Flavor Release from Composite Dairy Gels: A Comparison between

Chapter 31

Flavor Release from Composite Dairy Gels: A Comparison between Model Predictions and Time-Intensity Experimental Studies 1

I. P. T. Moore , T. M. Dodds, R. P. Turnbull, and R. A. Crawford

Downloaded by CHINESE UNIV OF HONG KONG on November 2, 2016 | http://pubs.acs.org Publication Date: September 7, 2000 | doi: 10.1021/bk-2000-0763.ch031

New Zealand Dairy Research Institute, Palmerston North, New Zealand

We tested a spreadsheet-based model for flavor release from composite (fat, protein, water) gels in the mouth using a simplified gel system with single flavors added and a sensory panel trained in the use of time-intensity methods. Gel manufacture was adjusted to control the parameters used in the model. The model mimicked the shape of the experimental time-intensity plot only when it incorporated removal of flavor by breathing and swallowing. The predicted time-intensity curve depended on the exchange flow of air between the mouth and throat. Factors associated with the gel had a much smaller effect. The model predicted correctly that flavor diffusivity and fat particle size had a small effect on perceived maximum intensity of flavor (IMax) and time of maximum intensity (TMax). It also predicted that IMax would be proportional to flavor concentration in the gel, but the experimental increase in IMax was less than proportional to changes in concentration.

The balance of perceived flavors in a food material can be changed by changing the concentration of the flavor compounds in the food, for example by changing the ingredients. However, it is also possible to obtain different perceived flavors by changing the texture of the food, without altering the composition. This occurs because changing the texture affects the speed of release of the individual flavor compounds to different extents. Thus the balance of flavors as perceived in the mouth is also changed. To avoid lengthy sensory trials during product development, a method is required which can be used to predict the perceived flavor. One part of this method is to be able to estimate the rate of flavor release from a knowledge of some simple physical properties of the flavor compound and the food. This chapter describes the development of a method to calculate the release rate of a flavor compound from a Current address: Goodman Fielder Ingredients, 45-47, Green Street, Botany, New South Wales 2019, Australia.

© 2000 American Chemical Society

Roberts and Taylor; Flavor Release ACS Symposium Series; American Chemical Society: Washington, DC, 2000.

381


382 composite gel (made up of an aqueous protein matrix plus discrete fat particles). It then compares the predictions, obtained by applying the methods to a real system, with experimental measurements of dynamic flavor perception using time-intensity techniques. The particular focus of the study reported here was the effect of fat particle size on flavor release from a composite gel (similar to processed cheese). Most of the critical volatile flavor compounds in cheese and processed cheese are fat soluble (1). The release of volatile fat-soluble flavor compounds from liquid emulsions has been widely studied and modelled (2-10). Some of the studies indicated that the flavor release rate from emulsions depends on the fat droplet size in the emulsions (3, 6) and we expected to observe a similar effect in composite gels. As far as possible, we manufactured composite gels with similar firmness, to avoid the texture of the gels interfering with the measurement of the effects of fat particle size.

Model Development The method adopted in developing our model was similar to that of Harrison & Hills (5). We differ in the method of realising the model, where we have chosen to implement a direct numerical simulation within an Excel spreadsheet and in some minor details of the stages in the release process. Harrison and Hills (5) identified rate-determining steps in the release process; we included all the steps we could identify in the model. The composite gel was assumed to contain two phases, an aqueous protein phase and a dispersed fat particle phase. The only mechanism for bringing the flavor compounds to the surface of a gel particle was assumed to be diffusion. Melting of the gel at the surface and direct transfer from the fat to saliva were neglected. Typically, a real gel sample for sensory testing would be roughly cubic in shape. Inside the gel, the fat particles would be roughly uniformly dispersed. For composite gels, the fat particles are typically 1-50 pm in diameter and a variety of shapes (Figure 1). Within the sample, some fat particles will be very close to the sample surface, while others will be close to the sample center and so roughly at a distance of half the sample size from the surface. Thus flavor compounds dissolved in the fat will have different distances to diffuse to the sample surface, from almost zero to half the sample size. To simplify the mathematical model, the fat particles were assumed to be all spherical, of identical size, and surrounded by a spherical shell of protein material. Flavor compounds diffused through the spherical shell to reach the surface, where they passed into a surrounding pool of aqueous liquid which represented the saliva in the mouth (Figure 2).

dc _ j^dPc For spherical diffusion:

~~ ^j

^

2

2dc\ ~*~ "JT^T |

w

n

e

r

e

c

l s m

e

concentration

of the flavor compound in the gel, D is the diffusivity and r is the distance out from the center of the sphere.



