Oil Migration in a Chocolate Confectionery System Evaluated by Magnetic Resonance ImagingYOUNG J. CHOI, KATHR YN L. MC C AR THY, AND MICHAEL J. MC C AR THY THRYN ARTHY ARTHY ABSTRA CT : O il migr ation is a common pr oblem in composite chocolate confectioner y pr oducts r esulting in ABSTRACT CT: Oil migration problem confectionery products resulting softening of chocolate and hardening of the filling. Spatial and temporal changes in the liquid oil content of a 2layer peanut butter and chocolate model system were evaluated using a magnetic resonance imaging (MRI) technique. The experimental factors were chocolate particle size, milk fat content, emulsifier concentration, , and stor age temper atur e. The r degr ee of temper esponses w er e migr ation r ate and o ver all change in signal temperatur ature responses degree temper, storage wer ere migration rate ov erall intensity (amount of migr ation). B ased on analysis of v ar iance (ANO VA), par ticle siz e, milk fat content, and migration). Based var ariance (ANOV particle size storage temperature were significant factors for oil migration rates. Milk fat content and temperature were significant factors for o all change in signal intensity . ov erall intensity. v er Keywor ds: chocolate , peanut butter , confectioner y, oil migr ation, magnetic r esonance imaging eywords: chocolate, butter, confectionery migration, resonance

E: Food Engineering & Physical Properties

Introduction

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il migration occurs in chocolate confectionery products that contain 2 or more oil-containing components adjacent to one another (Wootton and others 1971; Wacquez 1975; Talbot 1990; Couzens and Wille 1997; Ziegleder 1997). Typical examples are composite chocolate products in which chocolate enrobes a fat-containing center (for example, nut pastes, peanut butter, truffles). Different oil species migrate at varying rates and to different extents during storage depending on physical and chemical properties. The migration of the liquid lipid into the chocolate layer results in unwanted changes such as softening of the chocolate coating, hardening of the filling, and recrystallization of oil, which eventually leads to fat bloom (Talbot 1995; Ziegleder 1997; Lonchampt and Hartel 2004). Oil migration also changes sensory properties, such as color and flavor (Ali and others 2001). A number of contributing factors have been reported in the literature. Temperature is a strong contributing factor; the rate of fat migration increases as temperature increases. Ali and others (2001) modeled the migration rate of oil from a desiccated coconut and palm mid-fraction blend through dark chocolate as a linear dependence, with the rate increasing as the temperature increased from 18 C to 30 C. These researchers used nuclear magnetic resonance (NMR) to evaluate the solid fat content in the system over time as a function of temperature, as did Couzens and Wille (1997) and Talbot (1990). Magnetic resonance imaging (MRI), in contrast, provides both spatial and temporal information and has been used in studies to differentiate between components (Duce and other 1990; McCarthy 1994; Couzens and Wille 1997) and to monitor crystallization as lipid samples cooled (Simoneau and others 1992). The same samples can be followed over time because the MRI technique is nondestructive. Guiheneuf and others (1997) documented migration profiles at 19 C and 28 C in a model system of hazel nut

MS 20040759 Submitted 11/20/04, Revised 2/5/05, Accepted 3/2/05. The authors are with Dept. of Food Science and Technology, One Shields Ave, Univ. of California, Davis, Davis, CA 95616. Direct inquiries to author K.L. McCarthy (E-mail: klmccarthy@ucdavis.edu).

