate of2°C/min to100°C.The ther-mal routine was preceded or followed by a frequency sweep at the lowest and highest experimental tempera-tures.Thus readings of the rigidity/storage modulus (G0),viscous/loss modulus(G00)and dynamic viscosity (g?)variation with temperature and frequency were obtained.

3.Results and discussion

Fig.1reproduces a typical case of the e?ect of tem-perature on the viscoelastic behaviour of dried tuna samples.The plateau region is clearly seen at tempera-tures above0°C where both moduli change little with temperature and the elastic component of the network remains dominant(G0>G00).At lower temperatures, modulus traces rise by an order of magnitude thus achieving at)40°C values around1:4?109and2?108 Pa for G0and G00,respectively.At low temperatures,the trace of storage modulus approaches an upper bound, which re?ects the glassy state.The glassy state is also unveiled as a function of frequency in Fig.2,where the modulus remained relatively constant as a function of frequency.This is a matter of considerable experimental convenience,since it is not possible to obtain from a single frequency sweep at constant temperature to evaluate the structural properties of the system.Within

322M.Sha?ur Rahman et al./Journal of Food Engineering57(2003)321–326

the experimentally accessible frequency window,mod-ulus values re?ect a rigid solid with the linear trace of complex dynamic viscosity approaching the limiting slope of )1.The mechanical pro?le is indicative of the lack of thermal motions,with macromolecular segments being unable to perform coordinated oscillations at )40°C thus contributing to a predominantly elastic re-sponse.

The drop in modulus values between the glassy state and the viscoelastic region in Fig.1is due to the glass

transition region.Many of the earlier investigations of linear viscoelastic behaviour were carried out on amor-phous synthetic polymers and measured the shear mo-duli over a wide range of temperatures (Ferry,1980).Results showed clearly an extensive glass transition re-gion covering three to ?ve orders of magnitude of modulus,with the viscous component of the network being the dominant response (Plazek,1996).Recent work on high solids mixtures containing sugar between 70%and 86%and polysaccharides at levels of normal industrial use (up to 1%)demonstrated the formation of a lightly cross-linked network that at subzero tempera-tures undergoes vitri?cation (Kasapis,1998).As the vi-scoelasticity mounts up,G 00overtakes G 0as seen in the entropic transition from rubbery to glassy consistency in synthetic polymers (Kasapis,Al-Alawi,Guizani,Khan,&Mitchell,2000b).

In contrast with the behaviour of synthetic and bio-logical rubbers,the viscoelasticity of semicrystalline polymers is much less clearly de?ned.The fall in mod-ulus over the glass transition region for semicrystalline materials is,at between one and two orders of magni-tude,much less than for amorphous polymers (Ward &Hadley,1993).Furthermore,the change in modulus with temperature or frequency is much more gradual,indicating a broader relaxation time spectrum at which the solid-like response dominates over the liquid-like counterpart (Thompson &Woods,1956).The thermal pro?le obtained in Fig.1is congruent with the above characteristics,a result which argues that molecular mobility is substantially curtailed by the ordered regions within the network of dried tuna.Due to substantial order in the protein network,values of storage modulus at temperatures above 0°C remain in excess of 108Pa,so it is no longer correct to regard the system as rubber-like.

The combined framework of the Williams–Landel–Ferry equation with the free volume theory predicts that the rheological glass transition temperature (T g )of amorphous materials lies at the junction of the glass transition region (G 00>G 0)and the glassy state (G 0>G 00).Such reversal of viscoelastic functions is not,of course,observed in the tuna network,a result which makes ambiguous the determination of T g as de?ned above.To circumvent this problem,we considered a plot of the ?rst derivative of shear modulus (G 0)as a function of sample temperature (Fig.3).The curve shows three distinct regions,which are characteristic of the glass transition of semi-amorphous materials:(i)part AB is the relatively ?at portion of the curve of the ?rst de-rivative re?ecting the viscoelastic region in Fig.1;(ii)part BC is the portion of the curve where d G /d T de-creases rapidly to a minimum and corresponds to the thermal pro?le in Fig.1where the rate of increase in shear modulus is accelerating;(iii)part CD is the por-tion where d G /d T increases rapidly to match

the

M.Sha?ur Rahman et al./Journal of Food Engineering 57(2003)321–326323

decelerating rate of increase of shear modulus in Fig.1.Point C is important because it enables a clear distinc-tion to be drawn between the glass transition region and the glassy state thus de?ning the rheological T g .

