- Email: [email protected]
Transient process in ice creams evaluated by laser speckles
Food Research International 43 (2010) 1470–1475
Contents lists available at ScienceDirect
Food Research International journal homepage: www.elsevier.com/locate/foodres
Transient process in ice creams evaluated by laser speckles Elieste da Silva Jr. a, Emerson Rodrigo Teixeira da Silva b, Mikiya Muramatsu b, Suzana Caetano da Silva Lannes a,* a b
Biochemical-Pharmaceutical Technology Dept., Pharmaceutical Sciences Faculty, São Paulo Univ. Av. Prof. Lineu Prestes, 580, B. 16, Cidade Univ. São Paulo/SP, CEP 05508-900, Brazil General Physics Dept., Physics Institute, São Paulo Univ. Rua do Matão, Travessa R, 187, Ala 1, Cidade Univ. São Paulo/SP, CEP 05508-090, Brazil
a r t i c l e
i n f o
Article history: Received 24 November 2009 Accepted 28 April 2010
Keywords: Ice creams Sweeteners Speckle Co-occurrence matrix Breakdown emulsion
a b s t r a c t When a coherent light beam is scattered from a colloidal medium, in the observation plane, appears a random grainy image known as speckle pattern. The time evolution of this interference image carries information about the ensemble-averaged dynamics of the scatterer particles. The aim of this work was to evaluate the use of dynamic speckles as an alternative tool to monitoring frozen foams formulated with glucose and fructose syrups. Ice creams, after preparation and packing, were stored at 18 °C. Changes in properties of products were analyzed by speckle phenomena at three room temperatures (20 °C, 25 °C and 30 °C), minute by minute, during 50 min. Two moments were identified in which samples activity achieved significant levels. These instants were associated, respectively, to ice crystals melting and to air bubbles dissipation into the food matrix causing motion of diverse structures. As expected, ice crystals melting was first in formulations containing fructose syrup, but for same samples, air losses were delayed. Speckle methodology was satisfactory to observe temporal evolution of transient process, opening goods prospects to application, still incoming, in foodstuffs researches. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Ice cream and related products are generally aerated and characterized as frozen foams. It is complex-colloidal systems comprised in their frozen state: ice crystals, air bubbles, partially-coalesced fat globules and aggregates, all in discrete phases surrounded by an unfrozen continuous matrix of sugars, proteins, salts, polysaccharides and water (Goff, 2002). The foam stability is correlated to the sensed creaminess and can be improved with smaller air cells and reduced coalescence and is greatly affected by rheological properties of the continuous phase as well as by the viscoelastic properties of the interfacial film (Camacho, Martinez-Navarrete, & Chiralt, 2001; Eisner, Wildmoser, & Windhab, 2005). While formation and the stabilization of the different microstructures involve all of the ice cream ingredients (Granger, Da Costa, Toutain, Barey, & Cansell, 2006), their structure comes from the manufacturing process that includes the steps of preheating, homogenization, pasteurization, ageing, freezing and hardening (Granger, Leger, Barey, Langendorff, & Cansella, 2005). Despite on very low temperatures, in fact, ice cream is in constant evolution. As a result, all the structural and textural characteristics are modified. Therefore, control of ice cream structure is necessary to a better understanding of the phenomena, taking place during
* Corresponding author. Tel.: +55 11 3091 3691. E-mail address: [email protected] (S.C. da S. Lannes). 0963-9969/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2010.04.017
the different stages of manufacture and during storage (Lucas, Wagener, Barey, & Mariette, 2005). Numerous techniques such as dilatometry, ultrasonic velocity measurements, X-ray diffraction, and DSC have been used to study physical state transitions in emulsions (Relkin, Ait-Taleb, Sourdet, & Fosseux, 2003). The melt-down rate of ice cream is affected by many factors, including the amount of air incorporated, the nature of ice crystals, and the network of fat globules formed during freezing. Ice creams with low overruns melted quickly, whereas ice creams with high overruns began to melt slowly and had a good melting resistance. This slower melting rate in ice creams with high overruns was attributed to a reduced rate of heat transfer due to a larger volume of air but may also be due to the more tortuous path through which the melting fluid must flow (Muse & Hartel, 2004). Literature shows that the air bubbles diameter range is located between 30 and 150 lm with a mean diameter around 40 lm (Caillet, Cogné, Andrieu, Laurent, & Rivoire, 2003). In addition, sugars present in the formulations affect dramatically the ice creams melting point temperature because of the cryoscopic effect. The extent of the impact caused by presence of these ingredients is associated with conditions as such spatial configuration, branching chains, molar mass. Sweeteners, depending on type and concentrations, affect the freezing point of the ice cream mix, the amount of frozen water at certain temperature, and the glass transition temperature. The most pronounced structural effect of sweeteners in ice cream is their effect on freezing point but also contribute to viscosity of
E.da Silva et al. / Food Research International 43 (2010) 1470–1475
the unfrozen phase (Miller-Livney & Hartel, 1997). Although general principles are been understood, effect of sucrose or combinations of sweeteners has not been clearly defined, particularly the effect on recrystallization rate (Hagiwara & Hartel, 1996). Ice cream standardization is the mathematical procedure necessary to determine the amounts of the various ingredients needed to make a product of the desired weight and composition. 1.1. Dynamic speckles The scattered light generated by interference between optical fields from a large number of scatterers under coherent light illumination is stochastic and forms a speckle pattern in a far field. The physical basis is that the frequency spectrum of the scattered light is Doppler broadened according to the velocities of all the scattering sites. The shape of the spectrum reveals the nature of the motion, for example, whether it is ballistic or diffusive; the characteristic width of the spectrum reveals the rate of the motion, for example, the root mean-squared speed or the diffusion coefficient. If the sample is nearly transparent, so that incident photons scatter at most once, then the spectrum can be resolved versus scattering angle in order to probe collective motion at different length scales. This is the single-scattering regime. By contrast, if the sample is opaque so that incident photons scatter off many sites before exiting the sample, then any wavevector dependence is lost. The art of DLS (Dynamic Light Scattering) in this regime is known as diffusing-wave spectroscopy (Bandyopadhyay, Gittings, Suh, Dixon, & Durian, 2005). Fig. 1 shows a typical speckle pattern observed in a Fraunhoffer plan. When a laser illuminated surface undergoes a deformation, the speckles appearing in the scattered field show displacement accompanied by change in structure and the resulting speckle pattern is a coded carrier of surface information. As a result, a wide range of metrological techniques of deformation analysis and shape measurements has been developed during the last two decades. When the surface which produces the speckle effect shows some type of activity, the speckle pattern changes in the time and is called dynamic speckle or biospeckle. This effect is a convenient tool to accompany tests and monitoring industrial and biological process presenting practical interesting: process evaluation of metal oxidation (Muramatsu, Guedes, & Gaggioli, 1994); biological activity of seeds (Braga junior et al., 2007), drying of coating (Brun, Brunel, & Snabre, 2006); cerebral blood flow (Zakharov et al., 2009); and dynamic of colloidal dispersions (Snabre & Crassous, 2009).