oil and dark chocolate. The researchers suggested that the mechanism of migration involves both diffusion of the liquid triacylglycerols and capillary attraction of the oil into the chocolate matrix. Degree of temper was added as a contributing factor to oil migration in a follow-up study by Miquel and others (2001) using dark chocolate and hazelnut oil. Oil concentration from MRI data was plotted against the square root of time; rates were characterized by the slope of the line. This approach is consistent with diffusion of a component in a semi-infinite media (Crank 1975) and was also used by Ziegleder (1997). Although good temper provides the best resistance to fat migration (Bolliger and others 1998), the tempering regime was reported to have no effect on the speed of the migration (Miquel and others 2001). However, the degree of temper was not stated quantitatively for the low-temper and high-temper samples. The researchers did report a different saturation concentration in the under-tempered and well-tempered chocolate that was hypothesized to be due to structural differences. In the process of oil migration, 2 phenomena have been identified: migration due to diffusion and/or capillary action and phase behavior (Aguilera and others 2004; Ziegler and others 2004). The focus of the work by Ziegler and others (2004) was to discuss the changes in equilibrium between solid and liquid phases during oil migration that alter the fat phase structure. Implicit, however, is that anything that decreases the solid fat content will increase the migration rate, including formulation. Lower solid fat content (SFC) products are softer and more prone to migration. The interaction between cocoa butter and milk fat is particularly important in defining the characteristics of milk chocolate. Bigalli (1988) stated that high levels of milk fat (for example, 20%) promote softening. The dominant factor is due to the liquid fraction of milk fat, which behaves almost as a straight dilution effect, similar to liquid nut oils such as peanut oil. Cocoa butter is simply diluted by these liquid oils rather than forming eutectics (Lonchampt and Hartel 2004). As part of a larger study, Walter and Cornillon (2002) evaluated oil migration in a model confectionery system of a layer of peanut butter over a layer of dark chocolate in an NMR tube. After 1 d, the NMR signal from the chocolate region had higher signal intensity because

Oil migration in chocolate . . .of migration of liquid fat from peanut butter. After the 2nd day, a low signal intensity layer appeared in the sample at the interface of the peanut butter and chocolate, which the researchers suggested was a more complex mechanism of migration than diffusion alone. This research addressed oil migration in a composite confectionery product of milk chocolate and peanut butter paste. The objective of this study was to identify and characterize important factors impacting oil migration in the model system. Proton density signal from oil was monitored during storage using MRI. The experimental factors were milk fat content, degree of temper, and storage temperature. In addition to these factors previously reported in the literature as important to oil migration, 2 other factors were incorporated: chocolate particle size and emulsifier concentration. The experimental responses were the rate of migration of the oil from the peanut butter paste to the chocolate and the overall change in signal intensity in the chocolate region due to increased liquid fat.Table 1Composition of the 5 chocolate formulations Particle size of chocolate AMF ( m) content (%) 45 60 45 45 45 3.57 3.57 0 10 3.57 Emulsifier concentration (%) a Lecithin 0.30 0.40 0.40 0.40 0 PGPR 0.08 0.11 0.11 0.11 0

Formulation 1 2 3 4 5

a AMF = anhydrous milk fat; PGPR = polyglyceryl polyricinoleate.

Materials and MethodsSample preparation and experimental designThe model system was a 2-layer chocolate confectionery system. A layer of milk chocolate was deposited into a 2.59-cm-dia 3.78cm-high sample container. A layer of peanut butter paste was deposited on top of the solidified milk chocolate (Figure 1). Each layer was approximately 1 cm high. Sample mass was 12.2 g with a standard deviation of 0.3 g. The plastic container was sealed with an airand moisture-tight lid. Five different chocolate formulations and 1 peanut butter paste formulation were used. The compositions of the chocolate formulations are given in Table 1. All chocolate formulations consisted of 26.65% total fat and 76.35% total nonfat solids. The total nonfat solids included 4.41% lactose, 11.57% cocoa liquor, and 8.70% nonfat dry milk. The standard chocolate (Formulation 1) consisted of 3.57% anhydrous milk fat (AMF), 0.3% lecithin, and 0.08% polyglyceryl polyricinoleate (PGPR), with a mean particle size of 45 m. Formulations vary in particle size, AMF, and emulsifier level. To change AMF content in formulations, part of the cocoa butter was replaced with AMF to maintain the total fat content. The mean particle size of Formulation 2 was 60 m. Formulations 3 and 4 contained 0% and 10% AMF, respectively. Formulation 5 had no emulsifier added. The peanut butter paste contained 36.20% fat, 61.84% nonfat solids, and 1.96% moisture; the mean particle size was 39 m. The chocolate samples were prepared to 3 different degrees of temper (under-, well-, and over-tempered) to study the effect of tempering on oil migration of peanut oil into the milk chocolate. For each formulation, the milk chocolate paste was melted (T > 38 C) in a temper machine (Revolation 1, ChocoVision Corp., Poughkeepsie, N.Y., U.S.A.) and then cooled gradually to 30 C. Cooling curves were monitored using a chocolate temper unit (CTU) value and slope values from the temper meter (Model 205 Portable Choc-

