Kinetic Models of Evaporation and Total Phenolics ...

doi 10.1515/ijfe-2014-0016

International Journal of Food Engineering 2014; 10(3): 383–392

Athanasia M. Goula*, A. Tzika and K.G. Adamopoulos

Kinetic Models of Evaporation and Total Phenolics Degradation during Pomegranate Juice Concentration Abstract: Pomegranate juice was concentrated by conventional heating at different temperatures (45, 50, 55, 60, and 65°C), and the effect of temperature on evaporation rate and on thermal degradation of total phenolics was investigated. Concentration kinetics modeling was developed based on thin-layer drying models. The logarithmic model was found to give better predictions than the others. The temperature dependence of the model constants was expressed by polynomial relationships. In addition, a first-order decay model, with an Arrhenius and a polynomial dependency on temperature and water content, respectively, was used to describe the joint influence of temperature and moisture content on the thermal degradation of total phenolics in a concentration process of pomegranate juice. Keywords: concentration, evaporation rate, kinetics, phenolics, pomegranate

*Corresponding author: Athanasia M. Goula, Laboratory of Food Engineering and Processing, Department of Food Science and Technology, Faculty of Agriculture, Aristotle University of Thessaloniki, University Campus of Thessaloniki, 54124 Thessaloniki, Greece, E-mail: [email protected]; [email protected] A. Tzika, Department of Food Science and Technology, Faculty of Agriculture, Aristotle University, 54124 Thessaloniki, Greece, E-mail: [email protected] K.G. Adamopoulos, Department of Chemical Engineering, School of Engineering, Aristotle University, 54124 Thessaloniki, Greece, E-mail: [email protected]

1 Introduction Pomegranate (Punica granatum L.) is one of the oldest known edible fruit that contains the highest concentration of total polyphenols in comparison with other fruits studied [1]. They are fruits rich in aril, the percentage of which ranges from 50 to 70% of total fruit and comprises 78% juice and 22% seeds [2]. The production and consumption of pomegranate have greatly increased throughout the world in recent years due to the health-promoting

potential of different components of pomegranates. The high demand for pomegranate products has caused substantial increase in pomegranate juice production. This has revealed the importance of pomegranate juice yield, not only in terms of economics but also in terms of the organoleptic and physicochemical properties of pomegranate juices [3]. In addition, concentrated pomegranate juices can be used as fruit ingredients in many foods such as dairy products, jams and jellies, syrups, confectionery, and so on. In general, the concentration of fruit juices includes a series of advantages such as weight and volume reduction with consequent reduction of packaging, transport, handling, and storage costs; water activity reduction with enhancement of the product stability; and a better product preparation for a final drying treatment [4]. Commercial processes in current use usually involve multistage vacuum evaporation of water at high temperature followed by recovery and concentration of volatile flavors and their addition back to the concentrated product [5]. However, it is known that concentration of fruit juices by evaporation determines a loss of most volatile aroma compounds with a consequent remarkable qualitative decline [6]. Besides, the heat required to perform the evaporation results in some “cooked” notes recognized as off-flavors. Commercial freeze concentration systems permit to preserve the volatiles during the water removal process, but they require a remarkable energy consumption. Another limitation of the process is that the achievable concentration (about 50°Brix) is lower than the values obtained by evaporation (60–65°Brix) [7]. Several researchers have described the advantages of mathematical models to develop a better understanding of controlling the parameters of a dehydration/concentration process [8]. The mathematical models may help to gain a better understanding of transport phenomena associated with processing and can be used to control or optimize the variables of the process [9]. The concentration process can be described by theoretical, semitheoretical, or empirical equations. The theoretical models are based on the heat transfer coefficient, which is mostly affected by the internal film coefficient that is

Brought to you by | Aristotle University of Thessaloniki Authenticated | [email protected] author's copy Download Date | 10/30/14 8:57 AM