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The speckle evolution can be evaluated by methods as Fourier Spectrum, Fujii Mapping, Photon Correlation Spectroscopy and others. According to Oulamara, Tribillon, and Duvernoy (1989) changes occurred in the material are recorded in image files called THSP (time history of the speckle pattern) in which evolutions for a same 480 pixels line is monitored minute by minute. Hence, the first line on the THSP corresponds to pixels line in first moment of analysis. The second line corresponds to this same pixels line into the second moment and so on, until the total evolution to be obtained (Fig. 2). For every state of the phenomenon being assessed, successive images are registered of the dynamical speckle pattern and a certain column is selected in each of them. With that column, a new pixels composite image is then constructed. In this image, that we name the time history of the speckle pattern (THSP), the rows represent different points on the object and the columns their intensity state in every sampled instant. The activity of the sample appears as intensity changes in the horizontal direction. When a phenomenon shows low activity, time variations of the speckle pattern are slow and the THSP shows elongated shape. When the phenomenon is very active, the THSP resembles an ordinary speckle pattern. The THSP is assumed to be a representative sample of the state of the phenomenon being assessed when it was registered. We now consider how to use the co-occurrence matrix to characterize it (Figs. 2 and 3). Arizaga, Trivi, and Rabal (1999) had proposed the use of the co-occurrence matrix of the time history of the intensity of a speckle pattern. Co-occurrence matrix of the intensity in the space domain is obtained when a phenomenon evolves slowly, and transitions are expected to occur between neighbor intensity levels while when fluctuations are fast, than transitions between separate levels will be more frequent, a measurement characterizing the spread around its main diagonal. They suggested a measure of the activity based on the use of one of its second order moments, by using some numerical simulations performed for testing purposes, and the co-occurrence matrix (COM) after normalization is defined as Eq. (1). The entries are the number N of occurrences of a certain intensity value i, that is immediately followed by an intensity value j, it is usually used to characterize texture in images. Mij is modified co-occurrence matrix (MCOM).
Nij Mij ¼ P j N ij
ð1Þ
In the upper part of Fig. 3 the scattered surface is static, THSP not shows intensity variations by time, and just the diagonal principal matrix has no null occurrence values Mij. When THSP presents a high activity level (lower part of Fig. 3), the no nulls values scattering around the principal diagonal. So more activity shows the sample, more scattered around of the diagonal are the points distributed (Eq. (2)) (Arizaga et al., 1999). This is a particular second order moment called the inertia moment (IM) of the matrix with respect to its principal diagonal in the row direction.
IM ¼
X
M ij ði jÞ2
ð2Þ
ij
Fig. 1. Typical speckle pattern.
The use of non-contact and low cost methods on monitoring of transient processes is much desired because they could be employed in food industry. Thus, techniques involving speckle phenomena are an excellent alternative to supply this necessity. The aim was to evaluate the applicability of speckle phenomena to monitoring ice creams produced from two different sugar blends.
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E.da Silva Jr. et al. / Food Research International 43 (2010) 1470–1475
Fig. 2. Scheme of THSP construction. In time, the same pixels row is cutted (left) and pasted in a new image where the speckle time evolution is recorded (right).
2. Materials and methods 2.1. Formulation and ice cream production Ice cream mixes are shown in Table 1. After ingredient weighting (batches = 3.0 kg), mixes were preheated, pasteurized in a boiling water (82 °C, 25 s), and cooled at 10 °C. Next step, were homogenized using a mixer Fisaton 713D (850 rpm), and aged under refrigeration conditions (4 °C, 16 h). Aeration and freezing were performed in an ice cream machine (Conservex/Skynsen; 100 rpm), until achieved 7 °C. Ice creams were withdrawed from dasher and packed in plastic containers (V = 2 L) and stored at 18 °C, in a domestic freezer. The air volume incorporated (overrun) was calculated as (mmix = mixture mass; mice cream) = ice cream mass) (Eq. (3)):
% ov errun ¼
mmix mice cream mice cream
ð3Þ
2.2. Speckle patterns acquisitions
Fig. 3. THSPs and corresponding co-occurrence matrices. Low level activity generates THSPs similar to patterns of vertical parallel bars. The corresponding co-occurrence matrix presents points along the main diagonal only (top). Bigger velocities of the scatterer centres imply more randomly THSPs, with co-occurrence matrices presenting spread points around the main diagonal. The ‘‘inertia moment” is a quantitative measure of this dispersion.
Laser speckle acquisitions were performed in Laboratory of Optics and Amorphous Systems, Physics Institute, University of São Paulo. The illumination source was a polarized He–Ne laser (Melles Griot Laser, 20 mW, 633 nm). The register system was a CCD VGA camera (Hitachi KP-M1), driven by Global Lab Image software (DataTranslation Inc.). Sample-detector distance was 30 cm and the illumination angle was 60°. The laser beam had approximately 2 mm for diameter, arising 3 mm2 for illuminated area. A THSP was constructed every minute and the total monitoring time was 50 min. The basic scheme of the experimental set-up is showed in Fig. 4. The room was totally dark during the tests. Ice creams were maintained in a thermal box and its temperature was controlled by help of solid carbon dioxide. Changes were monitored at 20 °C, 25 °C and 30 °C, three samples of each formulation (50 ± 1.0 g per sample) Initial temperature of samples was 12 ± 0.5 °C and its variations were accompanied by TP 870 thermometer, Delta Ohm. Inertia moments in co-occurrence matrices were calculated, according to Eq. (2), using a home made Matlab routine (MatLab 6.0, Math works).