MRI measurementsOne-dimensional signal intensity profiles across the center of the sample container were obtained from 1H signal (liquid lipid) using a 7T superconducting magnet in conjunction with a Biospec console (Bruker Biospin MRI Inc., Billerica, Mass., U.S.A.), which corresponds to 300 MHz for 1H-resonance frequency. A spin echo imaging pulse sequence without phase encoding was used to acquire 1-dimensional MR images (that is, signal intensity profiles), as described by McCarthy (1994) and Callaghan (1991). The field of view was 6.4 cm with a slice thickness of 8 mm, and echo time was 4.9 ms; 256 data points (pixels) were acquired for each echo and 8 echoes were averaged. The resolution was 250-m /pixel. Three types of standards were prepared in sample containers: 100% milk chocolate (Formulation 1), 100% peanut butter paste, and a mixture of powdered cane sugar (30% w/w) in peanut oil. The powdered sugar/peanut oil standard was more time-invariant than the peanut butter paste standard and gave signal intensity values intermediate to the milk chocolate standard (low) and the peanut butter paste standard (high). Therefore, MRI signal intensities from the model confectionery system were normalized with the sugar/peanut oil standard, obtained on the same day; this procedure compensated for day-today variations of the spectrometer signal. The data at the initial time (t = 0) were used to identify the chocolate region and the peanut butter region in each sample container (Figure 2a). At that point, the chocolate and peanut butter regions were clearly identifiable, both in the 1dimensional profile (Figure 2a) and in the corresponding image (Fig-

Figure 1Schematic diagram of the model chocolate confectionery system

E: Food Engineering & Physical Properties

olate Temper Meter, Tricor Systems Inc., Elgin, Ill., U.S.A.). The degree of temper was evaluated by an industrial standard in which the slope value between 0.6 and 0.6 is considered well-tempered, above 0.6 is under-tempered, and below 0.6 is over-tempered (Bolliger and others 1998). The degree of temper was controlled by adding seed chocolate crystals based on a standard curve developed for each milk chocolate formulation. The samples were stored in controlled environment chambers at 20 0.5 C and 30 0.5 C. The temperature of 20 C represents normal storage conditions; the temperature of 30 C represents accelerated shelf-life test. The samples were removed from the controlled environment chambers and evaluated at room temperature. Samples were at room temperature no longer than 20 min and then returned to storage conditions. Three full factorial designs were used to evaluate the following combinations of factors: (1) chocolate particle size (Formulations 1 and 2), degree of temper, and storage temperature; (2) AMF content (Formulations 1, 3, and 4), degree of temper, and storage temperature; and (3) emulsifier concentration (Formulations 1 and 5), degree of temper, and storage temperature. The experimental designs were performed with 2 levels each of particle size, emulsifier concentration, and temperature; 3 levels were used for degree of temper and AMF content. Replicates were performed to confirm the effect of chocolate particle size, AMF content, and emulsifier concentration.

Oil migration in chocolate . . .ure 2b). The color map for the MR image is inverted gray scale, which means low proton signal intensity is bright and high proton signal intensity is dark. The spatial regions were designated and used throughout the study to evaluate the average signal intensity due to chocolate and due to the peanut butter paste over the experimental timeframe of 15 wk. Data analysis was performed using MATLAB 6.5 software (Mathworks, Natick, Mass., U.S.A.). At day 11, very little oil migration was evident in the samples stored at 20 C, as illustrated in Figure 3. Both the chocolate and peanut butter regions had virtually the same signal intensity that was evident on day 1 of imaging. In contrast, the images of the samples stored at 30 C illustrate the effect of oil migration on signal intensity (Figure 3). The chocolate region has increased in signal intensity due to the liquid lipid, especially at the interface region between the peanut butter paste and the chocolate. As the oil moved from the peanut butter layer to the chocolate, the signal was depleted in the peanut butter layer. The rate of oil migration at 30 C was higher in the 10% AMF sample (Figure 3b) than with the 0% AMF sample (Figure 3a). Figure 4, 5, and 6 illustrate the change in relative signal intensity over time for the well-tempered samples. Figure 4 corresponds to Experimental Design 1, which evaluated the effect of chocolate particle size. Figure 5 corresponds to Experimental Design 2, which evaluated the effect of anhydrous milk fat content. Figure 6 corresponds to Experimental Design 3, which evaluated the effect of emulsifier level. The data in these figures are viewed in terms of 2 regions: the upper region is the signal from the peanut butter paste and designated by dark markers, and the lower region is the signal intensity from the chocolate region and designated by open markers. For both the chocolate region and the peanut butter region, the signal intensity values have been summed, normalized by the sugar/peanut oil standard acquired on the same imaging day, and multiplied by 100%. The normalization was performed in this way to ensure that possible phase changes by the lipid component would not be masked. In each experimental design, the signal intensity of the peanut butter paste decreased over time as the signal intensity of the chocolate increased. The change was more rapid and more distinct for the samples stored at 30 C than for the samples stored at 20 C.

