- Email: [email protected]
Measurement of glass transition temperature by mechanical (DMTA), thermal (DSC and MDSC), water diffusion and density methods: A comparison study
Chemical Physics Letters 440 (2007) 372–377 www.elsevier.com/locate/cplett
Measurement of glass transition temperature by mechanical (DMTA), thermal (DSC and MDSC), water diffusion and density methods: A comparison study Mohammad Shafiur Rahman *, Insaaf Mohd Al-Marhubi, Abdullah Al-Mahrouqi Department of Food Science and Nutrition, College of Agricultural and Marine Sciences, Sultan Qaboos University, P.O. Box 34, Al-Khod-123, Muscat, Sultanate of Oman Received 15 December 2006; in final form 15 April 2007 Available online 25 April 2007
Abstract Glass transition measured by DMTA from the change in slope in storage modulus was 55 C, which was 10.5 C lower than the value measured by tan d peak. Initial glass transition measured by DSC, increased exponentially and reached a constant value of 55 C at or higher heating rate of 30 C/min. Transition temperature, measured by MDSC, remained constant up to heating rate 15 C/min and then decreased. The glass transition values determined from reversible heat flow was 60 C. The break in diffusivity and density (i.e. volume) was observed at 50 C below the glass transition temperature measured by thermal and mechanical methods. 2007 Elsevier B.V. All rights reserved.
1. Introduction The structure of glasses (non-crystalline solids) differs significantly from that of crystals. As a result, glassy materials possess some unique properties important for industrial applications [1]. Glass transition could be used to determine the stability of foods during storage. The significant applications of the glass transition concept in the 1980s were emerged in food processing when Levine and Slade [2], and Slade and Levine [3] identified its merits in food processing, and food’s stability during storage [4]. The most common method used to determine glass transition is the differential scanning calorimetry (DSC) that detects the change in heat capacity (i.e. a shift in the base line) occurring over the two periods. Recently, modulated DSC (MDSC) is being used to increase the sensitivity and resolution of complex thermal events. The thermomechanical analysis (TMA, DMA and DMTA) and oscillation methods are less commonly used, however these methods are more sensitive. Thermo-mechanical (TMA) *
Corresponding author. Fax: +968 24413418. E-mail address: shafi[email protected] (M.S. Rahman).
0009-2614/$ - see front matter 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.cplett.2007.04.067
measures the change of slope when dimension is plotted against temperature. Differential mechanical thermal analysis (DMTA) and oscillation methods measure the storage and loss modulus as a function of temperature. Each method has its advantages and limitations over other methods. In the literature negligible work has been done to compare different methods for measuring the glass transition. Variation of data exists when different methods are used by different researchers for measuring glass transition, for example, the transition of trehalose varied from 75 to 120 C [4]. The objectives of this study were to measure the glass transition temperature using thermal analysis (DSC and MDSC), differential mechanical thermal analysis (DMTA), moisture diffusivity and density measurements. The data obtained by several methods are discussed considering spaghetti as a model system. 2. Materials and methods 2.1. Source of materials Commercial ribbon shaped spaghetti produced in France was purchased from the local supermarket in
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
Muscat and stored in an air tight glass bottle at 20 C until used for the experiments. Moisture content of the sample was determined from the solids content by drying spaghetti sample in an over at 105 C for at least 18 h. 3. Differential mechanical thermal analysis (DMTA) The dynamic mechanical thermal analyzer DMTA V (Rheometrics, Piscataway, NJ) in compression and parallel-plate geometry was used to determine the E 0 (storage modulus), E00 (loss modulus), and tan d. Initially linear viscoelastic region was determined at a 0.6% compression with a frequency range 0.1–100 Hz. To conduct temperature sweeps, samples were heated at a heating rate of 5 C/min from 25 to 120 C keeping 0.6% compression with varied frequency from 0.001 to 2 Hz. The glass transition temperature was measured from the change of slope or break in E 0 and peak of tan d. Statistical comparison of the data was done by glm procedure of SAS [5] at 5% significance level. 