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E.da Silva et al. / Food Research International 43 (2010) 1470–1475 Table 1 Chocolate ice creams formulations (Silva Junior, 2008).
a b c d e f
Ingredient (%)
Sucrosea
Milka powdered
Fatb
GSc
FSd
Chocolate powderede
Emulsifier/stabilizerf
F1 F2
12.0 12.0
14.0 14.0
8.1 8.1
4.0 0
0 4.0
4.0 4.0
0.6 0.6
Powdered sucrose and milk, purchased in local market. Hidrogenated vegetable fat, Glaze, Cargill. GS – glucose syrup, ExcellÒ 1040, 40 DE, corn products. FS – fructose syrup, 80,9 °Brix, Getec. Chocolate powdered, Nestlé. Emulsifier/stabilizer, Cremodan™, SIM-B, Danisco.
2.3. Textural measurements Tests were carried out in a Stable Micro System TA-XT2 using back extrusion cell (A/BE), 5 kg load cell. Dimensions of the back extrusion probe are: inner cup diameter 55 mm; cup height 70 mm and compression plate diameter 45 mm. A texture analyzer TA-XT2 – Stable Micro System was used in order to determine the extrusion force in ice creams from both formulations. Three measurements were performed for each formulation (samples at 12 ± 0.5 °C). The results were analyzed by texture expert software. The test, pre-test and post-test speed were 1 mm/s; the scanned distance was 25 mm; auto-force 0.1 N; data acquisition rate 200 PPS (points per second). 3. Results and discussions 3.1. Speckle analysis The overrun (n = 3 ± sd) found to the ice creams was around 35%. Ice cream and related products are generally aerated and characterized as frozen foams. The gas phase volume varies greatly from a high of greater than 50% to a low of 10–15% (Goff, 2002). Mix containing glucose syrup presented lower overrun. Products added with this carbohydrate generally exhibits higher viscosity and in agreement to Adapa, Dingeldein, Schmidt, and Herald (2000), more viscous systems do not favor foaming capacity. Hence, this parameter could have been the primary cause for decreasing in whipping ability. The results to the intensity momentum versus time were plotted by using of Origin 8.0 (OriginLabs, Inc.). The typical curves to the measurements among 20 °C and 25 °C, for each formulation, are presented in Figs. 5 and 6. The registered low activity to initial instants was tribute to structural rigidity imposed by the ice quantity of the system. The activity was higher as the time, and the two picks were observed at the middle of the curves, suggesting the minimal occurrence of the two different events. In the final monitoring minutes, the values of the intensity momentum were as
Fig. 5. Temporal evolution of samples at 20 °C.
Fig. 6. Temporal evolution of samples at 25 °C.
CCD device
Mirror
Laser Thermocouple
Sample
Fig. 4. Schematic arrangement of data acquisition system.
lower as the levels encountered in initial instants. The point curves show the frontiers between the activity domains. The limits were subjectively established from macroscopic observations of the samples surfaces during the experiments, so it represents an indication of behavior. Because the best definition of the instants of the occurrence of the activity picks, some Gaussian adjustment around of the maximum activity places. The Pearson (R2) coefficient was higher than 0.8 for all picks of the samples, indicating strong cor-
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Table 2 Media time for occurence of peaks (n = 3 ± sd). Formulation
Temperature (°C)
1st peak ± sd (min)
2nd peak ± sd (min)
Dt = t2nd peak t1st peak (min)
F1
20 25
25.4 ± 1.4 17.2 ± 2.9
39.6 ± 1.5 30.0 ± 2.4
14.2 12.8
F2
20 25
19.3 ± 2.0 16.0 ± 1.1
39.3 ± 4.6 31.7 ± 3.3
20.0 15.7
Dt = time variation between peaks. t1st peak = time for occurrence of 1st peak. t2nd peak = time for occurence of 2nd peak.
relation between the experimental values and the adjusted functions. The positions of the picks were assumed as being the average of the Gaussian values. The first pick of the formulations curves was associated to the melting of the ice crystals dispersed in the sample by heating absorption of the room. Migration of air followed of loss on top of samples suggested reduction in strength of bond between several structures, explaining the second peak. Sofjan and Hartel (2004) pointed that generally, drainage involves the rise of air cells and subsequent downward flow of serum phase due to gravity. This effect as well contractions into the food matrix due to viscoelastic nature could be to justify numerous air bubbles which observed on samples surface during analysis. The averages of the instants of the picks maximum values were obtained and the respectively standard deviation were calculated, and the results are showed in Table 2. As higher the temperature as the melting of the samples was lowering, and this process was conducted around 30 °C, when the samples were in a liquid phase. The same behavior was verified to the air loss, and also was observed at both temperatures the fructose syrup formulation has started the melting firstly. In general was noted the doubts associated to the second pick occurrences were bigger than the first one, pointed the biggest complexity of this one. With the speckle methodology was possible to observe that after ice melting, at the same temperature, the air loss of the fructose syrup samples occurred later. This behavior was considered very positive pointing a good perspective of structural maintenance for more time. Mathlouthi (1984) related that structuring properties associated to fructose chains into the systems in which are employed can be associated to metilenic groups (CH2) dispersed along to the monosaccharide molecule. Thus, the structural preservation observed in ice creams added to fructose syrup can also has been caused by effects of this carbohydrate on surfaces of the different crystalline structures present into the products. Typical peaks disappeared at 30 °C. This behavior, probably due to thermal effects, suggests the emulsion breakdown (Fig. 7).
3.2. Texture analysis Extrusion tests are shown in Fig. 8. Due to smaller impact of glucose syrup on freezing temperature, ice creams had higher ice content and hence are harder. This supposition is reasonable since in food matrix only non attached water on hydrophilic sites is available to phase transition. This could be a probable cause to lower hardness in ice creams made with fructose syrup. Solubilities at 0 °C(S) of sugars applied in this work are: Sglucose 35 g/100 g H2O; Ssucrose 65 g/100 g H2O e Sfructose 75 g/100 g H2O (Hartel, 1992). By extrapolation of these data was possible conclude that increase in solutes concentration due to decreasing in solvent content as a consequence to crystallization could had leaded to the partial crystallization, responding by mechanical behavior of the
Fig. 7. Temporal evolution of samples at 30 °C.
