Thermal degradation kinetics study of curcumin with nonlinear methods

Food Chemistry 155 (2014) 81–86

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Thermal degradation kinetics study of curcumin with nonlinear methods Zhipeng Chen a, Yao Xia a, Sen Liao a,⇑, Yingheng Huang a,b, Yu Li a, Yu He a, Zhangfa Tong a, Bin Li a a Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology, School of Chemistry and Chemical Engineering, Guangxi University, Nanning, Guangxi 530004, China b School of Materials Science and Engineering, Guangxi University, Nanning, Guangxi 530004, China

a r t i c l e

i n f o

Article history: Received 9 August 2013 Received in revised form 10 December 2013 Accepted 15 January 2014 Available online 23 January 2014 Keywords: Curcumin Thermogravimetric analysis Kinetic analysis Advanced isoconversional method Activation energy

a b s t r a c t The results of TG/DTG when curcumin was used as the food colouring agent indicated that the processing temperature of the food should not exceed 190 °C. The decomposition process of curcumin involved two stages. The results of Ea values, determined by an advanced isoconversional method, showed that the two stages were both single-step processes. The most probable mechanisms of the two stages were estimated by using comparative and nonlinear model-fitting methods. The mechanisms obtained from the two methods are the same, which are the assumed random nucleation and its subsequent growth for stage I and one-dimensional diffusion for stage II, respectively. The values of pre-exponential factor A for both stages were obtained on the basis of Ea and g(a). Besides, some thermodynamic functions (DS–, DH– and DG–) of the transition state complexes for the two stages were also calculated. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Curcumin is commonly used as a spice, food preservative, flavoring and colouring agent. It has also been extensively studied in animal and clinical trials. Curcumin is widely applied in many different types of food products, including bread, instant rice, and noodles, to provide its health beneficial effects (Bhawana, Basniwal, Buttar, Jain, & Jain, 2011; Choi, Kim, Park, & Hong, 2012). The consumption of curcumin and its related food products has markedly increased. Many physiological effects of curcumin have been reported. For example, curcumin possesses potent antioxidant (Pizzo, Scapin, Vitadello, Florean, & Gorza, 2010; Sugiyama, Kawakishi, & Osawa, 1996), antiinflammatory (Aggarwal & Harikumar, 2009), antitumor (Lee et al., 2009), antiHIV (Jordan & Drew, 1996), and antimicrobial properties (De et al., 2009). It also has the ability to inhibit lipid peroxidation and scavenge superoxide anion, singlet oxygen, nitric oxide and hydroxyl radicals (Jovanovic, Boone, Steenken, Trinoga, & Kaskey, 2001). However, to the best of our knowledge, there are no reports on the non-isothermal kinetics of the decomposition of curcumin. As a colouring agent, curcumin may be used in baked and fried foods. So, it is important and interesting to study the thermal stability of curcumin. Kinetic analysis, which is a modern technique and widely used to study thermal decomposition, has received considerable ⇑ Corresponding author. Tel./fax: +86 771 3233718. E-mail addresses: [email protected], [email protected] (S. Liao). http://dx.doi.org/10.1016/j.foodchem.2014.01.034 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved.

attention. For example, recently, it has been applied to study different thermal processes (Mercali, Jaeschke, Tessaro, & Marczak, 2013; Niamnuy, Nachaisin, Poomsa-ad, & Devahastin, 2012; Rawson, Brunton, & Tuohy, 2012; Zhao & Wang, 2012). Kinetic analysis can have either a practical or theoretical application. A major practical application is the prediction of process rates, thermal stability and material lifetimes. Hence, different methods (Domínguez, Grivel, & Madsen, 2012; Joraid et al., 2012; Vyazovkin et al., 2011; Wan, Li, Fan, Bu, & Li, 2012) have been employed to study the kinetics of thermal decomposition. Kinetic analysis is both essential and useful for the preparation and application of various substances. As part of our systematic studies in kinetic analysis (Chen, Chai, Liao, Chen, et al., 2012; Chen, Chai, Liao, He, et al., 2012; He et al., 2013), we report here, the thermal decomposition processes of curcumin, investigated by a non-isothermal thermogravimetric analysis (TGA) technique. Kinetic data were collected using a simultaneous TG/DTG technique. Non-isothermal kinetics of the decomposition process were analyzed by nonlinear methods (Domínguez et al., 2012; Joraid et al., 2012; Vyazovkin et al., 2011; Wan et al., 2012). The values of Ea were obtained by an advanced isoconversional procedure. For the single-step kinetic process, the most probable mechanism function g(a) of the thermal decomposition reaction was deduced by a comparison method and a nonlinear model-fitting method. Furthermore, the nonlinear model-fitting method was developed to get accurate n values of the most probable reaction mechanism function as a precisely determined result.