Results and Discussion

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wo-dimensional cross-sectional images provided quantitative information on oil migration. Representative images of different chocolate particle size samples after 11 d of storage are displayed in Figure 3. An image of the sugar/peanut oil standard, designated as PO (for peanut oil), is included to provide reference signal intensity values. Two distinct regions are visible in the 20 C samples. The chocolate region is at the bottom of the sample container with lower signal intensity; the peanut butter paste is the top layer in the sample container with higher signal intensity. Both the 20 C samples and sugar/peanut oil standard show phase separation of the liquid oil and the bulk material. This phase separation had occurred by day 1 of imaging (day 0 was the initial time, t = 0). Although the phase separation also occurred in the 30 C samples, the oil layer had reabsorbed into the bulk material by day 11. As a general comment, there are signal intensity variations in the peanut butter region; the heterogeneity was due in part to a small amount of air entrapped during sample preparation of the viscous paste.

E: Food Engineering & Physical Properties

Figure 2Representative magnetic resonance imaging (MRI) information for the chocolate confectionery system at the initial time (t = 0), (a) 1-dimensional signal intensity profile and (b) MR image; the darker the gray, the higher the proton signal.

Figure 3Two-dimensional images of samples after 11 d of storage for (a) 0% anhydrous milk fat (AMF) at 20 C and at 30 C, and (b) 10% AMF at 20 C and at 30 C. The image of the sugar/peanut oil standard is designated PO.

Oil migration in chocolate . . .Table 2Signal intensity from the chocolate region on day 1 (S1), the constant level after storage (Sf), their difference, and the fractional change of the liquid fat signal relative to the signal intensity at day 1 for samples stored at 30 Ca Formulation 1 0.3% emulsifier 45-m particle size 3.57% AMF 2 60-m particle size Te m p e r Under Well Over Mean Under Well Over Mean Under Well Over Mean Under Well Over Mean Under Well Over Mean S1 16.07 15.46 15.27 15.06 14.89 16.39 14.50 15.26 8.92 9.08 8.55 8.85 23.47 22.72 20.90 22.37 12.69 13.46 11.81 12.65 Sf 31.53 31.63 31.85 31.67 32.33 33.04 31.99 32.45 23.55 25.77 23.87 24.40 38.17 37.63 37.65 37.82 28.63 28.63 27.96 28.40 Sf S1 (Sf S1)/S1 15.46 16.16 16.58 16.06 17.44 16.65 17.50 17.20 14.63 16.69 15.31 15.54 14.70 14.91 16.74 15.45 15.94 15.17 16.15 15.75 0.96 1.05 1.09 1.03 1.17 1.02 1.21 1.13 1.64 1.84 1.79 1.76 0.63 0.66 0.80 0.70 1.26 1.13 1.37 1.25

3 0% AMF

4 10% AMF

5 0% emulsifier

a AMF = anhydrous milk fat.