Fig. 8. Extrusion forces of the samples.
systems. Thus, higher amount of fructose syrup none crystallized in formulation two could be responsible by smaller hardness. 4. Conclusions This work suggests the viability of the dynamic speckle as a transient process monitoring tool in food emulsions. Two formulations were analyzed at different room temperatures. The results allowed verifying the presence of two intense activities in the samples related to the ice melting and air loss. Moreover, it was possible to observe the different melting range of the formulations. The texture experiments have showed coherence among the behavior observed by speckle and the viscoelastic properties of the systems. Although the difficult for obtaining quantitative data, this work suggests the employment of this technique by industry, because is a simple method, with low cost, and the contact with the sample is not necessary. Acknowledgements We acknowledge to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support and scholarships.
E.da Silva et al. / Food Research International 43 (2010) 1470–1475
References Adapa, S., Dingeldein, H., Schmidt, K. A., & Herald, T. J. (2000). Rheological properties of ice cream mixes and frozen ice cream containing fat and fat replacers. Journal of Dairy Science, 83, 2224–2229. Arizaga, R., Trivi, M., & Rabal, H. (1999). Speckle time evolution characterization by the co-occurence matrix analysis. Optics & Laser Technology, 31, 163–169. Bandyopadhyay, R., Gittings, A. S., Suh, S. S., Dixon, P. K., & Durian, D. J. (2005). Speckle-visibility spectroscopy: A tool to study time-varying dynamics. Review of Scientific Instruments, 76, 093110. Braga, R. A., Junior, Silva, B. O., Rabelo, G., Costa, R. M., Enes, A. M., Cap, N., Rabal, H., Arizaga, R., Trivi, M., & Horgan, G. (2007). Reliability of biospeckle image analysis. Optics and Lasers in Engineering, 45, 390–395. Brun, A., Brunel, L., & Snabre, P. (2006). Adaptive speckle imaging interferometry(ASII): New technology for advanced drying analysis of coatings. Surface Coatings International Part B: Coatings Transactions, 89, 193–268. Caillet, A., Cogné, C., Andrieu, J., Laurent, P., & Rivoire, A. (2003). Characterization of ice cream structure by direct optical microscopy. Influence of freezing parameters. Lebensmittel Wissenchaft und Technologie, 36, 743–749. Camacho, M. M., Martinez-Navarrete, N., & Chiralt, A. (2001). Stability of whipped dairy creams containing locust bean gum/k-carragenan mixtures during freezing-thawing processes. Food Research International, 34, 463–472. Eisner, M. D., Wildmoser, H., & Windhab, E. (2005). Air cell microstructuring in a high viscous ice cream matrix. Colloids and Surfaces A: Physicochemical Engineering Aspects, 263, 390–399. Goff, H. D. (2002). Formation and stabilisation of structure in ice-cream and related products. Current Opinion in Colloid and Interface Science, 7, 432–437. Granger, C., Leger, A., Barey, P., Langendorff, V., & Cansella, M. (2005). Influence of formulation on the structural network in ice cream. International Dairy Journal, 15, 255–262. Granger, C., Da Costa, J. P., Toutain, J., Barey, P., & Cansell, M. (2006). Mapping of ice cream formulation using front-face fluorescence spectroscopy. International Dairy Journal, 16, 489–496. Hagiwara, T., & Hartel, R. W. (1996). Effect of sweetener, stabilizer, and storage temperature on ice recrystallization in ice cream. Journal of Dairy Science, 79, 735–744.
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Hartel, R. W. 1992. Solid–liquid equilibrium: Crystallization in foods. In H. G. Schwartzberg, R. W. Hartel, (Eds.), Physical chemistry of foods (p. 47–81). New York: Marcel Dekker [chap. 2]. Lucas, T., Wagener, M., Barey, P., & Mariette, F. (2005). NMR assessment of mix and ice cream. Effect of formulation on liquid water and ice. International Dairy Journal, 15, 1064–1073. Mathlouthi, M. (1984). Relationship between the structure and the properties of carbohydrates in aqueous solutions: Solute-solvent interactions and the sweetness of D-fructose, D-glucose and sucrose in solution. Food Chemistry, 13, 1–16. Miller-Livney, T., & Hartel, R. W. (1997). Ice recrystallization in ice cream: Interactions between sweeteners and stabilizers. Journal of Dairy Science, 80, 447–456. Muramatsu, M., Guedes, G. H., & Gaggioli, N. G. (1994). Speckle correlation used to study the oxidation process in real-time. Optics and Laser Technology, 26, 167–168. Muse, M. R., & Hartel, R. W. (2004). Ice cream structural elements that affect melting rate and hardness. Journal of Dairy Science, 87, 1–10. Oulamara, A., Tribillon, G., & Duvernoy, J. (1989). Biological activity measurements on botanical specimen surfaces using a temporal decorrelation effect of laser speckle. Journal of Modern Optics, 36, 136–139. Relkin, P., Ait-Taleb, A., Sourdet, S., & Fosseux, P. Y. (2003). Thermal behavior of fat droplets as related to adsorbed milk proteins in complex food emulsions. A DSC study. Journal of the American Oil Chemists Society, 80, 741–746. Silva junior, E., 2008. Special ice creams formulations. Thesis. Faculdade de Ciências Farmacêuticas/Universidade de São Paulo (p. 133), São Paulo, Brazil [Suzana Caetano da Silva Lannes-PhD]. Snabre, P., & Crassous, J. (2009). Multispeckle diffusing wave spectroscopy of colloidal particles suspended in a random packing of glass spheres. The European Physical Journal, 29, 149–155. Sofjan, R. P., & Hartel, R. W. (2004). Effects of overrun on structural and physical characteristics of ice cream. International Dairy Journal, 14, 255–262. Zakharov, P., Volker, A. C., Wyss, M. T., Haiss, F., Calcinaghi, N., Zunzunegui, C., et al. (2009). Dynamic laser speckle imaging of cerebral blood flow. Optics Express, 17, 13904–13917.