383

Figure 1. Confocal laser micrograph of a typical composite gel. Paler regions are the fat particles; darker regions are the protein matrix.

Figure 2. Conceptual model of the composite gel particle. flavor molecules.

White circles represent


384 For a fat-soluble flavor compound, diffusion through the fat particle was also included. At the interface between the fat and the protein matrix, the flavor concentrations in the two phases were assumed to be in equilibrium. The concentration in the saliva was assumed to be equal to the concentration at the outer surface of the spherical shell of protein. At the interface between fat (A) and protein

= F ( c , J) =

flux = D ^=D $j± Downloaded by CHINESE UNIV OF HONG KONG on November 2, 2016 | http://pubs.acs.org Publication Date: September 7, 2000 | doi: 10.1021/bk-2000-0763.ch031

A

where C

B

F*(c*;J)

= Κ CA (K is the partition coefficient)

For the numerical solution, each particle was divided into 40 spherical shells at equal radial steps (20 through the fat, 20 through the protein). The model used Fickian diffusion through the spherical particles, with the rate of diffusion proportional to the concentration gradient. Once the initial model was tested, the model was extended to include mechanisms for removing flavor compounds from the mouth. The first mechanism was to allow for swallowing of saliva, which simply resulted in a step decrease in the concentration of flavor in the saliva. It was assumed that the volume of saliva in the mouth remained constant - the swallowed saliva was replaced instantaneously by fresh saliva. At the same time the model was adapted to allow for the effects of chewing, at least in a very simplified manner. It was assumed that chewing resulted in a step change in the particle size in the mouth, which occured at the same time as the swallowing. The smaller particle size resulted in a shorter distance for the flavor to diffuse over to the particle surface, so increasing the rate of flavor release from each particle. The mathematical procedure for modelling the step change was not strictly accurate, since the concentration profile in the particles also changed, but the overall effect appeared to be consistent with the expected change in release rates. The increase in complexity required to include a mathematically exact description of the concentration profile after the particle size change was not expected to give a significant improvement in the predictions of the model. The next step was to allow for evaporation of volatile flavors into the respired air at the back of the mouth. The absorption of volatile flavor compounds in the nose is a key part of flavor perception and it was expected that the concentration in the respired air would be a key predictor of flavor release. In addition, the evaporation of flavor compounds provides another mechanism for the loss of flavor from the mouth. Modelling this process required the introduction of two further parameters, the interfacial area between the saliva and the air, and the mass transfer coefficient between the saliva and the air. At this stage no values were available for either and so estimated values were used. It was assumed that the evaporation occurred into a fixed volume of air at the back of the mouth and that the fixed volume was swept by a constant flow of respired air (Figure 3). The mass transfer between the retronasal air (flowrate Q) and the saliva is described by the differential equation: < L

dt

=

! ^

V

(

yQ_

K

V

c

'

s

w

o J

=

V

r

°

(

V

v

s

+

jÎM.

v

c

/

dt


385 where hsG - niass transfer coefficient between saliva and air, ASG = interfacial area between saliva and air, Κψ

= equilibrium constanat for partition of flavor

between saliva and air, C = the overall mean concentration in the saliva and composite gel. Vc, Vs, VQ refer to the volumes of the gel particle, the saliva and the


air in the mouth respectively; c$, Cg, are the concentrations of the flavor compound in the saliva and the air. The differential equations for the diffusion were converted to a numerical format using the Crank-Nicolson method. The resulting set of 42 simultaneous equations (including the saliva and the gas) was solved using a matrix inversion method implemented in a Microsoft Excel Spreadsheet. This approach made it possible to make step changes in the parameters during the simulation, such as the changes due to chewing and swallowing. Once written, the spreadsheet ran the simulations reasonably quickly (about 4 minutes on a 133 M H z Pentium PC to simulate a 1 minute flavor release experiment).