At 30 C, the oil migration progressed primarily within the 1st 2 wk, and oil concentration in the chocolate region reached a constant level by 3 wk. In contrast, migration was much slower at 20 C; the signal intensity had not reached a constant value after 106 d of storage. To give a quantitative understanding of the extent (or amount) of oil migration, Table 2 gives signal intensity values at day 1 (S1), final constant signal intensity values (Sf) in the chocolate region, their difference (Sf S1), and the fractional change of the liquid fat signal relative to the signal intensity at day 1 for the under-, well-, and overtempered chocolate samples stored at 30 C. The greatest change in signal intensity over time occurred for the 60-m particle size (high-

Statistical analysisANO VA analysisANOVThree-way ANOVA was performed for each experimental design; the main effects and 2-way interactions were evaluated for each design at a level of significance of = 0.05. The 3 responses were rate of change of signal intensity from liquid fat in the chocolate phase (slope of the linear regression), amount of change of signal

Figure 4Relative signal intensity as a function of chocolate particle size over storage time. Solid markers represent signal intensity from the peanut butter region, open markers from the chocolate region. Square markers represent 45 m particle size; circle markers represent 60 m particle size. Markers with a dot inside represent 20 C storage temperature; markers without a dot represent 30 C storage temperature.

Figure 5Relative signal intensity as a function of anhydrous milk fat (AMF) concentration over storage time. Solid markers represent signal intensity from the peanut butter region, open markers from the chocolate region. Square markers represent 0% AMF; circle markers represent 10% AMF. Markers with a dot inside represent 20 C storage temperature; markers without a dot represent 30 C storage temperature.

E: Food Engineering & Physical Properties

level particle size). The least change in signal intensity over time occurred for the 10% AMF sample (high-level AMF). The greatest fractional change occurred for the 0% AMF sample. To evaluate migration rates, the relative signal intensity (relative to the sugar/peanut oil standard) for the chocolate region was plotted against the square root of time. As stated in the Introduction, this approach is consistent with diffusion of a component in a semi-infinite media; in addition, the relationship is consistent with capillary flow (Aguilera and others 2004). Figure 7 illustrates the change in signal intensities for each experimental design: Figure 7a illustrates particle size effect (Experimental Design 1), Figure 7b illustrates the effect of AMF content (Experimental Design 2), and Figure 7c illustrates the effect of emulsifier content (Experimental Design 3). The high level of each of these factors (particle size, AMF content, and emulsifier content) is given by dark markers. For each experimental design, the high level of these factors yielded higher overall content of liquid lipid. A linear regression was performed on the linear region of the signal intensity versus square root of time plots. For each set of data, the time frame was 14 d. The value of the slope, which quantifies migration rate, the intercept, and the coefficient of determination (R2) are given in Table 3 for the well-tempered samples. The R2 values for the 20 C storage samples were considerably lower than for the 30 C storage samples. The lower values are due to a weaker linear relationship (slope near zero) rather than scatter in the experimental data. The rate of migration is clearly a strong function of temperature; slope values at 30 C storage differ by a factor of 10 from the slope values of samples stored at 20 C. In addition, the rate of migration increased as particle size increased. Analysis of variance (ANOVA) was performed to determine statistically significant differences in the values of the responses due to each of the 5 experimental factors.

Oil migration in chocolate . . .of liquid fat over the storage time frame (Sf S1), and fractional change of the liquid fat signal relative to the signal intensity at day 1, (Sf S1)/S1. The signal intensity at day 1 (S1) was the sum of the signal intensity after 24 h at storage temperature. This value was viewed to be more indicative of changes at a constant temperature than the initial value at day 0 after sample preparation. For Experimental Design 1, the levels of the factors were as follows: 2 levels of particle size: 45 m, 60 m; 3 levels of temper: under-, well-, over-tempered; and 2 storage temperatures: 20 C and 30 C. Results of ANOVA are given in Table 4. Temperature was a significant factor for all 3 responses. Increasing the temperature from 20 C to 30 C increased the rate of migration and the extent of migration. Increasing the particle size from 45 m to 60 m increased the rate of migration. The amount of migration was not statistically different. The degree of temper was not a significant factor; interaction terms were not significant. The statistical analysis indicates that the more porous structure due to the larger particle size facilitates more rapid oil migration but does not significantly affect the amount that migrates. For Experimental Design 2, the levels of the factors were as follows: 3 levels of anhydrous milk fat: 0%, 3.57%, 10%; 3 levels of temper: under-, well-, over-tempered; and 2 storage temperatures: 20 C and 30 C. Like Experimental Design 1, temperature was a significant factor for all 3 responses. Increasing the temperature from 20 C to 30 C increased the rate of migration and the extent of migration. The fractional change in signal was a more sensitive indicator of overall change in signal intensity than the difference between Sf and S1. Again, the degree of temper was not a significant factor. Similar to Experimental Design 1, interaction terms between the factors were not significant except for temperature/AMF for the fractional change. In this case, the signal intensity at day 1 was also samples each). The responses over 15 wk were evaluated, and the ANOVA results are presented in Table 5. The only difference between Table 5 and Table 4 is that the rates of oil migration are significantly different at P = 0.06 rather than at = 0.05. As with the 1st set of experimental designs, particle sizes of 45 m and 60 m did not yield significantly different extents of migration; AMF content yielded statistically different rates and extents of oil migration in the range of 0% to 10%, and emulsifier levels (at 0% and 0.3%) did not yield significantly different response values for either the rate or extent of migration.