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Z. Chen et al. / Food Chemistry 155 (2014) 81–86

2. Materials and methods

ln b ¼ ln

0:0048AEa Ea  1:0516 ; gðaÞR RT

ð7Þ

2.1. Materials Curcumin (analytical grade) was purchased from the Sinopharm Chemical Reagent Co. Ltd., China. 2.2. Experimental methods TG/DTG measurements were made with a NETZSCH STA 409 PC/PG thermogravimetric analyzer under an air atmosphere, with a flow rate of 20 ml min1. 6 ± 0.1 mg of powder samples of curcumin were used in the experiments with different heating rates of 5, 8, 11, 15 °C min1, and up to 800 °C. The samples were loaded, without pressing, into a platinum crucible. The results of kinetic and thermodynamic analyses were obtained by using the programmes compiled by ourselves with BASIC. 2.3. The mathematical methods According to non-isothermal kinetic theory, the kinetic equation of thermal decomposition of solid-state material is (Vyazovkin et al., 2011):

  da A Ea f ðaÞ; ¼ exp  dT b RT

ð1Þ

where a is the degree of conversion, b is the heating rate (°C min1), Ea is the apparent activation energy, A is the pre-exponential factor, R is the gas constant (R = 8.314 J mol1 K1). The Vyazovkin method (Domínguez et al., 2012; Joraid et al., 2012; Vyazovkin et al., 2011; Wan et al., 2012), which is an advanced and rigorous nonlinear procedure, can be used to calculate the approximate value of Ea approaching the exact value. This procedure is carried out at the expense of relatively complicated computations, so this method is used rather rarely in actual kinetic analyses. Fortunately, the above complicated computations can be resolved with a personal computer. For a series of runs performed at different heating rates, the Ea value can be determined by minimizing the following Eq. (2):

XðEa Þ ¼

n X n X IðEa ; T a;i Þbj i

j–i

IðEa ; T a;j Þbi

;

ð2Þ

where

IðEa ; T a Þ ¼

Z 0

Ta



 Ea dT: exp RT

ð3Þ

The temperature integral of Eq. (1) is Eq. (3). The temperature integral (Eq. (3)) can be evaluated with an approximation form (Eq. (4)) by a series of transforms (White, Catallo, & Legendre, 2011):

I¼

Ea pðxÞ; R

pðxÞ ¼

  expðxÞ x4 þ 18x3 þ 88x2 þ 96x ; x2 x4 þ 20x3 þ 120x2 þ 240x þ 120

ð4Þ

ð5Þ

where x = Ea/(RT). Besides, the Vyazovkin procedure requires an initial interval of Ea to bracket the minimum. The KAS, OFW and Starink methods can provide a starting point to establish this interval. KAS, OFW and Starink equations are shown in Eqs. (6)–(8) (Vyazovkin et al., 2011):

ln

b T

2

¼ ln

AR Ea  ; gðaÞEa RT

ð6Þ

ln

b T 1:92

¼ C 2  1:0008

Ea : RT

ð8Þ

The most probable mechanism function g(a) of the thermal decomposition reaction is deduced by the comparison between experimental plots and modelled results. The compensation effect can be observed when a model-fitting method, such as the Tang equation (Eq. (9)) (Tang, Liu, Zhang, & Wang, 2003), is applied to a single heating rate run:

" ln

#

g i ðaÞ T 1:894661 a

    Ai Ei ¼ ln þ 3:63504095  1:894661 ln Ei bR  1:00145033

Ei : RT a

ð9Þ

The different pairs of the Arrhenius parameters, ln Ai and Ei, which are yielded by substitution of different models gi(a) (Jiang, Wang, Wu, Wang, & Wang, 2010) into the Tang equation and fitting it to experimental data, all demonstrate a strong correlation known as a compensation effect (Vyazovkin et al., 2011):

ln Ai ¼ aEi þ b:

ð10Þ

The parameters of compensation effect a and b can be calculated from the Eq. (10) with the Ei and ln Ai. Then, the Eq. (11) is used to estimate ln A0:

ln A0 ¼ aE0 þ b;

ð11Þ

where a and b are obtained from Eq. (10), E0 is the average value of the apparent activation energy Ea calculated from Eq. (2). For constant heating rate conditions, integration of Eq. (1) leads to Eq. (12):

gðaÞ 

Z

a

0

da A ¼ f ðaÞ b

Z 0

T

exp

  E dT: RT

ð12Þ

The temperature integral in Eq. (12) can be replaced with various approximations, p(x) as follows (Jankovic, Marinovic-Cincovic, Jovanovic, Samarzija-Jovanovic, & Markovic, 2012):

gðaÞ ¼

AE pðxÞ; bR

ð13Þ

where A = A0, E = E0 and x = E/(RT). Therefore, the experimental plot of g(a) vs. a is obtained by using Eq. (13). The analytical form of the reaction model (i.e., equation) can then be established by comparison between the experimental plot and the theoretical plots obtained from the g(a) equations representing the reaction models (Jiang et al., 2010), and the best matching theoretical plot can be found. The most probable mechanism function, g(a), of the thermal decomposition reaction can also be deduced by a nonlinear model-fitting method. Fitting of either single or multi-step models is commonly accomplished by means of nonlinear regression, which works by minimizing the difference between the measured and calculated data. The method of least squares evaluates the difference in the form of the residual sum of squares (RSS) (Vyazovkin et al., 2011):

RSS ¼

X

½ðYÞexp  ðYÞcalc 2 ¼ min :

ð14Þ

For the single step model, (Y)exp = g(a)exp which is obtained from Eq. (13), and (Y)calc = g(a)calc which is calculated from the g(a) equations representing the reaction models. The pre-exponential factor A can be estimated from the following equation (Vyazovkin et al., 2011):

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Z. Chen et al. / Food Chemistry 155 (2014) 81–86

A¼

bE0 RT 2max f 0 ðamax Þ

exp



E0 RT max

 ;

curcumin at 194 °C in the DSC curve. Souguir, Salaün, Douillet, Vroman, and Chatterjee (2013) also reported that there was a 1.344% weight loss at 190.88 °C in the TG curve. So, the result of Fig. 1 is in agreement with those results reported by Sun et al. (2013) and Souguir et al. (2013). Since 190 °C is higher than the firing temperature (180 °C) (Ghafoorunissa, 2007) and the decomposure peak temperature (292 °C) is also higher than that of cassava starch (248 °C)(Jankovíc, 2013), it can be seen that curcumin is safe, when is used as a colouring agent for baked and fried foods.

ð15Þ

where g(a)f0 (a) = 1. In Eq. (15), the subscript max denotes the values related to the maximum of the differential kinetic curve obtained at a given heating rate. Some thermodynamic functions of the transition state complex (DS–, DH– and DG–) of the thermal decomposition reaction may be calculated according to the following Eqs. (Bamford & Tipper, 1980; Šesták, 1984):