3.1. Differential scanning calorimetry (DSC and modulated DSC) The glass transition of spaghetti samples were measured by differential scanning calorimetry with and without modulation (DSC Q10, MDSC Q1000, TA Instruments, New Castle, Delware). Details of the DSC and MDSC instruments including its operation and calibration procedure were presented in the earlier papers [6,7]. Cut samples of 10–20 mg was placed in a sealed aluminum pan were cooled to –50 C at 5 C/min, and equilibrated for 10 min. After equilibration it was scanned from 50 to 150 C at a constant rate which varied from 1 to 60 C/min in each experiment of DSC. Each thermogram was analyzed for the onset, mid and end of glass transition from a shift in the base line. In the case of MDSC, samples were scanned from 50 to 120 C at a constant rate within 3– 30 C/min with a modulation of ±0.53 C amplitude and 40 s period of modulation. Thermograms were analyzed from its total, reversible and non-reversible heat flow. The average values and standard deviations of 3–6 replicates were obtained. 3.2. Moisture diffusivity measurement The analytical solution of Fick’s law for infinite slab was used to estimate the moisture diffusivity [8]: " # 2 X X w X we 8 n¼1 1 ð2n þ 1Þ p2 De t ¼ exp X wo X we p2 n¼0 ð2n þ 1Þ2 L2
ð1Þ
where Xwo, Xwe, Xw are the moisture content at initial, equilibrium, and at any time during drying (wet basis: kg water/kg sample); L is the thickness of the slab (m), t is the drying time (s), and De is the effective moisture diffusiv-
373
ity (m2/s), respectively. One term equation can be transformed based on the assumptions provided by Rizvi [9] as: 2 X w X we 8 p De t ¼ exp 2 ð2Þ X wo X we p2 L The effective diffusivity was estimated from the slope of a semi-log plot of the moisture ratio (Xw Xwe)/(Xwo Xwe) versus time as: De ¼
Slope L2 p2
ð3Þ
Moisture content as a function of drying time was measured in dynamic isopiestic method (DIM) [10]. Approximately 70 mg (size: 15 mm · 4 mm · 0.04 mm) sample was placed in the balance inside the sample chamber. The DIM was set at a desired temperature and 10% relative humidity for performing drying kinetics. The loss of mass of the sample as a function of drying time was used to estimate the effective moisture diffusivity within the temperature range 10–80 C. 3.3. Density measurement Material density as a function of temperature was measured by pycnometer method using specific gravity bottle of 25 cm3 and approximately 30 g of ground spaghetti powder or small pieces [11]. Vegetable oil was used as a displacing liquid. The specific gravity bottle filled with sample and oil was placed in an oven and kept it at a specified temperature for at least 30 min and then weighed after wiping out (with soft tissue paper) excess oil on the surface of the bottle. The material density was estimated from the volume and mass of spaghetti assuming all pores are filled with oil. 4. Results and discussion 4.1. Differential mechanical thermal analysis (DMTA) The moisture content of spaghetti was 9.77 kg water/ 100 kg spaghetti. Frequency sweep is plotted in Fig. 1 shows that the viscoelastic region could be achieved up to frequency 10 Hz. After 10 Hz the storage modulus (E 0 ) decreased from its linearity, and similarly E00 and tan d increased from 1 Hz (Fig. 1), thus frequency up to 1 Hz could be used to measure the glass transition. Fig. 2 shows a typical plot of E 0 , E00 and tan d as a function of temperature indicating glass and rubber regions. The glass transition is usually identified when a shift or change in slope of E 0 (definition 1: transition from solid to rubber), peak of E00 (definition 2) and peak of tan d (definition 3: domination of liquid behavior over solid) are observed [4]. The transitions determined are presented in the Table 1. It is difficult to find a maximum in E00 as shown in Fig. 2. The variations within the frequency 0.002–10 Hz showed no significant effect in determining the glass (p > 0.05) (Fig. 3). The glass transition measured from the change in slope in E 0 (Tgi: 55 C) was significantly lower
374
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
Fig. 1. Storage and loss modulus as a function of frequency at 0.6% compression and 25 C.
Fig. 3. Glass transition temperature measured by DMTA method as a function of frequency.
Fig. 4. DSC thermogram at a heating rate of 10 C/min. Fig. 2. Storage and loss modulus of spaghetti at 1 Hz and 0.6% compression.
Table 1 Glass transition of spaghetti by differential mechanical thermal analysis (DMTA) Frequency (Hz)
Tgi form E 0 (C)
Tgd form d peak (C)
N
0.002 0.01 0.1 0.15 0.30 0.50 1.0 2.0 10.0 Average
60 50 60 (14) 45 60 60 59 (15) 55 43 55 (7)
48 70 80 (5) 65 70 – 61 (8) 70 60 65.5 (9.5)
1 1 2 1 1 1 5 1 2
Note: N is the numbers of sample.