Contents lists available at ScienceDirect
Food Research International journal homepage: www.elsevier.com/locate/foodres
Transient process in ice creams evaluated by laser speckles Elieste da Silva Jr. a, Emerson Rodrigo Teixeira da Silva b, Mikiya Muramatsu b, Suzana Caetano da Silva Lannes a,* a b
Biochemical-Pharmaceutical Technology Dept., Pharmaceutical Sciences Faculty, São Paulo Univ. Av. Prof. Lineu Prestes, 580, B. 16, Cidade Univ. São Paulo/SP, CEP 05508-900, Brazil General Physics Dept., Physics Institute, São Paulo Univ. Rua do Matão, Travessa R, 187, Ala 1, Cidade Univ. São Paulo/SP, CEP 05508-090, Brazil
a r t i c l e
i n f o
Article history: Received 24 November 2009 Accepted 28 April 2010
Keywords: Ice creams Sweeteners Speckle Co-occurrence matrix Breakdown emulsion
a b s t r a c t When a coherent light beam is scattered from a colloidal medium, in the observation plane, appears a random grainy image known as speckle pattern. The time evolution of this interference image carries information about the ensemble-averaged dynamics of the scatterer particles. The aim of this work was to evaluate the use of dynamic speckles as an alternative tool to monitoring frozen foams formulated with glucose and fructose syrups. Ice creams, after preparation and packing, were stored at 18 °C. Changes in properties of products were analyzed by speckle phenomena at three room temperatures (20 °C, 25 °C and 30 °C), minute by minute, during 50 min. Two moments were identified in which samples activity achieved significant levels. These instants were associated, respectively, to ice crystals melting and to air bubbles dissipation into the food matrix causing motion of diverse structures. As expected, ice crystals melting was first in formulations containing fructose syrup, but for same samples, air losses were delayed. Speckle methodology was satisfactory to observe temporal evolution of transient process, opening goods prospects to application, still incoming, in foodstuffs researches. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Ice cream and related products are generally aerated and characterized as frozen foams. It is complex-colloidal systems comprised in their frozen state: ice crystals, air bubbles, partially-coalesced fat globules and aggregates, all in discrete phases surrounded by an unfrozen continuous matrix of sugars, proteins, salts, polysaccharides and water (Goff, 2002). The foam stability is correlated to the sensed creaminess and can be improved with smaller air cells and reduced coalescence and is greatly affected by rheological properties of the continuous phase as well as by the viscoelastic properties of the interfacial film (Camacho, Martinez-Navarrete, & Chiralt, 2001; Eisner, Wildmoser, & Windhab, 2005). While formation and the stabilization of the different microstructures involve all of the ice cream ingredients (Granger, Da Costa, Toutain, Barey, & Cansell, 2006), their structure comes from the manufacturing process that includes the steps of preheating, homogenization, pasteurization, ageing, freezing and hardening (Granger, Leger, Barey, Langendorff, & Cansella, 2005). Despite on very low temperatures, in fact, ice cream is in constant evolution. As a result, all the structural and textural characteristics are modified. Therefore, control of ice cream structure is necessary to a better understanding of the phenomena, taking place during
* Corresponding author. Tel.: +55 11 3091 3691. E-mail address: [email protected] (S.C. da S. Lannes). 0963-9969/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2010.04.017
the different stages of manufacture and during storage (Lucas, Wagener, Barey, & Mariette, 2005). Numerous techniques such as dilatometry, ultrasonic velocity measurements, X-ray diffraction, and DSC have been used to study physical state transitions in emulsions (Relkin, Ait-Taleb, Sourdet, & Fosseux, 2003). The melt-down rate of ice cream is affected by many factors, including the amount of air incorporated, the nature of ice crystals, and the network of fat globules formed during freezing. Ice creams with low overruns melted quickly, whereas ice creams with high overruns began to melt slowly and had a good melting resistance. This slower melting rate in ice creams with high overruns was attributed to a reduced rate of heat transfer due to a larger volume of air but may also be due to the more tortuous path through which the melting fluid must flow (Muse & Hartel, 2004). Literature shows that the air bubbles diameter range is located between 30 and 150 lm with a mean diameter around 40 lm (Caillet, Cogné, Andrieu, Laurent, & Rivoire, 2003). In addition, sugars present in the formulations affect dramatically the ice creams melting point temperature because of the cryoscopic effect. The extent of the impact caused by presence of these ingredients is associated with conditions as such spatial configuration, branching chains, molar mass. Sweeteners, depending on type and concentrations, affect the freezing point of the ice cream mix, the amount of frozen water at certain temperature, and the glass transition temperature. The most pronounced structural effect of sweeteners in ice cream is their effect on freezing point but also contribute to viscosity of
E.da Silva et al. / Food Research International 43 (2010) 1470–1475
the unfrozen phase (Miller-Livney & Hartel, 1997). Although general principles are been understood, effect of sucrose or combinations of sweeteners has not been clearly defined, particularly the effect on recrystallization rate (Hagiwara & Hartel, 1996). Ice cream standardization is the mathematical procedure necessary to determine the amounts of the various ingredients needed to make a product of the desired weight and composition. 1.1. Dynamic speckles The scattered light generated by interference between optical fields from a large number of scatterers under coherent light illumination is stochastic and forms a speckle pattern in a far field. The physical basis is that the frequency spectrum of the scattered light is Doppler broadened according to the velocities of all the scattering sites. The shape of the spectrum reveals the nature of the motion, for example, whether it is ballistic or diffusive; the characteristic width of the spectrum reveals the rate of the motion, for example, the root mean-squared speed or the diffusion coefficient. If the sample is nearly transparent, so that incident photons scatter at most once, then the spectrum can be resolved versus scattering angle in order to probe collective motion at different length scales. This is the single-scattering regime. By contrast, if the sample is opaque so that incident photons scatter off many sites before exiting the sample, then any wavevector dependence is lost. The art of DLS (Dynamic Light Scattering) in this regime is known as diffusing-wave spectroscopy (Bandyopadhyay, Gittings, Suh, Dixon, & Durian, 2005). Fig. 1 shows a typical speckle pattern observed in a Fraunhoffer plan. When a laser illuminated surface undergoes a deformation, the speckles appearing in the scattered field show displacement accompanied by change in structure and the resulting speckle pattern is a coded carrier of surface information. As a result, a wide range of metrological techniques of deformation analysis and shape measurements has been developed during the last two decades. When the surface which produces the speckle effect shows some type of activity, the speckle pattern changes in the time and is called dynamic speckle or biospeckle. This effect is a convenient tool to accompany tests and monitoring industrial and biological process presenting practical interesting: process evaluation of metal oxidation (Muramatsu, Guedes, & Gaggioli, 1994); biological activity of seeds (Braga junior et al., 2007), drying of coating (Brun, Brunel, & Snabre, 2006); cerebral blood flow (Zakharov et al., 2009); and dynamic of colloidal dispersions (Snabre & Crassous, 2009).