Conclusions

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his study identified statistically significant factors impacting oil migration in a model chocolate confectionery system. Based on the ANOVA results, the most significant factor was storage temperature, with particle size and milk fat content statistically signif-

E: Food Engineering & Physical Properties

Figure 6Relative signal intensity as a function of emulsifier concentration over storage time. Solid markers represent signal intensity from the peanut butter region, open markers from the chocolate region. Square markers represent 0% emulsifier; circle markers represent 0.3% emulsifier. Markers with a dot inside represent 20 C storage temperature; markers without a dot represent 30 C storage temperature.

Figure 7Relative signal intensity changes in the chocolate region of different (a) particle size, (b) anhydrous milk fat (AMF) content, and (c) emulsifier concentration samples with different degree of temper at 30 C. Open markers represent the low level of the factor; closed markers represent the high level of the factor. The square markers are undertempered chocolate; triangle markers are well-tempered chocolate; and circle markers are over-tempered chocolate.

Oil migration in chocolate . . .Table 3Results of linear regression for the rate of migration for the well-tempered samplesa Samples at 20 C Slope Intercept Particle size 45 m 0.30 60 m 0.40 AMF 0% 3.57% 10% 0.15 0.30 0.51 9.19 7.34 3.67 9.19 13.80 8.31 9.19 R2 0.899 0.719 0.655 0.899 0.684 0.733 0.899 Samples at 30 C Slope Intercept 5.63 6.93 5.75 5.63 7.08 5.69 5.63 9.91 9.25 2.96 9.91 15.22 7.46 9.91 R2 0.988 0.958 0.998 0.988 0.962 0.978 0.990 Table 5Analysis of variance (ANOVA) for the rate and extent of oil migration into the chocolate region of welltempered samples stored at 30 Ca Rate Particle size, Formulations: 1, 2 45 m NSc 60 m S at P = 0.06 AMFb, Formulations: 1, 3, 4 0% 3.57% S 10% Emulsifier, Formulations: 1, 5 0% NS 0.3% Extent (Sf S1)/S 1 NS

S

Emulsifier 0% 0.28 0.3% 0.30

NS

a AMF = anhydrous milk fat.

aTwo samples at each formulation were tested. Significance at = 0.05 (P 0.05) is designated as S, non significance as NS. bAMF = anhydrous milk fat. cDifferent than Table 4.

Table 4Analysis of Variance (ANOVA) for the responses from each experimental design a Amt change S f S1 Fractional change (Sf S1)/S1 NS NS S NS NS NS S NS S NS S NS NS NS S NS NS NS

AcknowledgmentsThe authors are grateful to Hershey Foods Corp. personnel for providing samples and lending instruments, special thanks to W. Hanselmann, D. Sweigart, J. Furjanic, J. Shuleva, J. Zhao, and D. Teets for insightful discussions. This work was supported by USDA grant 2002-35503-12276.