than the value from tan d (Tgp: 65.5 C) (p < 0.05). Similarly around 20 C lower transition temperature was observed for different types of proteins and starch [12–15]. 4.2. Differential scanning calorimetry (DSC) Fig. 4 shows a typical DSC thermogram showing a shift in the heat flow baseline and transition temperatures are presented in Table 2. Fig. 5 shows the glass transition as
a function of heating rate measured by DSC and MDSC. In the case of DSC, glass transition (initial is shown) increased exponentially and reached to a constant value of 55 C at or above heating rate of 30 C/min. Similar exponential rise was observed from 1 C/min and then reached constant after 10 C/min [16] and others observed linear increase [6,12,17]. 4.3. Modulated differential scanning calorimetry (MDSC) The shape of the total heat flow at 15 C/min and shift observed in Fig. 6 is relatively different from the DSC thermogram as in Fig. 4. In Fig. 7, the reversible heat flow showed a clear shift in the base line at its glass transition. The non-reversible (i.e. kinetic change) heat flow showed an endothermic peak (marked A in Fig. 8), which could be attributed to hydrogen bond disruption within proteins or other structural changes in the components of a matrix [18] or the dehydration of immobilized water of protein [19]. The transition from the total heat flow remained constant at 50 C up to heating rate 15 C/min, and then decreased significantly, whereas transition from reversible heat flow remained constant at 60 C and then decreased (Fig. 5). In the case of spaghetti, equilibrium DSC transition was similar to the MDSC values determined from the reversible heat flow, whereas MDSC total heat flow
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
375
Table 2 Glass transition from differential scanning calorimetry (DSC) and modulated differential scanning calorimetry (MDSC) method Differential scanning calorimetry (DSC) method
Modulated differential scanning calorimetry (MDSC) method
rc
Tgi
1 5 10 15 20 30 50 60
26 41 46 45 49 57 55 56
Total heat flow
(11) (13) (5) (3) (6) (11) (10) (4)
Tgp
Tge
rc
Tgi
46 64 61 68 66 77 80 79
61 (5) 76 (18) 71 (9) 81 (9) 76 (7) 98 (10) 102 (15) 107 (7)
3 5 15 30
50 51 50 38
(15) (15) (2) (7) (17) (16) (15) (7)
(10) (4) (5) (6)
Reversible heat flow Tgp
Tge
Tgi
65 67 57 54
70 74 67 59
59 62 59 41
(17) (17) (7) (2)
(19) (23) (2) (14)
(2) (19) (14) (3)
Tgp
Tge
63 64 65 52
81 91 69 61
(3) (2) (8) (15)
(8) (20) (9) (15)
rc: Heating rate (C/min).
Fig. 5. Glass transition temperature (initial) as a function of heating rate based on DSC and MDSC thermograms.
Fig. 7. MDSC reversible heat flow thermogram at a heating rate of 15 C/min.
Fig. 6. MDSC total heat flow thermogram at a heating rate of 15 C/min.
showed 10 C lower value. The value determined from the reversible heat flow is considered most reliable since it was separated from the interference of the other kinetic changes in the sample. Fig. 9 shows the total thermogram at 30 C/ min heating rate and the observed shift was much lower than the heating rate at or below 15 C/min. In addition numbers of exothermic and endothermic peaks are observed both in reversible and non-reversible thermograms (Figs. 10 and 11). The structural change of ordering in hydrogen-atom positions within the network may result to move the glass transition earlier and may cause unexpected spontaneous exothermic and endothermic effects [20]. Thus the heating rate could significantly affects the glass transition determined from DSC or MDSC thermo-
Fig. 8. MDSC non-reversible heat flow thermogram at a heating rate of 15 C/min.
gram. In the literature thermal method showed lower values compared to the rheological methods [13,15,17,21]. The DSC would be expected to be at a lower temperature than the tan d peak as it is effectively a static technique [15] and possibility of moisture loss in DMTA [22]. 4.4. Moisture diffusivity The slope of the second linear portion of the semi-log plot was used to estimate the effective moisture diffusivity (Fig. 12). In the glass transition region effective diffusivity increased from 2 · 1012 to 5 · 1010 m2/s when temperature
376
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
Fig. 9. MDSC total heat flow thermogram at a heating rate of 30 C/min.
varied from 10 to 50 C. In the rubbery region the diffusivity remained nearly constant at about 5 · 1010 m2/s. Fig. 12 shows a clear break at 50 C, which was 10 C (Tc/ Tgi = 0.83) below the MDSC glass transition temperature of 60 C (Fig. 13). This indicated molecular mobility of water started 10 C below the glass transition. The molecular mobility of many reactants exists even below glass transition causing the continuation of chemical or biochemical reactions [4,23–25]. Similarly the break or decoupling in diffusivity was observed at 1.16 and 1.14 for other systems [26,27]. Porosity and pore-size also affect effective diffusivity, however the porosity of spaghetti was found low of value of 0.023 (data not shown). The measurement of critical temperature from diffusivity measurement could be very helpful when conventional thermal and mechanical methods are unable to trace the transition [7]. 4.5. Material density Fig. 14 shows a break or change of slope at 50 C which is below the MDSC glass transition of 60 C. Free volume theory indicates that at glassy state the free volume in the matrix is lower than the rubbery state. In the case of powder sample the change in slope is less sensitive compared to the broken sample since applied vacuum process may
Fig. 10. MDSC reversible heat flow thermogram at a heating rate of 30 C/min.