DS– ¼ R ln



Ah evkB T P



3.2. Results of thermal decomposition kinetics and thermodynamics

ð16Þ

DH– ¼ E–  RT P

ð17Þ

DG– ¼ DH–  T p DS–

ð18Þ

3.2.1. Calculation of activation energy Ea According to Eq. (2), the equation of the advanced isoconversional procedure was used. The Ea values of thermal decomposition of curcumin, corresponding to different degrees of conversion, a, are shown in Fig. 2 and Tables 1S and 2S (supporting information). As shown in Fig. 2, Tables 1S and 2S, compared with the relative errors of the values of Ea obtained by the Vyazovkin method using Ea of KAS, OFW and Starink as initial interval sof Ea, respectively, the results obtained from the three initial intervals of Ea are very close to each other. Besides, from Table 2S, the analysis of relative errors of the values of Ea from the three comparative initial intervals of Ea shows that maximal relative errors for the two stages are only 0.09% and 0.133%, respectively. So, it is obvious that the values of Ea obtained by the Vyazovkin method, using Ea of KAS, OFW and Starink, as initial interval of Ea, are all reliable. If the Ea values are independent of a (the relative error of Ea 6 10%), the decomposition process is dominated by a single reaction step (Boonchom, 2008); otherwise, it should be interpreted in terms of a multi-step mechanism (Vlaev, Nikolova, & Gospodinov, 2004). From Fig. 2 and Table 1S, it can be seen that the Ea values are roughly constant in stage I (a = 0.12  0.90, the relative error is 9.8%) and stage II (a = 0.10  0.90, the relative error is 1.4%). So, this indicates that the thermal decomposition of the two stages can be considered as a single-step reaction mechanism and described by a unique kinetic triplet [Ea, g(a) and A]. The average Ea values of the two stages, calculated by the Vyazovkin method, are 153.8 ± 15.2 and 152.6 ± 2.1 kJ mol1, respectively.

where A is the pre-exponential factor, e = 2.7183 is the Neper number; v is the transmission factor, which is unity for monomolecular reactions; kB is the Boltzmann constant (1.381  1023 J K1); h is the Planck constant (6.626  1034 J s); TP is the peak temperature in the DTG curve; R is the gas constant (8.314 J mol1 K1) and E– is the activation energy, Ea, which can be obtained from the Eq. (2). 3. Results and discussion 3.1. TG/DTG analysis Fig. 1 shows the TG/DTG curves of curcumin at four different heating rates. As can be seen from Fig. 1, the mass loss starts at about 94 °C, ends at about 533 °C (b = 11 °C min1). The observed mass loss in the TG curve is 100%, which indicates that curcumin has been completely decomposed. The thermal decomposition of curcumin below 800 °C occurs in two stages. There is an inflection point (broad upward peak) at about 357 °C in the DTG curve (b = 11 °C min1), which can be regarded as the end-point of stage I. Therefore, the stage I, whose mass loss is 33.77% starts at about 94 °C and ends at about 357 °C, which is characterized by a DTG peak at about 292 °C. The stage II, whose mass loss is 66.23% begins at about 357 °C, and ends at about 533 °C, which involves a DTG peak at about 486 °C. The TG/DTG analyses suggest that: (I) the stage I is due to the decomposition of substituent groups of curcumin; (II) the stage II is due to the decomposition of two benzene rings of curcumin. From Fig. 1, it can be seen that the curcumin is clearly decomposed at 190 °C (weight loss = 1.5%), and there is a decomposed peak at 292 °C. Sun, Williams, Hou, and Zhu (2013) reported that there was a thermal decomposition peak of

I:33.77 %

100

3.2.2. Determination of the most probable mechanism function by the comparative method The determination of the reaction models can be accomplished (Vyazovkin et al., 2011) by using the compensation effect process. Parameters of the compensation effect are shown in Table 1. Eq. (11) is used to generate experimental plots of g(a) vs. a for the stages I and II. The resulting experimental plots (b = 11 °C min1)

II:66.23%

0.0

(a)

(b)

-0.2

60

o

DTG/ mg min

TG / %

-1

80 -1

5 C min o -1 8 C min o -1 11 C min o -1 15 C min

40 20

-0.4

o

-1

o

-1

o

-1

o

-1

5 C min 8 C min

-0.6

11 C min

-0.8

15 C min

-1.0

0 -1.2

100

200

300

400 o

Temperature / C

500

600

100

200

300

400 o

Temperature / C

Fig. 1. TG/DTG curves of curcumin at different heating rates: (a) TG; (b) DTG.

500

600

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Z. Chen et al. / Food Chemistry 155 (2014) 81–86

180

-1

170

Eα / kJ mol

mechanism functions for the two stages need to be further corrected. The theoretical curves for the two stages with different values of n are illustrated in Fig. 4. From the results of further comparisons, the most probable reaction mechanism functions are determined precisely, and the accurate values of n for the two stages are n = 2.16 for stage I and n = 1.92 for stage II. Therefore, for the thermal decomposition of curcumin, the exact integral forms of reaction mechanism functions are as follows: (i) g(a) = [ln(1  a)]2.16 (No. 20) for stage I, (ii) g(a) = a1.92 (No. 27) for stage II, which belong to (i) the mechanism of the assumed random nucleation and its subsequent growth for stage I, (ii) the mechanism of one-dimensional diffusion for stage II. Both mechanisms are physical processes. It is interesting that the melting point of curcumin is about 183 °C, however, the DTG peaks of the two stages are all above the melting point. This suggests that curcumin is in a molten state for the two stages. So, it is reasonable that the results of mechanisms show that the processes of the two stages are controlled by physical processes, though the decomposition processes of the two stages include chemical reactions.