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The speckle evolution can be evaluated by methods as Fourier Spectrum, Fujii Mapping, Photon Correlation Spectroscopy and others. According to Oulamara, Tribillon, and Duvernoy (1989) changes occurred in the material are recorded in image files called THSP (time history of the speckle pattern) in which evolutions for a same 480 pixels line is monitored minute by minute. Hence, the first line on the THSP corresponds to pixels line in first moment of analysis. The second line corresponds to this same pixels line into the second moment and so on, until the total evolution to be obtained (Fig. 2). For every state of the phenomenon being assessed, successive images are registered of the dynamical speckle pattern and a certain column is selected in each of them. With that column, a new pixels composite image is then constructed. In this image, that we name the time history of the speckle pattern (THSP), the rows represent different points on the object and the columns their intensity state in every sampled instant. The activity of the sample appears as intensity changes in the horizontal direction. When a phenomenon shows low activity, time variations of the speckle pattern are slow and the THSP shows elongated shape. When the phenomenon is very active, the THSP resembles an ordinary speckle pattern. The THSP is assumed to be a representative sample of the state of the phenomenon being assessed when it was registered. We now consider how to use the co-occurrence matrix to characterize it (Figs. 2 and 3). Arizaga, Trivi, and Rabal (1999) had proposed the use of the co-occurrence matrix of the time history of the intensity of a speckle pattern. Co-occurrence matrix of the intensity in the space domain is obtained when a phenomenon evolves slowly, and transitions are expected to occur between neighbor intensity levels while when fluctuations are fast, than transitions between separate levels will be more frequent, a measurement characterizing the spread around its main diagonal. They suggested a measure of the activity based on the use of one of its second order moments, by using some numerical simulations performed for testing purposes, and the co-occurrence matrix (COM) after normalization is defined as Eq. (1). The entries are the number N of occurrences of a certain intensity value i, that is immediately followed by an intensity value j, it is usually used to characterize texture in images. Mij is modified co-occurrence matrix (MCOM).
Nij Mij ¼ P j N ij
ð1Þ
In the upper part of Fig. 3 the scattered surface is static, THSP not shows intensity variations by time, and just the diagonal principal matrix has no null occurrence values Mij. When THSP presents a high activity level (lower part of Fig. 3), the no nulls values scattering around the principal diagonal. So more activity shows the sample, more scattered around of the diagonal are the points distributed (Eq. (2)) (Arizaga et al., 1999). This is a particular second order moment called the inertia moment (IM) of the matrix with respect to its principal diagonal in the row direction.
IM ¼
X
M ij ði jÞ2
ð2Þ
ij
Fig. 1. Typical speckle pattern.
The use of non-contact and low cost methods on monitoring of transient processes is much desired because they could be employed in food industry. Thus, techniques involving speckle phenomena are an excellent alternative to supply this necessity. The aim was to evaluate the applicability of speckle phenomena to monitoring ice creams produced from two different sugar blends.
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Fig. 2. Scheme of THSP construction. In time, the same pixels row is cutted (left) and pasted in a new image where the speckle time evolution is recorded (right).
2. Materials and methods 2.1. Formulation and ice cream production Ice cream mixes are shown in Table 1. After ingredient weighting (batches = 3.0 kg), mixes were preheated, pasteurized in a boiling water (82 °C, 25 s), and cooled at 10 °C. Next step, were homogenized using a mixer Fisaton 713D (850 rpm), and aged under refrigeration conditions (4 °C, 16 h). Aeration and freezing were performed in an ice cream machine (Conservex/Skynsen; 100 rpm), until achieved 7 °C. Ice creams were withdrawed from dasher and packed in plastic containers (V = 2 L) and stored at 18 °C, in a domestic freezer. The air volume incorporated (overrun) was calculated as (mmix = mixture mass; mice cream) = ice cream mass) (Eq. (3)):
% ov errun ¼
mmix mice cream mice cream
ð3Þ
2.2. Speckle patterns acquisitions
Fig. 3. THSPs and corresponding co-occurrence matrices. Low level activity generates THSPs similar to patterns of vertical parallel bars. The corresponding co-occurrence matrix presents points along the main diagonal only (top). Bigger velocities of the scatterer centres imply more randomly THSPs, with co-occurrence matrices presenting spread points around the main diagonal. The ‘‘inertia moment” is a quantitative measure of this dispersion.
Laser speckle acquisitions were performed in Laboratory of Optics and Amorphous Systems, Physics Institute, University of São Paulo. The illumination source was a polarized He–Ne laser (Melles Griot Laser, 20 mW, 633 nm). The register system was a CCD VGA camera (Hitachi KP-M1), driven by Global Lab Image software (DataTranslation Inc.). Sample-detector distance was 30 cm and the illumination angle was 60°. The laser beam had approximately 2 mm for diameter, arising 3 mm2 for illuminated area. A THSP was constructed every minute and the total monitoring time was 50 min. The basic scheme of the experimental set-up is showed in Fig. 4. The room was totally dark during the tests. Ice creams were maintained in a thermal box and its temperature was controlled by help of solid carbon dioxide. Changes were monitored at 20 °C, 25 °C and 30 °C, three samples of each formulation (50 ± 1.0 g per sample) Initial temperature of samples was 12 ± 0.5 °C and its variations were accompanied by TP 870 thermometer, Delta Ohm. Inertia moments in co-occurrence matrices were calculated, according to Eq. (2), using a home made Matlab routine (MatLab 6.0, Math works).