Fig. 11. MDSC non-reversible heat flow thermogram at a heating rate of 30 C/min. Fig. 13. Moisture diffusivity as a function of drying temperature.
Fig. 12. Plot of Ln [(Xw/Xwe)/(Xwo Xwe)] as a function of drying time at 10 C.
Fig. 14. Material density of spaghetti as a function of temperature (series1: broken sample, series2: powder sample).
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
unable to remove all the air inside the complicated pores inside powder (Fig. 14). 5. Conclusions In measuring the glass transition it is important to define glass transition and to present heating rate in the case of DSC or MDSC, and frequency in the case of DMTA. The break in diffusivity and density occurred at 50 C which is lower than the glass transition measured by thermal or mechanical methods. This indicated that the significant change in molecular mobility and free volume occurred much earlier than the glass transition as measured by thermal and mechanical methods. Acknowledgement The project was supported by the Sultan Qaboos University, Sultanate of Oman. References [1] [2] [3] [4] [5] [6] [7]
A.P. Sokolov, Endeavour 21 (3) (1997) 109. H. Levine, L. Slade, Carbohyd. Polym. 6 (1986) 213. L. Slade, H. Levine, Pure Appl. Chem. 60 (1988) 1841. M.S. Rahman, Trends Food Sci. Technol. 17 (2006) 129. SAS, SAS Users’ Guide: Statistics, SAS Institute, NC, 2001. M.S. Rahman, Int. J. Food Prop. 7 (3) (2004) 407. M.S. Rahman, S. Kasapis, N.S.Z. Al-Kharusi, I.M. Al-Marhubi, A.J. Khan, J. Food Eng. 80 (1) (2007) 1. [8] J. Crank, The Mathematics of Diffusion, Oxford University Press, Oxford, 1975.
377
[9] S.S.H. Rizvi, in: M.A. Rao, S.S.H. Rizvi, A.K. Datta (Eds.), Engineering Properties of Foods, Third edn., CRC Press, Boca Raton, FL, 2005, p. 239. [10] M.S. Rahman, R.H. Al-Belushi, Int. J. Food Prop. 9 (3) (2006) 421. [11] M.S. Rahman, Food Properties Handbook, CRC Press, Boca Raton, FL, 1995. [12] S. Kasapis, I.M. Al-Marhoobi, J.R. Mitchell, Carbohyd. Res. 338 (2003) 787. [13] L. Di Gioia, B. Cuq, S. Guilbert, Int. J. Biol. Macromol. 24 (1999) 341. [14] M. Pouplin, A. Redl, N. Gontard, J. Agric. Food Chem. 47 (1999) 538. [15] M.T. Kalichevsky, E.M. Jaroszkiewicz, S. Ablett, J.M.V. Blanshard, P.J. Lillford, Carbohyd. Polym. 18 (1992) 77. [16] T.J. Maurice, Y.J. Asher, S. Thomson, in: H. Levine, L. Slade (Eds.), Water Relationships in Food, Plenum Press, New York, 1991, p. 215. [17] A.M. Cocero, J.L. Kokini, J. Rheol. 35 (2) (1991) 257. [18] L.N. Bell, D.E. Touma, J. Food Sci. 61 (1996) 807, 828. [19] C.H. Tang, H. Wu, H.P. Yu, L. Lin, Z. Chen, X.Q. Yang, J. Food Biochem. 29 (2005) 402. [20] K. Kawai, T. Suzuki, M. Oguni, Thermochim. Acta 431 (2005) 4. [21] E.M. De Graaf, H. Madeka, A.M. Cocero, J.L. Kokini, Biotechnol. Progr. 9 (1993) 210. [22] T. Hashimoto, T. Hagiwara, T. Suzuki, R. Takai, Fisheries Sci. 69 (2003) 1290. [23] G. Zografi, S.R. Byrn, in: Y.H. Roos, R.B. Leslie, P.J. Lillford (Eds.), Water Management in the Design and Distribution of Quality Foods, Technomic Publishing, Lancaster, 1999, p. 397. [24] D. Champion, M. Le Meste, D. Simatos, Trends Food Sci. Technol. 11 (2000) 41. [25] I. Tan, C.C. Wee, P.A. Sopade, P.J. Halley, Carbohyd. Polym. 58 (2004) 191. [26] D. Champion, H. Hervet, G. Blond, M. Le Meste, D. Simatos, J. Phys. Chem. B. 10 (1997) 10674. [27] Y.M. Gunning, R. Parker, S.G. Ring, Carbohyd. Res. 329 (2000) 377.