384

A. M. Goula et al.: Concentration Kinetics of Pomegranate Juice

normally described in terms of physical properties and flow of the fluid. Usually it is put in terms of Prandtl and Reynolds numbers. The temperature driving force depends on the external temperature, internal pressure, which can be determined from the pressure, and the boiling point elevation. However, many researchers described a food drying/concentration process using semitheoretical and empirical models such as Lewis model, Henderson and Pabis model, Page model, modified Page model, twoterm equation, two-term exponential equation, logarithmic model, and Wang and Singh model [10–12]. However, the scientific literature lacks information on the concentration of pomegranate juice. Over the past three decades, there has been an increased concern for food quality with a significant amount of work accomplished in the area of kinetics of nutrient destruction or general quality degradation during concentration processes [13]. Generally, the problem of chemical conversions during concentration is extremely complicated. The rate constants of the reactions depend on temperature, concentration of the reactants, and concentration of water (water activity) [14]. The question arises in which cases the temperature dependence may be described by the simple Arrhenius equation. During the concentration process, concentrations change and also it is not clear to what extent thereby the chemical changes are influenced. The influence of the water activity is important but insufficiently understood. Water acts as solvent for the chemicals of nutritional importance present in the product. As water is removed, the concentration of the chemicals increases. The loss of nutrient is concentration dependent and would increase as dehydration progresses. On the other hand, some of the water-soluble compounds may act as catalysts to the decomposition process. These catalytic effects are greatly reduced as the moisture is removed. Although various oxidation reactions show a minimum rate of reaction at a certain water activity [15], in general, chemical reactions are slower as the water activity decreases [16]. Phenolics compounds in fruits are important contributors to the color, flavor, and aging characteristics of fruit products. According to Alper et al. [17], the antioxidant, antiproliferative, and antiatherogenic activity of pomegranate juice have been reported, and these properties have been attributed to its high content of phenolic compounds that were identified as punicalagin – an ellagitannin in which gallagic and ellagic acids are linked to a glucose molecule – ellagic acid derivatives, and anthocyanins (delphinidin, cyaniding, and pelargonidin 3-glucosides and 3,5-diglucosides) [18–20]. The phenolic constituents of pomegranates give the characteristic

flavor and also play a large role in the acquisition of sensory properties (color, bitterness, astringency, etc.) of the juice [21]. It is proposed that these polyphenols also contribute to haze formation during concentration and storage through prior polymerization or condensation leading to the formation of polymeric complexes [17]. Polyphenol contents as well as polyphenol profiles of pomegranate juices are the primary parameters affected by the juice yields. Depending on the increase in juice yield, the amounts of polyphenols, especially the tannins that pass into juice from the rind, also increase [3]. As a result, astringency, color stability, and turbidity of pomegranate juice are also increased because of interactions between the tannins and the proteins in saliva and between anthocyanins and proteins in the juice [22]. However, little published information exists on the phenolic involvement in product quality changes during pomegranate juice concentration [23]. Thus, the objectives of this work were to model the concentration process of pomegranate juice and to study the kinetics of total phenolics degradation during the concentration process.

2 Materials and methods 2.1 Sample preparation Fresh, good quality pomegranates (Wonderful variety) procured from the local market were used. Before juice extraction, pomegranates were washed in cold tap water and drained. The top and bottom of pomegranate rinds were removed with a sharp stainless steel knife to prevent microbial contamination. The arils were separated from the husks and pericarp by hand and then squeezed in a juicer (HR 1861, Philips, Amsterdam, Netherlands). Pomegranates were cut into four pieces and processed into juice. The juice was stored in a refrigerator below 5°C overnight before subsequent concentration.

2.2 Concentration of pomegranate juice The concentration of pomegranate juice with an initial total soluble solids content of 14–15°Brix to a final concentration of ~60°Brix was accomplished in a rotary vacuum evaporator (Model R 114, Buchi LaboratoriumsTechnik, Flawil, Switzerland) at 45, 50, 55, 60, and 65°C. Temperature and pressure during concentration were controlled using a PLC controller. Weight loss was calculated every 10 or 15 min by the value measured for



pomegranate juice weight, whereas juice temperature was continuously measured. Concentrated juice samples were drawn every 10 or 15 min and analyzed for total phenolics. At the end of concentration process, total solids of the concentrated juice were measured to validate the calculated weight losses. Pomegranate juice concentration and thermal degradation kinetics were replicated twice at the five product temperatures.