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E.da Silva et al. / Food Research International 43 (2010) 1470–1475 Table 1 Chocolate ice creams formulations (Silva Junior, 2008).
a b c d e f
Ingredient (%)
Sucrosea
Milka powdered
Fatb
GSc
FSd
Chocolate powderede
Emulsifier/stabilizerf
F1 F2
12.0 12.0
14.0 14.0
8.1 8.1
4.0 0
0 4.0
4.0 4.0
0.6 0.6
Powdered sucrose and milk, purchased in local market. Hidrogenated vegetable fat, Glaze, Cargill. GS – glucose syrup, ExcellÒ 1040, 40 DE, corn products. FS – fructose syrup, 80,9 °Brix, Getec. Chocolate powdered, Nestlé. Emulsifier/stabilizer, Cremodan™, SIM-B, Danisco.
2.3. Textural measurements Tests were carried out in a Stable Micro System TA-XT2 using back extrusion cell (A/BE), 5 kg load cell. Dimensions of the back extrusion probe are: inner cup diameter 55 mm; cup height 70 mm and compression plate diameter 45 mm. A texture analyzer TA-XT2 – Stable Micro System was used in order to determine the extrusion force in ice creams from both formulations. Three measurements were performed for each formulation (samples at 12 ± 0.5 °C). The results were analyzed by texture expert software. The test, pre-test and post-test speed were 1 mm/s; the scanned distance was 25 mm; auto-force 0.1 N; data acquisition rate 200 PPS (points per second). 3. Results and discussions 3.1. Speckle analysis The overrun (n = 3 ± sd) found to the ice creams was around 35%. Ice cream and related products are generally aerated and characterized as frozen foams. The gas phase volume varies greatly from a high of greater than 50% to a low of 10–15% (Goff, 2002). Mix containing glucose syrup presented lower overrun. Products added with this carbohydrate generally exhibits higher viscosity and in agreement to Adapa, Dingeldein, Schmidt, and Herald (2000), more viscous systems do not favor foaming capacity. Hence, this parameter could have been the primary cause for decreasing in whipping ability. The results to the intensity momentum versus time were plotted by using of Origin 8.0 (OriginLabs, Inc.). The typical curves to the measurements among 20 °C and 25 °C, for each formulation, are presented in Figs. 5 and 6. The registered low activity to initial instants was tribute to structural rigidity imposed by the ice quantity of the system. The activity was higher as the time, and the two picks were observed at the middle of the curves, suggesting the minimal occurrence of the two different events. In the final monitoring minutes, the values of the intensity momentum were as
Fig. 5. Temporal evolution of samples at 20 °C.
Fig. 6. Temporal evolution of samples at 25 °C.
CCD device
Mirror
Laser Thermocouple
Sample
Fig. 4. Schematic arrangement of data acquisition system.
lower as the levels encountered in initial instants. The point curves show the frontiers between the activity domains. The limits were subjectively established from macroscopic observations of the samples surfaces during the experiments, so it represents an indication of behavior. Because the best definition of the instants of the occurrence of the activity picks, some Gaussian adjustment around of the maximum activity places. The Pearson (R2) coefficient was higher than 0.8 for all picks of the samples, indicating strong cor-
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Table 2 Media time for occurence of peaks (n = 3 ± sd). Formulation
Temperature (°C)
1st peak ± sd (min)
2nd peak ± sd (min)
Dt = t2nd peak t1st peak (min)
F1
20 25
25.4 ± 1.4 17.2 ± 2.9
39.6 ± 1.5 30.0 ± 2.4
14.2 12.8
F2
20 25
19.3 ± 2.0 16.0 ± 1.1
39.3 ± 4.6 31.7 ± 3.3
20.0 15.7
Dt = time variation between peaks. t1st peak = time for occurrence of 1st peak. t2nd peak = time for occurence of 2nd peak.
relation between the experimental values and the adjusted functions. The positions of the picks were assumed as being the average of the Gaussian values. The first pick of the formulations curves was associated to the melting of the ice crystals dispersed in the sample by heating absorption of the room. Migration of air followed of loss on top of samples suggested reduction in strength of bond between several structures, explaining the second peak. Sofjan and Hartel (2004) pointed that generally, drainage involves the rise of air cells and subsequent downward flow of serum phase due to gravity. This effect as well contractions into the food matrix due to viscoelastic nature could be to justify numerous air bubbles which observed on samples surface during analysis. The averages of the instants of the picks maximum values were obtained and the respectively standard deviation were calculated, and the results are showed in Table 2. As higher the temperature as the melting of the samples was lowering, and this process was conducted around 30 °C, when the samples were in a liquid phase. The same behavior was verified to the air loss, and also was observed at both temperatures the fructose syrup formulation has started the melting firstly. In general was noted the doubts associated to the second pick occurrences were bigger than the first one, pointed the biggest complexity of this one. With the speckle methodology was possible to observe that after ice melting, at the same temperature, the air loss of the fructose syrup samples occurred later. This behavior was considered very positive pointing a good perspective of structural maintenance for more time. Mathlouthi (1984) related that structuring properties associated to fructose chains into the systems in which are employed can be associated to metilenic groups (CH2) dispersed along to the monosaccharide molecule. Thus, the structural preservation observed in ice creams added to fructose syrup can also has been caused by effects of this carbohydrate on surfaces of the different crystalline structures present into the products. Typical peaks disappeared at 30 °C. This behavior, probably due to thermal effects, suggests the emulsion breakdown (Fig. 7).
3.2. Texture analysis Extrusion tests are shown in Fig. 8. Due to smaller impact of glucose syrup on freezing temperature, ice creams had higher ice content and hence are harder. This supposition is reasonable since in food matrix only non attached water on hydrophilic sites is available to phase transition. This could be a probable cause to lower hardness in ice creams made with fructose syrup. Solubilities at 0 °C(S) of sugars applied in this work are: Sglucose 35 g/100 g H2O; Ssucrose 65 g/100 g H2O e Sfructose 75 g/100 g H2O (Hartel, 1992). By extrapolation of these data was possible conclude that increase in solutes concentration due to decreasing in solvent content as a consequence to crystallization could had leaded to the partial crystallization, responding by mechanical behavior of the
Fig. 7. Temporal evolution of samples at 30 °C.