Experimental Method Panel Training Time Intensity Methods Time intensity (TI) is a sensory method that measures human response to a stimulus over a time period. TI has most commonly been used to measure attributes such as bitterness, sweetness, astringency and trigeminal sensations. TI has also been used effectively for evaluating textural attributes and flavor release in products which undergo changes of phase in the mouth. Panel Training A key part of the training for this trial was to establish a common evaluation procedure amongst the panelists. The sensory panel was made up of twelve members, who were selected for their sensory acuity and trained in the evaluation of natural cheese. As none of the panel members had experience in the evaluation of composite gels or the time intensity method, a training program was implemented to cover these two areas. The training program aimed to - allow panelists to become familiar with the composite gel product - develop an evaluation procedure - allow panelists to understand and practice the time intensity method - identify and develop descriptors for the flavor compound Twenty-eight training sessions were held. Approximately four sessions focused on developing a method for sample evaluation. Once the decision had been made to use ethyl butyrate as the flavor compound for this trial, training focused on the flavor and intensities associated with the chosen levels of ethyl butyrate (approximately seven sessions).


386 During initial training sessions panelists recorded their responses on a paper ballot at 5 s intervals. At more advanced training sessions and for the duration of this trial, C S A (Version 5.2.4) was used to record the data. The computerised system allows panelists to indicate their response by movement of a mouse cursor along an unstructured scale. Panelists were able to use the scale freely and were not forced to return the cursor to the zero position at the end of the time period.


Composite Gel Manufacture

We used composite gels manufactured from rennet casein, anhydrous milkfat, water and emulsifying salts (Trial 1) and from Mozzarella cheese, water and emulsifying salts (Trial 2) as shown in Table I.

Table I. Formulations used for Sample Production

Mozzarella cheese Water Anhydrous milkfat Rennet casein Emulsifying salt Hydrochloric acid (0.1M) Emulsifier type

Flavoring

Mixer speed

Levels - Trial 1 -

Levels - Trial 2 87.9% w/w

48.0% w/w 19.4% w/w

9.8% w/w -

29.2% w/w 2.4% w/w 1.5% w/w

-

None, Tween 60 (at 0.5% w/w) Ethyl Butyrate Absent, 50ppm (low), lOOppm (high) 120 rpm (slow), 180 rpm (fast)

2.4% w/w

None, Tween 60 (at 0.5% w/w) Absent, 50ppm Ethyl Butyrate 1500 rpm (slow), 3000 rpm (fast)

In both trials we used Sodium Hexametaphosphate as the calcium scavenger to release casein to emulsify the fat particles. Sodium Hexametaphosphate gives a firmtextured product. The protein source was selected to minimise any flavors which would distract from the added flavor. In addition, the cheese-based gels proved to be significantly more palatable than the rennet casein-based gels. We added ethyl butyrate to the gels in both trials to give a distinctive flavor at a controlled level. Ethyl butyrate is preferentially fat soluble, gives a fruity (pineapple) flavor and is often found in cheese.



387 The gels for trial 1 were manufactured in a 25 kg capacity Blentech twin-screw cooker. Those for trial 2 were made using a 25 kg capacity Stephan cooker-cutter. A l l the ingredients (including the flavor) were blended together in the cooker at ambient temperature and the mixture was then heated by steam injection to 85°C over 90 s. The mixture was then cooked for 3 minutes at 85°C and packed. During the cooking process, the cooker was kept closed, to prevent loss of the flavor compounds by evaporation. We tested for flavor losses due to processing in a separate experiment in which we used direct and indirect heating. The level of flavor compound in the product was tested by solid phase microextraction followed by gas chromatography and we found that in both cases the levels of flavor compound after processing were similar to those in a sample which had been blended with the desired amount of the flavor compound after cooling. For both trials, we manufactured 10 kg of each gel sample, and filled each sample into 500g pots. After taking 1 pot each for chemical analysis, microbiological testing, texture testing and confocal laser microscopy, the remaining samples were stored at 4°C until required by the sensory panel. Experimental Design for Composite Gel Manufacture The three factors used to produce the samples for each trial formed a randomised block design. Details of the levels are shown in Table I. The design intended that replicate samples be produced in two blocks. Each block was to have consisted of two days of gel manufacture, with all ten samples within a block being produced in random order. However, difficulties during production meant that this schedule was not fully adhered to and the block effect was lost from the design.