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ReferencesAguilera JM, Michel M, Mayor G. 2004. Fat migration in chocolate: diffusion or capillary flow in a particulate solid? A hypothesis paper. J Food Sci 69(7):R16774. Ali A, Selamat J, Che Man YB, Suria AM. 2001. Effect of storage temperature on texture, polymorphic structure, bloom formation and sensory attributes of filled dark chocolate. Food Chem 72:4917. Bigalli GL. 1988. Proceedings of 42nd PMCA Production Conference on Practical aspects of the eutectic effect on confectionery fats and their mixtures. 1988 April 26-8; Hershey, Pa.. Hershey, Pa.: PMCA, An International Association of Confectioners. 42:6671. Bolliger S, Breitschuh B, Stranziner M, Wagner T, Windhab EJ. 1998. Comparison of precrystallization of chocolate. J Food Eng 35:28197. Callaghan PT. 1991. Principles of nuclear magnetic resonance microscopy. Oxford, U.K.: Clarendon Press. 492 p. Couzens PJ, Wille HJ. 1997. Fat migration in composite confectionery products. Manuf Conf 77:457. Crank J. 1975. The mathematics of diffusion. 2nd ed. Oxford, U.K.: Clarendon Press. 414 p. Duce SL, Carpenter TA, Hall LD. 1990. Nuclear magnetic resonance imaging of chocolate confectionery and the spatial detection of polymorphic states of cocoa butter in chocolate. Lebensm Wiss Technol 23:5459. Guiheneuf TM, Couzens PJ, Wille H-J, Hall LD. 1997. Visualisation of liquid triacylglycerol migration in chocolate by magnetic resonance imaging. J Sci Food Agric 73:26573. Lonchampt P, Hartel RW. 2004. Fat bloom in chocolate and compound coatings. Eur J Lipid Sci Technol 106:24174. McCarthy MJ. 1994. Magnetic resonance imaging in foods. New York: Chapman & Hall. 110 p. Miquel ME, Stephen D, Couzens PJ, Wille HJ, Hall LD. 2001. Kinetics of the migration of lipids in composite chocolate measured by magnetic resonance imaging. Food Res Int 34:77381. Simoneau C, McCarthy MJ, Reid DS, German JB. 1992. Measurement of fat crystallization using NMR imaging and spectroscopy. Trends Food Sci Technol 3:20811. Talbot G. 1990. Fat migration in biscuits and confectionery systems. Conf Prod 56:26572 Talbot G. 1995. Chocolate fat bloomthe causes and the cure. Int Food Ingred 1:405 Wacquez J. 1975. Fat migration into enrobing chocolate. Manuf Conf 55(3):1926. Walter P, Cornillon P. 2002. Lipid migration in two-phase chocolate systems investigated by NMR and DSC. Food Res Int 35:7617. Wootton M, Weeden D, Munk N. 1971. A study of fat migration in chocolate enrobed biscuits. Rev Int Choc 26(10):26671. Ziegleder G. 1997. Fat migration and bloom. Manuf Conf 77(2):434. Ziegler GR, Shetty A, Anantheswaran RC. 2004. Nut oil migration through chocolate. Manuf Conf 84(9):11826.

Experimental Design 2, Formulations: 1, 3, 4 AMFb S NS Degree of temper NS NS Temperature S S AMF-temper NS NS AMF-temperature NS NS Temper-temperature NS NS Experimental Design 3, Formulations: 1, 5 Emulsifier NS NS Degree of temper NS NS Temperature S S Emulsifier-temper NS NS Emulsifier-temperature NS NS Temper-temperature NS NS

a Significance at the = 0.05 ( P 0.05) level is designated as S, non significance as NS. b AMF = anhydrous milk fat.

icant as well. These factors influenced oil migration rate and the amount of change in liquid oil content in the chocolate over time. Spatial variations in liquid lipid signal were observed that were consistent with the observation of Walter and Cornillon (2002) for their sample of commercial peanut butter and dark chocolate. These spatial variations are not completely consistent with Fickian diffusion and suggest that capillary flow may have a role. Most notably, the proton density of liquid fat decreases dramatically at the interface between the peanut butter paste and the chocolate. A diluting effect due to the liquid peanut oil (no eutectic) was expected, but images indicate more complex phenomena than Fickian diffusion.

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Experimental Design 1, Formulations: 1, 2 Particle size S NS Degree of temper NS NS Temperature S S Particle size-temper NS NS Particle size-temperature NS NS Temper-temperature NS NS