Fig. 8. Extrusion forces of the samples.
systems. Thus, higher amount of fructose syrup none crystallized in formulation two could be responsible by smaller hardness. 4. Conclusions This work suggests the viability of the dynamic speckle as a transient process monitoring tool in food emulsions. Two formulations were analyzed at different room temperatures. The results allowed verifying the presence of two intense activities in the samples related to the ice melting and air loss. Moreover, it was possible to observe the different melting range of the formulations. The texture experiments have showed coherence among the behavior observed by speckle and the viscoelastic properties of the systems. Although the difficult for obtaining quantitative data, this work suggests the employment of this technique by industry, because is a simple method, with low cost, and the contact with the sample is not necessary. Acknowledgements We acknowledge to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support and scholarships.
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References Adapa, S., Dingeldein, H., Schmidt, K. A., & Herald, T. J. (2000). Rheological properties of ice cream mixes and frozen ice cream containing fat and fat replacers. Journal of Dairy Science, 83, 2224–2229. Arizaga, R., Trivi, M., & Rabal, H. (1999). Speckle time evolution characterization by the co-occurence matrix analysis. Optics & Laser Technology, 31, 163–169. Bandyopadhyay, R., Gittings, A. S., Suh, S. S., Dixon, P. K., & Durian, D. J. (2005). Speckle-visibility spectroscopy: A tool to study time-varying dynamics. Review of Scientific Instruments, 76, 093110. Braga, R. A., Junior, Silva, B. O., Rabelo, G., Costa, R. M., Enes, A. M., Cap, N., Rabal, H., Arizaga, R., Trivi, M., & Horgan, G. (2007). Reliability of biospeckle image analysis. Optics and Lasers in Engineering, 45, 390–395. Brun, A., Brunel, L., & Snabre, P. (2006). Adaptive speckle imaging interferometry(ASII): New technology for advanced drying analysis of coatings. Surface Coatings International Part B: Coatings Transactions, 89, 193–268. Caillet, A., Cogné, C., Andrieu, J., Laurent, P., & Rivoire, A. (2003). Characterization of ice cream structure by direct optical microscopy. Influence of freezing parameters. Lebensmittel Wissenchaft und Technologie, 36, 743–749. Camacho, M. M., Martinez-Navarrete, N., & Chiralt, A. (2001). Stability of whipped dairy creams containing locust bean gum/k-carragenan mixtures during freezing-thawing processes. Food Research International, 34, 463–472. Eisner, M. D., Wildmoser, H., & Windhab, E. (2005). Air cell microstructuring in a high viscous ice cream matrix. Colloids and Surfaces A: Physicochemical Engineering Aspects, 263, 390–399. Goff, H. D. (2002). Formation and stabilisation of structure in ice-cream and related products. Current Opinion in Colloid and Interface Science, 7, 432–437. Granger, C., Leger, A., Barey, P., Langendorff, V., & Cansella, M. (2005). Influence of formulation on the structural network in ice cream. International Dairy Journal, 15, 255–262. Granger, C., Da Costa, J. P., Toutain, J., Barey, P., & Cansell, M. (2006). Mapping of ice cream formulation using front-face fluorescence spectroscopy. International Dairy Journal, 16, 489–496. Hagiwara, T., & Hartel, R. W. (1996). Effect of sweetener, stabilizer, and storage temperature on ice recrystallization in ice cream. Journal of Dairy Science, 79, 735–744.
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Hartel, R. W. 1992. Solid–liquid equilibrium: Crystallization in foods. In H. G. Schwartzberg, R. W. Hartel, (Eds.), Physical chemistry of foods (p. 47–81). New York: Marcel Dekker [chap. 2]. Lucas, T., Wagener, M., Barey, P., & Mariette, F. (2005). NMR assessment of mix and ice cream. Effect of formulation on liquid water and ice. International Dairy Journal, 15, 1064–1073. Mathlouthi, M. (1984). Relationship between the structure and the properties of carbohydrates in aqueous solutions: Solute-solvent interactions and the sweetness of D-fructose, D-glucose and sucrose in solution. Food Chemistry, 13, 1–16. Miller-Livney, T., & Hartel, R. W. (1997). Ice recrystallization in ice cream: Interactions between sweeteners and stabilizers. Journal of Dairy Science, 80, 447–456. Muramatsu, M., Guedes, G. H., & Gaggioli, N. G. (1994). Speckle correlation used to study the oxidation process in real-time. Optics and Laser Technology, 26, 167–168. Muse, M. R., & Hartel, R. W. (2004). Ice cream structural elements that affect melting rate and hardness. Journal of Dairy Science, 87, 1–10. Oulamara, A., Tribillon, G., & Duvernoy, J. (1989). Biological activity measurements on botanical specimen surfaces using a temporal decorrelation effect of laser speckle. Journal of Modern Optics, 36, 136–139. Relkin, P., Ait-Taleb, A., Sourdet, S., & Fosseux, P. Y. (2003). Thermal behavior of fat droplets as related to adsorbed milk proteins in complex food emulsions. A DSC study. Journal of the American Oil Chemists Society, 80, 741–746. Silva junior, E., 2008. Special ice creams formulations. Thesis. Faculdade de Ciências Farmacêuticas/Universidade de São Paulo (p. 133), São Paulo, Brazil [Suzana Caetano da Silva Lannes-PhD]. Snabre, P., & Crassous, J. (2009). Multispeckle diffusing wave spectroscopy of colloidal particles suspended in a random packing of glass spheres. The European Physical Journal, 29, 149–155. Sofjan, R. P., & Hartel, R. W. (2004). Effects of overrun on structural and physical characteristics of ice cream. International Dairy Journal, 14, 255–262. Zakharov, P., Volker, A. C., Wyss, M. T., Haiss, F., Calcinaghi, N., Zunzunegui, C., et al. (2009). Dynamic laser speckle imaging of cerebral blood flow. Optics Express, 17, 13904–13917.