Measurement of glass transition temperature by mechanical (DMTA), thermal (DSC and MDSC), water diffusion and density methods: A comparison study Mohammad Shafiur Rahman *, Insaaf Mohd Al-Marhubi, Abdullah Al-Mahrouqi Department of Food Science and Nutrition, College of Agricultural and Marine Sciences, Sultan Qaboos University, P.O. Box 34, Al-Khod-123, Muscat, Sultanate of Oman Received 15 December 2006; in final form 15 April 2007 Available online 25 April 2007
Abstract Glass transition measured by DMTA from the change in slope in storage modulus was 55 C, which was 10.5 C lower than the value measured by tan d peak. Initial glass transition measured by DSC, increased exponentially and reached a constant value of 55 C at or higher heating rate of 30 C/min. Transition temperature, measured by MDSC, remained constant up to heating rate 15 C/min and then decreased. The glass transition values determined from reversible heat flow was 60 C. The break in diffusivity and density (i.e. volume) was observed at 50 C below the glass transition temperature measured by thermal and mechanical methods. 2007 Elsevier B.V. All rights reserved.
1. Introduction The structure of glasses (non-crystalline solids) differs significantly from that of crystals. As a result, glassy materials possess some unique properties important for industrial applications [1]. Glass transition could be used to determine the stability of foods during storage. The significant applications of the glass transition concept in the 1980s were emerged in food processing when Levine and Slade [2], and Slade and Levine [3] identified its merits in food processing, and food’s stability during storage [4]. The most common method used to determine glass transition is the differential scanning calorimetry (DSC) that detects the change in heat capacity (i.e. a shift in the base line) occurring over the two periods. Recently, modulated DSC (MDSC) is being used to increase the sensitivity and resolution of complex thermal events. The thermomechanical analysis (TMA, DMA and DMTA) and oscillation methods are less commonly used, however these methods are more sensitive. Thermo-mechanical (TMA) *
Corresponding author. Fax: +968 24413418. E-mail address: shafi[email protected] (M.S. Rahman).
0009-2614/$ - see front matter 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.cplett.2007.04.067
measures the change of slope when dimension is plotted against temperature. Differential mechanical thermal analysis (DMTA) and oscillation methods measure the storage and loss modulus as a function of temperature. Each method has its advantages and limitations over other methods. In the literature negligible work has been done to compare different methods for measuring the glass transition. Variation of data exists when different methods are used by different researchers for measuring glass transition, for example, the transition of trehalose varied from 75 to 120 C [4]. The objectives of this study were to measure the glass transition temperature using thermal analysis (DSC and MDSC), differential mechanical thermal analysis (DMTA), moisture diffusivity and density measurements. The data obtained by several methods are discussed considering spaghetti as a model system. 2. Materials and methods 2.1. Source of materials Commercial ribbon shaped spaghetti produced in France was purchased from the local supermarket in
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
Muscat and stored in an air tight glass bottle at 20 C until used for the experiments. Moisture content of the sample was determined from the solids content by drying spaghetti sample in an over at 105 C for at least 18 h. 3. Differential mechanical thermal analysis (DMTA) The dynamic mechanical thermal analyzer DMTA V (Rheometrics, Piscataway, NJ) in compression and parallel-plate geometry was used to determine the E 0 (storage modulus), E00 (loss modulus), and tan d. Initially linear viscoelastic region was determined at a 0.6% compression with a frequency range 0.1–100 Hz. To conduct temperature sweeps, samples were heated at a heating rate of 5 C/min from 25 to 120 C keeping 0.6% compression with varied frequency from 0.001 to 2 Hz. The glass transition temperature was measured from the change of slope or break in E 0 and peak of tan d. Statistical comparison of the data was done by glm procedure of SAS [5] at 5% significance level. 