Sensory Analysis Samples were presented to panelists in four sessions, each session consisting of two sittings. Each session was held on a separate day. A total of six experimental samples were presented at each session, one of which was a duplicate (5 samples + 1 duplicate). Of these six samples, 3 were presented at a sitting, with the sample presentation order being randomised over the 2 sittings held each day. The panel members were given a 1.5 cm cube of each gel. The panel were instructed to record the flavor intensity while chewing the gel. After 30s, the panel were allowed to swallow naturally, and after 60s they spat out any remaining cheese. There was a 2 minute interval between samples, during which panellists rinsed their palates with soda water, carrots, and water at 24°C. A 'warm up' control sample was presented first at each sitting to allow panelists to orientate themselves with the base flavors of the gels. The C S A software produces 7 parameters which we used to analyse differences among samples and among experimental variables. The parameters are shown in Table II.


388

—I

AIR

I

>

•


SALIVA GEL

Figure 3. Conceptual model of the mouth.

250

25

α 200

20

C Ο

150

15

£

100

10

a.

Φ

ο c ο ο

50 0 4~ 4

10

6

t i m e (s)

Concentration at centre of fat (L.H. axis) ~~ Concentration at outer surface of fat (R.H. axis) Concentration in saliva (R.H. axis)

Figure 4. Initial development of flavor concentrations left hand axis; R.H. = right hand axis.

- simplified model. L.H. =


389 Table II. Definition of T I Parameters


Parameter Time at maximum intensity (TMAX) Maximum Intensity (IMAX)) Duration (DURr) Area Under Curve (AUC) Increase Angle (Inc Angle) Increase Area (Inc Area) Decrease Angle (Dec Angle) Decrease Area (Dec Area)

C S A Version 5.2.4 Definition The time required to reach maximum intensity The highest point on the curve The total time (seconds) from the time the attribute is first detected to the finish of the test The total area under the curve The angle (in degrees) of ascent from start to IMax. The area under the ascending portion of the curve from start to Imax The angle (in degrees) of descent from IMax to the last recorded value The area under the descending portion of the curve from IMax to the last recorded value

Results Modelling Figure 4 illustrates the typical changes in concentration of a flavor volatile in the gel particles and the saliva for the simplest case of the model, where there is no swallowing and no removal of flavor from the mouth by breathing. The curves are tending to an equilibrium (plateau) value because the mechanisms for removal of the flavor volatiles have been excluded. In practice, as Baek et al (11) have shown, the flavor concentration in the mouth rises to a peak and then falls during a flavor release test. Figure 5 shows the effect of assuming that 50% of the saliva is removed by swallowing every 10 s. The model assumes all the solid gel remains in the mouth. The steps in concentration are very sharp, since it was also assumed that the total saliva volume was constant and that the swallowed saliva was instantly replaced by fresh saliva. By allowing the air in the mouth to also exchange with air in the throat, the effect of the sharp changes in the saliva concentration were considerably smoothed. The air in the mouth acts as a pool of flavor which only responds slowly to changes in the saliva. The effect of changing the air flow rate through the mouth is shown in Figure 6. When the flow rate in the model was increased to near normal breathing rate, the concentration of flavor in the mouth followed the changes in saliva more closely. This suggested that an exchange process between air in the mouth and air in the throat would be a more realistic way to interpret this part of the model.



390

Ο

20

40

60

time (s) Air flow rate = 2 mL/s (L.H. axis ) Air flow rate = 50 mL/s (R.H. axis )

Figure 5. Effect of air flow rate on flavor concentration in air.

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50 si

0.12

g- 40 C Ο

c

0)

ο c ο ο

0.1

"Χ.

30

0.08

20

0.06 0.04

10 0

0.02

-" - — "

I

20

0

"

40

60

time (s) Air flow rate = 2 mL/s (L.H. axis) • Air flow rate = 1000 mL/s (R.H.axis)

Figure 6. Effect of air flow rate on flavor concentrations in air.


391


Experimental Study For each sample in the trial, we averaged the time intensity parameters over all the panel members. Figure 7 shows a typical experimental time-intensity curve. The comparable data from the model have also been shown. The two curves are very similar up to 30s. At this point, the panellist was allowed to swallow freely, and the model assumed that the panellist swallowed 50% of the saliva (and no solids) at 30s, again at 40s and again at 50s. The appearance of the experimental curve suggests that this panellist swallowed almost all the sample and saliva at 30s, so removing all the flavor from the mouth. Because we allowed free swallowing, the model could not be expected to prefectly match any single panel member.