In 1952,Gordon and Taylor proposed an empirical equation to predict the glass transition temperature of mixtures comprising amorphous synthetic polymers (Gordon &Taylor,1952).Today this is commonly used to predict the vitri?cation properties of multi-compo-nent mixtures,such as foodstu?s and biological mate-rials:T gm ?

X s T gs tkX w T gw

X s tkX w

e1T

where T gm ;T gs ,and T gw are the glass transition temper-atures of the mixture,solids,and water respectively,X s and X w are the mass fraction of solids and water,and k is the Gordon–Taylor parameter,which from the ther-modynamic standpoint is equivalent to the ratio of the change of component mixture speci?c heat at their T g (Couchman &Karasz,1978).The model parameters of Eq.(1)were estimated using SAS non-linear regression analysis (SAS,1996).Considering that the glass transi-tion temperature of supercooled water is )135°C,the values of T gs and k for tuna meat were found to be 95.1°C and 2.89,respectively.

The moisture content and freezing point of fresh tuna meat were 72.32%and )1.4°C,respectively.A typical cooling curve is shown in Fig.4.Cooling below the initial freezing point of a sample without formation of ice is de?ned as supercooling.Following initial super-cooling,the critical mass of nuclei is reached with the system nucleating at point,a.At this stage,the sample

releases its latent heat of fusion faster than the amount of heat removed from the system causing an instanta-neous increase in temperature to the equilibrium freez-ing point,b.The extent of supercooling is low in tuna compared to dates (Kasapis et al.,2000a).The cooling rate was varied from 0.5to 4°C/min using di?erent extent of insulation.In case of cooling rate above 3°C/min the freezing plateau was very short and it was dif-?cult to identify the freezing point accurately.Moreover the freezing point was signi?cantly lower compared to the cooling rates at or below 1.5°C/min.The measured freezing points at 1.5°C/min and lower cooling rate were not signi?cantly di?erent (p >0:05),thus all ex-periments were conducted at the cooling rate at or below 1.5°C/min.Freezing points data of tuna meat as a function of solids content are given in Table 1.Rahman et al.?s (2002)method was used to estimate the end point

of freezing (T 0

m )from cooling curve.In this method,slope of cooling curve is determined and plotted as a function of time to identify the end point of freezing.Initially,the slope is decreased and then reached a minimum value,which is identi?ed as the nucleation of ice.The end point of freezing is identi?ed when the slope starts to decrease from its highest value or plateau.A typical plot of the slope of the cooling curve for tuna meat is shown in Fig.5.The point c was identi?ed as the

end point of freezing.The value of T 0

m was found )13.3°C.

Rahman (1995)reviewed di?erent types of empirical and theoretical models used to predict freezing points of foods.In this work,the theoretical Clausius–Clapeyron equation was used to model the freezing point of tuna meat as a function of solids content.The Clausias–Clapeyron equation can be written

as:

324M.Sha?ur Rahman et al./Journal of Food Engineering 57(2003)321–326

d ?àb k w ln

1àX s

1àX s tEX s

e2T

where d is the freezing point depression (T w àT F ),T F is the freezing point of food (°C),T w is the freezing point of water (°C),b is the molar freezing point constant of water (1860kg K/kg mol),k w is the molecular weight of water,X s is the solids mass fraction,and E is the molecular weight ratio of water and solids (k w /k s ).The model pa-rameter E was estimated using SAS non-linear regres-sion and found to be 0.071for tuna meat.From the values of E ,the e?ective molecular weight of solids was 253.5.The value of E for squid mantle meat was found to be 0.082(Rahman &Driscoll,1994).The Clausius–Clapeyron equation is limited to an ideal solution (i.e.for a very dilute solution).Theoretical models can be improved by introducing parameters for non-ideal be-havior.Some of water in food is bound to the solids and unavailable for freezing.Thus,unfreezable water con-tent (B )can be de?ned as (Chen,1986):