3.1. Differential scanning calorimetry (DSC and modulated DSC) The glass transition of spaghetti samples were measured by differential scanning calorimetry with and without modulation (DSC Q10, MDSC Q1000, TA Instruments, New Castle, Delware). Details of the DSC and MDSC instruments including its operation and calibration procedure were presented in the earlier papers [6,7]. Cut samples of 10–20 mg was placed in a sealed aluminum pan were cooled to –50 C at 5 C/min, and equilibrated for 10 min. After equilibration it was scanned from 50 to 150 C at a constant rate which varied from 1 to 60 C/min in each experiment of DSC. Each thermogram was analyzed for the onset, mid and end of glass transition from a shift in the base line. In the case of MDSC, samples were scanned from 50 to 120 C at a constant rate within 3– 30 C/min with a modulation of ±0.53 C amplitude and 40 s period of modulation. Thermograms were analyzed from its total, reversible and non-reversible heat flow. The average values and standard deviations of 3–6 replicates were obtained. 3.2. Moisture diffusivity measurement The analytical solution of Fick’s law for infinite slab was used to estimate the moisture diffusivity [8]: " # 2 X X w X we 8 n¼1 1 ð2n þ 1Þ p2 De t ¼ exp X wo X we p2 n¼0 ð2n þ 1Þ2 L2
ð1Þ
where Xwo, Xwe, Xw are the moisture content at initial, equilibrium, and at any time during drying (wet basis: kg water/kg sample); L is the thickness of the slab (m), t is the drying time (s), and De is the effective moisture diffusiv-
373
ity (m2/s), respectively. One term equation can be transformed based on the assumptions provided by Rizvi [9] as: 2 X w X we 8 p De t ¼ exp 2 ð2Þ X wo X we p2 L The effective diffusivity was estimated from the slope of a semi-log plot of the moisture ratio (Xw Xwe)/(Xwo Xwe) versus time as: De ¼
Slope L2 p2
ð3Þ
Moisture content as a function of drying time was measured in dynamic isopiestic method (DIM) [10]. Approximately 70 mg (size: 15 mm · 4 mm · 0.04 mm) sample was placed in the balance inside the sample chamber. The DIM was set at a desired temperature and 10% relative humidity for performing drying kinetics. The loss of mass of the sample as a function of drying time was used to estimate the effective moisture diffusivity within the temperature range 10–80 C. 3.3. Density measurement Material density as a function of temperature was measured by pycnometer method using specific gravity bottle of 25 cm3 and approximately 30 g of ground spaghetti powder or small pieces [11]. Vegetable oil was used as a displacing liquid. The specific gravity bottle filled with sample and oil was placed in an oven and kept it at a specified temperature for at least 30 min and then weighed after wiping out (with soft tissue paper) excess oil on the surface of the bottle. The material density was estimated from the volume and mass of spaghetti assuming all pores are filled with oil. 4. Results and discussion 4.1. Differential mechanical thermal analysis (DMTA) The moisture content of spaghetti was 9.77 kg water/ 100 kg spaghetti. Frequency sweep is plotted in Fig. 1 shows that the viscoelastic region could be achieved up to frequency 10 Hz. After 10 Hz the storage modulus (E 0 ) decreased from its linearity, and similarly E00 and tan d increased from 1 Hz (Fig. 1), thus frequency up to 1 Hz could be used to measure the glass transition. Fig. 2 shows a typical plot of E 0 , E00 and tan d as a function of temperature indicating glass and rubber regions. The glass transition is usually identified when a shift or change in slope of E 0 (definition 1: transition from solid to rubber), peak of E00 (definition 2) and peak of tan d (definition 3: domination of liquid behavior over solid) are observed [4]. The transitions determined are presented in the Table 1. It is difficult to find a maximum in E00 as shown in Fig. 2. The variations within the frequency 0.002–10 Hz showed no significant effect in determining the glass (p > 0.05) (Fig. 3). The glass transition measured from the change in slope in E 0 (Tgi: 55 C) was significantly lower
374
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
Fig. 1. Storage and loss modulus as a function of frequency at 0.6% compression and 25 C.
Fig. 3. Glass transition temperature measured by DMTA method as a function of frequency.
Fig. 4. DSC thermogram at a heating rate of 10 C/min. Fig. 2. Storage and loss modulus of spaghetti at 1 Hz and 0.6% compression.
Table 1 Glass transition of spaghetti by differential mechanical thermal analysis (DMTA) Frequency (Hz)
Tgi form E 0 (C)
Tgd form d peak (C)
N
0.002 0.01 0.1 0.15 0.30 0.50 1.0 2.0 10.0 Average
60 50 60 (14) 45 60 60 59 (15) 55 43 55 (7)
48 70 80 (5) 65 70 – 61 (8) 70 60 65.5 (9.5)
1 1 2 1 1 1 5 1 2
Note: N is the numbers of sample.