I-VK I-VO I-VS II-VK II-VO II-VS

160 150 140 130 120 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

α Fig. 2. Dependence of Ea on a for the thermal decomposition of curcumin.

Table 1 Parameters of the compensation effect for the thermal decomposition of curcumin. b/°C min1

a

b

ln A0

Stage I 5 8 11 15

0.2325 0.2299 0.2277 0.2257

4.1333 3.7084 3.4014 3.0815

31.6268 31.6456 31.6153 31.6300

Stage II 5 8 11 15

0.1740 0.1709 0.1688 0.1674

3.6468 3.1990 2.9062 2.6238

22.9085 22.8838 22.8565 22.9281

do not demonstrate any significant variation with the other three heating rates. As shown in Fig. 3, the analytical formations of the reaction models (i.e., equation) for stages I and II are established by comparing the above experimental plots with the theoretical plots obtained from 36 g(a) equations (Jiang et al., 2010) representing the reaction models. From the results of the comparison (Fig. 3), the most probable reaction mechanism functions for both stages are determined roughly, and the best matching mechanism functions are No. 20 and No. 27, respectively. The integral forms of reaction mechanism functions of the two stages are: (i) g(a) = [ln(1  a)]2 (No. 20) for stage I; (ii) g(a) = a2 (No. 27) for stage II, which can be reformatted as: (i) g(a) = [ln(1  a)]n (No. 20); (ii) g(a) = an (No. 27). From Fig. 3, the experimental and theoretical curves for the two stages do not match very well. So, n values of the reaction

3.2.3. Determination of the most probable mechanism functions by the nonlinear model-fitting method For the nonlinear model-fitting method, Eq. (12) is used to find the most probable reaction mechanism. Eq. (11) is used to generate experimental plots, and the theoretical plots are obtained from 36 g(a) equations (Jiang et al., 2010). The results shown in Table 2 are as follows: (i) the most probable reaction mechanism functions for the two stages are determined roughly, and the best matching mechanism functions are Nos. 20 and 27, respectively. (ii) accurate n values of the most probable reaction mechanism functions for the two stages are as follows: n = 2.16 (g(a) = [ln(1  a)]2.16, No. 20) for stage I, and n = 1.92 (g(a) = a1.92, No. 27) for stage II, which are in agreement with the results of the comparative method, but the nonlinear model-fitting method is quicker and more effective than the comparative method. 3.2.4. Determination of the pre-exponential factor A The pre-exponential factor A, for stages I and II of the thermal decomposition of curcumin, were estimated with Eq. (13). The results show that the average values of the pre-exponential factor A for stages I and II are 8.00  1013 min1 and 9.02  109 min1, respectively. The pre-exponential factor (A) values in the Arrhenius equation for solid state reactions are expected to be over a wide range (6 or 7 orders of magnitude), even after the effect of surface area is taken into account (Ioitßescu, Vlase, Vlase, & Doca, 2007; Turmanova, Genieva, Dimitrova, & Vlaev, 2008). A low pre-exponential factor will often indicate a surface reaction, but if the reaction is not 1.0

14 12 10

No. 10 No. 27 No. 1 No. 25 No. 29

0.8 0.6

(b)

g(α)

8

g (α)

(a)

No.21 No. 4 No. 20 No. 15 No. 3

6

0.4

4 0.2 2 0.0

0 0.0

0.2

0.4

0.6

α

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

α

Fig. 3. Rough comparison between the modelled results (different symbols) and the experimental results for b = 11 °C min1 (solid line): (a) for stage I; (b) for stage II.