B ?

water unavailable for freezing

total solids

In this case Eq.(2)can be written as:

d ?àb k w ln

1àX s àBX s

1àX s àBX s tEX s

e3T

The model parameter E and B were estimated using SAS non-linear regression and found to be 0.033and 0.383(in wet basis,unfreezable water,0.281and solids content is 1à0:281?0:719),respectively.The experimental and predicted values of freezing points calculated from Eqs.(2)and (3)are presented in Fig.6in order to assess the variation.It could be visualized from Fig.6that predicted values from Eq.(2)deviates from the experi-mental values.Fig.6also shows that predicted values from Eq.(3)considering the concept the bound water is

very accurate.The value of T 0

m (maximal freeze con-centration temperature or end point of freezing)was estimated from the cooling curve according to the method proposed by Rahman et al.(2002).The value of T 0m was found )13.3°C and the solids content from the freezing curve in Fig.6was found 0.610,which is dif-ferent from the values (X 0s ,0.719)estimated from Eq.(3).The reasons of this variation needs further study.The main reason could be that only adding the concept of bound water in Eq.(3)may not be enough to explain the complex process of freezing although the mathematical regression gave very accurate prediction.

Fig.7shows the state diagram of tuna meat incor-porating the cooling curve and glass transition curves.Curve AP represents the equilibrium between the solu-tion and the solids formed,and it has a negative gradient showing the expected decrease in the freezing point with increasing concentration of solids.The point P is shown

as T 0m in Fig.7.The intersection of the vertical extrap-olation of the point P on the glass transition curve at 61%(point E in Fig.7)is identi?ed as T 0g .At this stage,no water crystallize to ice in concentrated solutes of tuna and the system passes through the glass transition curve at T 0g ,which is about )54.2°C.Thus,the characteristic temperature of glass formation (T 0g )is lower than the point used to denote the temperature of solvent (i.e.water)crystallization.The water content at point E is

Table 1

Freezing point and glass transition temperature of tuna meat X s T F (°C/min)T 0m (°C)

X s T g (°C)0.267)1.4(fresh)0.268)1.3)13.3(3.7)

0.70

)28.090.289)1.10.74)23.980.363)2.40.800.030.397)2.90.8410.220.441)3.80.8932.240.450)3.40.94

61.92

0.451)3.90.489)4.90.500)5.30.546)7.60.547)7.80.599)11.50.604)11.40.619)15.10.633

)15.3

Note .Values in parenthesis is the standard deviation of ?ve

replicates.

M.Sha?ur Rahman et al./Journal of Food Engineering 57(2003)321–326

325

known as the unfreezable water,which for tuna meat is 39.0%(X0

4.Conclusion

The state diagram developed for tuna meat in this work can be used in determining the stability during frozen storage and in dried conditions as well as in de-signing drying and freezing processes.The present in-vestigation attempts to map out the melt,rubbery,and glassy state of tuna meat by means of a state diagram.In doing so,it utilizes the freezing point curve in combi-nation with the rheological glass transition temperature. The state diagram provided an estimate of maximally freeze-concentrated solutes at61%solids with the char-

acteristic temperature of end point of freezing(T0

m )being

)13.3°C and glass formation(T0

)being)54.2°C. Acknowledgements

The project was supported by the Sultan Qaboos University from the internal grant No.IG/AGR/FOOD/ 00/02.The assistance provided by Mr.Talib Mohd Al-Hinai for running SAS program is acknowledged and appreciated.

References

Ablett,S.,Clark, A.H.,Izzard,M.J.,&Lillford,P.J.(1992).

Modelling of heat capacity temperature data for sucrose–water systems.Journal of Chemical Society Faraday Transaction,88,795–802.

Bai,Y.,Rahman,M.S.,Perera,C.O.,Smith,B.,&Melton,L.D.

(2001).State diagram of apple slices:glass transition and freezing curves.Food Research International,34(2–3),89–95.