than the value from tan d (Tgp: 65.5 C) (p < 0.05). Similarly around 20 C lower transition temperature was observed for different types of proteins and starch [12–15]. 4.2. Differential scanning calorimetry (DSC) Fig. 4 shows a typical DSC thermogram showing a shift in the heat flow baseline and transition temperatures are presented in Table 2. Fig. 5 shows the glass transition as
a function of heating rate measured by DSC and MDSC. In the case of DSC, glass transition (initial is shown) increased exponentially and reached to a constant value of 55 C at or above heating rate of 30 C/min. Similar exponential rise was observed from 1 C/min and then reached constant after 10 C/min [16] and others observed linear increase [6,12,17]. 4.3. Modulated differential scanning calorimetry (MDSC) The shape of the total heat flow at 15 C/min and shift observed in Fig. 6 is relatively different from the DSC thermogram as in Fig. 4. In Fig. 7, the reversible heat flow showed a clear shift in the base line at its glass transition. The non-reversible (i.e. kinetic change) heat flow showed an endothermic peak (marked A in Fig. 8), which could be attributed to hydrogen bond disruption within proteins or other structural changes in the components of a matrix [18] or the dehydration of immobilized water of protein [19]. The transition from the total heat flow remained constant at 50 C up to heating rate 15 C/min, and then decreased significantly, whereas transition from reversible heat flow remained constant at 60 C and then decreased (Fig. 5). In the case of spaghetti, equilibrium DSC transition was similar to the MDSC values determined from the reversible heat flow, whereas MDSC total heat flow
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
375
Table 2 Glass transition from differential scanning calorimetry (DSC) and modulated differential scanning calorimetry (MDSC) method Differential scanning calorimetry (DSC) method
Modulated differential scanning calorimetry (MDSC) method
rc
Tgi
1 5 10 15 20 30 50 60
26 41 46 45 49 57 55 56
Total heat flow
(11) (13) (5) (3) (6) (11) (10) (4)
Tgp
Tge
rc
Tgi
46 64 61 68 66 77 80 79
61 (5) 76 (18) 71 (9) 81 (9) 76 (7) 98 (10) 102 (15) 107 (7)
3 5 15 30
50 51 50 38
(15) (15) (2) (7) (17) (16) (15) (7)
(10) (4) (5) (6)
Reversible heat flow Tgp
Tge
Tgi
65 67 57 54
70 74 67 59
59 62 59 41
(17) (17) (7) (2)
(19) (23) (2) (14)
(2) (19) (14) (3)
Tgp
Tge
63 64 65 52
81 91 69 61
(3) (2) (8) (15)
(8) (20) (9) (15)
rc: Heating rate (C/min).
Fig. 5. Glass transition temperature (initial) as a function of heating rate based on DSC and MDSC thermograms.
Fig. 7. MDSC reversible heat flow thermogram at a heating rate of 15 C/min.
Fig. 6. MDSC total heat flow thermogram at a heating rate of 15 C/min.
showed 10 C lower value. The value determined from the reversible heat flow is considered most reliable since it was separated from the interference of the other kinetic changes in the sample. Fig. 9 shows the total thermogram at 30 C/ min heating rate and the observed shift was much lower than the heating rate at or below 15 C/min. In addition numbers of exothermic and endothermic peaks are observed both in reversible and non-reversible thermograms (Figs. 10 and 11). The structural change of ordering in hydrogen-atom positions within the network may result to move the glass transition earlier and may cause unexpected spontaneous exothermic and endothermic effects [20]. Thus the heating rate could significantly affects the glass transition determined from DSC or MDSC thermo-
Fig. 8. MDSC non-reversible heat flow thermogram at a heating rate of 15 C/min.
gram. In the literature thermal method showed lower values compared to the rheological methods [13,15,17,21]. The DSC would be expected to be at a lower temperature than the tan d peak as it is effectively a static technique [15] and possibility of moisture loss in DMTA [22]. 4.4. Moisture diffusivity The slope of the second linear portion of the semi-log plot was used to estimate the effective moisture diffusivity (Fig. 12). In the glass transition region effective diffusivity increased from 2 · 1012 to 5 · 1010 m2/s when temperature
376
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
Fig. 9. MDSC total heat flow thermogram at a heating rate of 30 C/min.
varied from 10 to 50 C. In the rubbery region the diffusivity remained nearly constant at about 5 · 1010 m2/s. Fig. 12 shows a clear break at 50 C, which was 10 C (Tc/ Tgi = 0.83) below the MDSC glass transition temperature of 60 C (Fig. 13). This indicated molecular mobility of water started 10 C below the glass transition. The molecular mobility of many reactants exists even below glass transition causing the continuation of chemical or biochemical reactions [4,23–25]. Similarly the break or decoupling in diffusivity was observed at 1.16 and 1.14 for other systems [26,27]. Porosity and pore-size also affect effective diffusivity, however the porosity of spaghetti was found low of value of 0.023 (data not shown). The measurement of critical temperature from diffusivity measurement could be very helpful when conventional thermal and mechanical methods are unable to trace the transition [7]. 4.5. Material density Fig. 14 shows a break or change of slope at 50 C which is below the MDSC glass transition of 60 C. Free volume theory indicates that at glassy state the free volume in the matrix is lower than the rubbery state. In the case of powder sample the change in slope is less sensitive compared to the broken sample since applied vacuum process may
Fig. 10. MDSC reversible heat flow thermogram at a heating rate of 30 C/min.