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Z. Chen et al. / Food Chemistry 155 (2014) 81–86

1.0

9 8

7 5

7 g(α )

g(α)

6

6

4

3

3

2

0.8

0.7

0.6

5

4

0.9 0.8

g(α )

8

(a)

n=2.6 n=2.4 n=2.3 n=2.16 n=1.9

g(α )

9

0.6

n=1.6 n=1.8 n=1.92 n=2.1 n=2.3

(b)

0.5 0.4 0.3 0.2

0.4

0.5

0.6

0.7 α

0.8

0.9

2 0.78

0.80

0.82

1

0.84 α

0.86

0.88

0.2

0.90

0

0.0

-1 0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

α

α

Fig. 4. Precise comparison between the modelled results (different symbols) and the experimental results for b = 11 °C min1 (solid line): (a) for stage I; (b) for stage II.

Table 2 Parameters of the nonlinear model-fitting method for the thermal decomposition of curcumin. Rough results Stage I Function No. RSS

20 2.2503

15 39.5119

4 40.9935

3 46.8013

16 54.7406

17 62.5863

23 66.1345

18 70.8464

19 75.2507

7 77.7310

Stage II Function No. RSS

27 0.0183

25 0.0921

10 0.2128

1 0.2363

29 0.5138

26 0.6138

36 1.0522

2 1.2781

24 1.4411

32 1.5126

Precise results Stage I Different N of function No. 20 RSS

2.16 0.8944

2.14 0.9138

2.18 0.9238

2.20 1.0040

2.10 1.0907

2.22 1.1375

2.00 2.2504

1.90 4.2660

1.80 6.9570

1.70 10.1735

Stage II Different N of function No. 27 RSS

1.92 0.0137

1.94 0.0139

1.90 0.0141

2.00 0.0183

1.80 0.0274

2.10 0.0370

2.16 0.0541

2.18 0.0607

1.70 0.0618

2.20 0.0677

dependent on the surface area, the low factor may demonstrate a ‘‘tight’’ complex. A high factor will usually indicate a ‘‘loose’’ complex (Ioitßescu et al., 2007). Even higher factors (after the correction for surface area) can be obtained for complexes having free translation on the surface. Based on these reasons, the values of preexponential factor A of the two stages reveal that the thermal decomposition processes of stage I experienced a ‘‘loose’’ complex while stage II is a process of ‘‘tight’’ complex. 3.2.5. Determination of some thermodynamic functions of the transition state complex of the thermal decomposition reaction Eqs. (14)–(16) were used to calculate some thermodynamic functions of the transition state complex (DS–, DH– and DG–) for stages I and II. The average values of the functions are as follows: (i) DS– = 26.4 J mol1 K1, DH– = 149.1 kJ mol1 and – 1 DG = 163.9 kJ mol for stage I, (ii) DS– = 104.4 J mol1 K1, DH– = 146.3 kJ mol1 and DG– = 225.0 kJ mol1 for stage II. The values of DS– for the two stages are all negative, indicating that the corresponding activated complex has a higher degree of arrangement (lower entropy) than that in the initial state. The positive values of DG– of the two stages illustrate that the processes of decomposition are not spontaneous. 4. Conclusions The analyses of TG/DTG indicate that when curcumin is used as a food colouring agent, the processing temperature of the corresponding food should not exceed 190 °C. The kinetics of thermal decomposition, under oxygen atmosphere, of curcumin have been examined by using advanced isoconversional and nonlinear model-fitting

methods. The values of activation energies indicate that the thermal decomposition of curcumin experienced two stages. Stages I and II correspond to the decomposition of the substituent groups and the two benzene rings of curcumin, respectively. Both stages I and II are single-step kinetic processes which can be adequately described by a unique kinetic triplet [Ea, A, g(a)]. The comparative method and the nonlinear model-fitting method were used to determine the reaction mechanisms for stages I and II, and the results of both methods were the same. The most probable mechanisms are: (i) the assumed random nucleation and its subsequent growth for stage I and (ii) one-dimensional diffusion for stage II. Acknowledgements This study was financially supported by the Natural Scientific Foundation of China (Grant No. 21161002), the Dean Project of Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification (Grant Nos. 2012K03 and 2012K07), the Technology the Key laboratory of new processing technology for nonferrous metals and materials, Ministry of Education, Guangxi University (No. GXKFZ-02); the Guangxi Scientific Foundation of China (Grant No. 2012GXNSFAA053019 and No. 0991108); and the Guangxi Science and Technology Agency Research Item of China (Grant No. 0895002-9). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foodchem. 2014.01.034.

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