Figure 7. Experimental time-intensity curve of one panellist and model prediction

To compare the time intensity parameters against the model predictions, we required two key measurements. The first was the fat particle size, which we used to establish the gel particle size in the model. We measured fat particles using images from confocal laser microscopy (similar to Figure 1). By measuring and counting about 500 fat particles, we were able to obtain a representative mean size. The second parameter in the model that changed with varying processing conditions was the diffusivity of the flavor in the protein part of the gel. We could not measure this directly, but we assumed that the diffusivity was inversely proportional to the firmness of the gel. These values were then used in the model to predict the timeintensity parameters for each sample. For the experimental time-intensity parameters, we used the average values for all the panel members. This introduced some noise into the comparisons since the


392


physiological coefficients (saliva volume, air flow rate and mas transfer rates) would be different for each panel members and we had to assume a single value for the model. Figure 8 and 9 show the comparison between the predicted and experimental parameters for the 50 ppm ethyl butyrate samples (trials 1 and 2). There is considerable scatter around the ideal match between experiment and model (shown as a solid line).

Figure 8. Comparison of experimental parameters and model predictions



393

Ο

20

40

60

80

100

experiment Ι

IMax • TMax

Deer Angle |

Figure 9. Comparison of experimental parameters and model predictions The best match between model and experiment was for the area under the increasing part of the curve. As shown in Figure 8, the trends were correctly predicted apart from a single outlying point. We could not find any obvious explanation for this single point, but it does appear as an outlier in some of the other data too. The initial part of the curve is most likely to be matched by the model. The simulation was less valid for the later part of each experimental trial because we had allowed free swallowing, which the model could not match. Figure 9 shows a good match for both time to maximum intensity ( T M A X ) and maximum intensity (IMAX), although the outlier is clear again in this set. In particular, the trends of the experimental data were well matched by the model for T M A X . We also attempted to correlate the results for T M A X directly with the two measured properties of the gels, the hardness and the fat particle size. We found no direct correlation between these properties and any of the time-intensity parameters.



394

One of the problems which we encountered with this study was the difficulty in changing the experimental conditions sufficiently to see significant variations in the time-intensity parameters. Within a range of similar gels, the mean fat particle size could only be changed by a factor of about 10 times and both the experiments and the model showed that this had a relatively small effect on the flavor release. Similarly, for gels of similar gross texture (solids as opposed to liquids), we were limited to a hardness variation by a factor of approximately 5. This gave only a small variation in both experimental and predicted flavor release parameters. In addition, we found that the experimental sensory parameters for the watersoluble flavor differed between replicate manufactures. T M A X varied by up to 2 times and I M A X by up to 1.5 times. We were unable to find any relationship between the variations and the instrumentally measured properties (firmness and fat particle size). We believe that the differences in the sensory parameters were caused by panel variability.

Conclusions A mathematical model has been developed which can predict the release of flavor from a composite gel into saliva in the mouth or retronasal air. The model for flavor release predicted the variation in T M A X for fat soluble flavors. I M A X and A U C were less well predicted by the model. There was no evidence of a consistent difference between the predicted and the experimental values of I M A X and A U C ; this may indicate that the differences were mainly caused by random noise in the measured parameters.

References 1. 2.

Urbach, G. Int. J. Dairy Tech., 1997, 50, 79-89 Brossard, C.; Rousseau, F.; Dumont, J. P. In Flavor Science: Recent Developments, Taylor, A. J.; Mottram, D. S., Eds.; Royal Society of Chemistry: London, 1996; pp 375-379 3. De Roos, Κ. B . Food Tech., 1997, 51, 60-62 4. Dickinson, E.; Evison, J.; Gramshaw, J. W.; Schwope, D. Food Hydrocolloids, 1994, 8, 63-67 5. Harrison, M.; Hills, B . P. Int. J. Food Sci. Tech., 1997, 32, 1-9 6. Harrison, M.; Hills, B . P.; Bakker, J.; Clothier, T. J. Food Sci., 1997, 62, 653-664 7. McNulty, P. B.; Karel, M. J. Food Tech., 1973, 8, 309-318 8. McNulty, P. B.; Karel, M. J. Food Tech., 1973, 8, 319-331 9. Rousseau, F.; Castelain, C.; Dumont, J. P. Food Qual. Pref, 1996, 7, 299-303 10. Salvador, D.; Bakker, J.; Langley, K . R.; Potjewijd, R.; Martin, Α.; Elmore, J. S., Food Qual. Pref., 1994, 5, 103-107 11. Baek I; Linforth R S T; Blake A ; Taylor A J, Chemical Senses, 1999, 24, 155-160


Flavor Release from Composite Dairy Gels: A Comparison between

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