Fig. 11. MDSC non-reversible heat flow thermogram at a heating rate of 30 C/min. Fig. 13. Moisture diffusivity as a function of drying temperature.
Fig. 12. Plot of Ln [(Xw/Xwe)/(Xwo Xwe)] as a function of drying time at 10 C.
Fig. 14. Material density of spaghetti as a function of temperature (series1: broken sample, series2: powder sample).
M.S. Rahman et al. / Chemical Physics Letters 440 (2007) 372–377
unable to remove all the air inside the complicated pores inside powder (Fig. 14). 5. Conclusions In measuring the glass transition it is important to define glass transition and to present heating rate in the case of DSC or MDSC, and frequency in the case of DMTA. The break in diffusivity and density occurred at 50 C which is lower than the glass transition measured by thermal or mechanical methods. This indicated that the significant change in molecular mobility and free volume occurred much earlier than the glass transition as measured by thermal and mechanical methods. Acknowledgement The project was supported by the Sultan Qaboos University, Sultanate of Oman. References [1] [2] [3] [4] [5] [6] [7]
A.P. Sokolov, Endeavour 21 (3) (1997) 109. H. Levine, L. Slade, Carbohyd. Polym. 6 (1986) 213. L. Slade, H. Levine, Pure Appl. Chem. 60 (1988) 1841. M.S. Rahman, Trends Food Sci. Technol. 17 (2006) 129. SAS, SAS Users’ Guide: Statistics, SAS Institute, NC, 2001. M.S. Rahman, Int. J. Food Prop. 7 (3) (2004) 407. M.S. Rahman, S. Kasapis, N.S.Z. Al-Kharusi, I.M. Al-Marhubi, A.J. Khan, J. Food Eng. 80 (1) (2007) 1. [8] J. Crank, The Mathematics of Diffusion, Oxford University Press, Oxford, 1975.
377
[9] S.S.H. Rizvi, in: M.A. Rao, S.S.H. Rizvi, A.K. Datta (Eds.), Engineering Properties of Foods, Third edn., CRC Press, Boca Raton, FL, 2005, p. 239. [10] M.S. Rahman, R.H. Al-Belushi, Int. J. Food Prop. 9 (3) (2006) 421. [11] M.S. Rahman, Food Properties Handbook, CRC Press, Boca Raton, FL, 1995. [12] S. Kasapis, I.M. Al-Marhoobi, J.R. Mitchell, Carbohyd. Res. 338 (2003) 787. [13] L. Di Gioia, B. Cuq, S. Guilbert, Int. J. Biol. Macromol. 24 (1999) 341. [14] M. Pouplin, A. Redl, N. Gontard, J. Agric. Food Chem. 47 (1999) 538. [15] M.T. Kalichevsky, E.M. Jaroszkiewicz, S. Ablett, J.M.V. Blanshard, P.J. Lillford, Carbohyd. Polym. 18 (1992) 77. [16] T.J. Maurice, Y.J. Asher, S. Thomson, in: H. Levine, L. Slade (Eds.), Water Relationships in Food, Plenum Press, New York, 1991, p. 215. [17] A.M. Cocero, J.L. Kokini, J. Rheol. 35 (2) (1991) 257. [18] L.N. Bell, D.E. Touma, J. Food Sci. 61 (1996) 807, 828. [19] C.H. Tang, H. Wu, H.P. Yu, L. Lin, Z. Chen, X.Q. Yang, J. Food Biochem. 29 (2005) 402. [20] K. Kawai, T. Suzuki, M. Oguni, Thermochim. Acta 431 (2005) 4. [21] E.M. De Graaf, H. Madeka, A.M. Cocero, J.L. Kokini, Biotechnol. Progr. 9 (1993) 210. [22] T. Hashimoto, T. Hagiwara, T. Suzuki, R. Takai, Fisheries Sci. 69 (2003) 1290. [23] G. Zografi, S.R. Byrn, in: Y.H. Roos, R.B. Leslie, P.J. Lillford (Eds.), Water Management in the Design and Distribution of Quality Foods, Technomic Publishing, Lancaster, 1999, p. 397. [24] D. Champion, M. Le Meste, D. Simatos, Trends Food Sci. Technol. 11 (2000) 41. [25] I. Tan, C.C. Wee, P.A. Sopade, P.J. Halley, Carbohyd. Polym. 58 (2004) 191. [26] D. Champion, H. Hervet, G. Blond, M. Le Meste, D. Simatos, J. Phys. Chem. B. 10 (1997) 10674. [27] Y.M. Gunning, R. Parker, S.G. Ring, Carbohyd. Res. 329 (2000) 377.