Brake,N.C.,&Fennema,O.R.(1999).Glass transition values of muscle tissue.Journal of Food Science,64(1),10–15.Chen,C.S.(1986).E?ective molecular weight of aqueous solutions and liquid foods calculated from the freezing point depression.

Journal of Food Science,51(6),1537–1553.

Couchman,P.R.,&Karasz,F.E.A.(1978).Classical thermodynamic discussion of the e?ect of composition on glass transition temper-atures.Macromolecules,11,117–119.

Ferry,J.D.(1980).Viscoelastic Properties of Polymers(pp.264–320).

New York:John Wiley.

Gordon,M.,&Taylor,J.S.(1952).Ideal copolymers and the second-order transitions of synthetic rubbers.I.Non-crystalline copoly-mers.Journal of Applied Chemistry,2,493–500.

Inoue,C.,&Ishikawa,M.(1997).Glass transition of tuna?esh at low temperature and e?ects of salt and moisture.Journal of Food Science,62(3),496–499.

Kasapis,S.,Rahman,M.S.,Guizani,N.,&Al-Aamri,M.(2000a).

State diagram of temperature vs date solids obtained from the mature fruit.Journal of Agricultural and Food Chemistry,48,3779–3784.

Kasapis,S.,Al-Alawi,A.,Guizani,N.,Khan,A.J.,&Mitchell,J.R.

(2000b).Viscoelastic properties of pectin/co-solute mixtures at iso-free-volume states.Carbohydrate Research,329,399–407. Kasapis,S.(1998).Structural properties of high solids biopolymer systems.In J.R.Mitchell,S.E.Hill,&D.A.Ledward(Eds.), Functional properties of food macromolecules(pp.227–251).Not-tingham:Nottingham University Press.

Mitchell,J.R.(1998).Water and food macromolecules.In S.E.Hill,

D.A.Ledward,&J.R.Mitchell(Eds.),Functional properties of

food macromolecules(pp.50–76).Gaithersburg:Aspen Publishers. Paakkonen,K.,&Plit,L.(1991).Equilibrium water content and the state of water in dehydrated white cabbage.Journal of Food Science,56,1597–1599.

Plazek,D.J.(1996).Bingham medal address:Oh,thermorheological simplicity,wherefore art thou?Journal of Rheology,40,987–1014. Rahman,M.S.(1995).Food properties handbook(?rst ed.,pp.87–177).Boca Raton:CRC Press.

Rahman,M.S.(1999).Glass transition and other structural changes in foods.In M.S.Rahman(Ed.),Handbook of food preservation(pp.

75–94).New York:Marcell Dekker.

Rahman,M.S.,&Driscoll,R.H.(1994).Freezing points of selected seafoods(invertebrates).International Journal of Food Science and Technology,29(1),51–61.

Rahman,M.S.,Guizani,N.,Al-Khaseibi,M.,Al-Hinai,S.A.,Al-Maskri,S.S.,Al-Hamhami,K.,(2002).Analysis of cooling curve to determine the end point of freezing.Food Hydrocolloids,16(6), 653–659.

Roos,Y.H.(1995).Phase transitions in foods.San Diego:Academic Press.

Roos,Y.H.(1987).E?ect of moisture on the thermal behavior of strawberries studies using di?erential scanning calorimetry.Journal of Food Science,52,146–149.

SAS,1996.SAS users?guide:statistics.SAS Institute,NC. Schenz,T.(1996).In G.O.Phillips,P.A.Williams,&D.J.Wedlock (Eds.),Relevance of the glass transitions on product functionality (pp.331–340).Oxford:IRL Press.

Slade,L.,&Levine,H.(1991).Beyond water activity:recent advances based on an alternative approach to the assessment of food quality and safety.Critical Review in Food Science and Nutrition,30,115–360.

Thompson, A. B.,&Woods, D.W.(1956).The transitions of polyethylene terephthalate.Transactions of Faraday Society,52, 1383–1397.

Ward,I.M.,&Hadley, D.W.(1993).An introduction to the mechanical properties of solid polymers(pp.84–108).Chichester: John Wiley.

326M.Sha?ur Rahman et al./Journal of Food Engineering57(2003)321–326