2.3 Total phenolics measurement The total phenolics content was determined using the Folin–Ciocalteu reagent [24]. The samples were diluted (1/10) with acetone/water (80:20). Aliquots of 0.4 mL of diluted samples were introduced in test tubes followed by 0.5 mL of Folin–Ciocalteu reagent and 2.5 mL of distilled water. The tubes were mixed using a vortex mixer and allowed to react for 3 min; then 1 mL of sodium carbonate (25%) and 5.6 mL of distilled water were added to it. The mixture was incubated for 30 min at room temperature before absorbance was measured at 760 nm. Reagent blanks were prepared by replacing the sample volume by distilled water. The total phenols content was performed by duplicate in each sample, and results were expressed as gallic acid equivalents (mg GAE/100 mL juice).

2.4.2 Kinetic model of total phenolics degradation The kinetic models representative of total phenolics degradation during concentration were obtained using a dynamic test approach. According to Frias and Oliveira [28], the dynamic method for determining decay kinetics requires the acquisition of moisture, temperature, and quality factor concentration during the actual process. The degradation achieved is the common and indistinguishable effect of both temperature and moisture integrated in time. This experimental design allows for the determination of the kinetic parameters using a more reduced sample size, on conditions similar to real concentration, compared to the alternative of using isothermal or constant water content designs performed at several combinations of the variables [28, 29]. An empirical first-order kinetic model was used for degradation kinetics of total phenolics [13, 30]:

dC ¼kC dt

k ¼ kT k X Ea kT ¼ k0 exp RT

2.4 Modeling 2.4.1 Kinetic models of concentration

Table 1

ð9Þ

where C is the concentration of total phenolics (mg/g dry solids) at time t (min) and k is the reaction rate constant (min−1), which is a function of temperature and moisture content [15, 31]:

ð10Þ

kX ¼ A1 þ A2 X þ A3 X 2

Eight simplified drying models given in Table 1 were used to describe the concentration kinetics of pomegranate juice [10, 12, 25–27].

Model Lewis

Page Modified Page Two-term Two-term exponential Logarithmic Wang and Singh

ð11Þ ð12Þ

where k0 is the so-called frequency factor (min−1), Εa is the activation energy (kJ/mol), X is the juice moisture content (g/g dry solids), and A1, A2, and A3 are constants.

Mathematical models applied to concentration of pomegranate juice

Henderson and Pabis

385

Equation

References

B B0 ¼ expðKtÞ

ð1Þ

Doymaz [25]

B B0 ¼ a expðKtÞ

ð2Þ

McMinn [26]

B B0 ¼ expðKt Þ

ð3Þ

McMinn [26]

n

B B0 ¼ exp ðKtÞ

ð4Þ

McMinn [26]

B B0 ¼ a expðKtÞ þ b expðnt Þ

ð5Þ

Sharma et al. [27]

ð6Þ

Lee and Kim [12]

n

B B0 ¼ a expðK1 tÞ þ ð1 aÞ expðK2 atÞ B B0 ¼ a expðKtÞ þ b

ð7Þ

Sharma et al. [27]

B B0 ¼ 1 þ at þ bt2

ð8Þ

Sharma et al. [27]

Notes: t: concentration time (min); B: soluble solids content at time t (°Brix); B0: initial solids concentration (°Brix); K, a, n, b, K1, K2: constants.


386


2.4.3 Statistical analysis The parameters of the models were estimated by nonlinear regression using the statistics program Origin (OriginLab Corporation, Microsoft License, Massachusetts, USA). To evaluate the goodness of each approach fit, three criteria were used: the coefficient of determination, R2, which is the relative variance explained by the model with respect to the total variance, the adjusted R-squared, adj R2, which is used to compensate for the addition of variables to the model, and the sum of squared errors, SSE [eq. (13)] that indicates the error of the predictions. X 2 SSE ¼ Xexp Xpred ð13Þ where Xexp is the experimental value and Xpred is the predicted value of total solids concentration change in terms of °Brix and of total phenolics content.

3 Results and discussion 3.1 Concentration Figure 1 illustrates the total soluble solids concentration of pomegranate juice versus time using five temperatures. As it can be observed, the drying rate increased on increasing the temperature. The desired level of concentration was obtained after about 55, 40, 30, 28, and 27 min at 45, 50, 55, 60, and 65°C, respectively. At a higher temperature, the velocity of the individual molecules increases, and the total kinetic energy of the system increases. Therefore, the molecules can overcome their

70

T (ºC ) 45 50 55 60 65

60

B (ºBrix)

50 40 30 20 10 0 0

5

10

15

20

25 30 t (min)

35

40

45

50

55

Figure 1 Concentration curves of pomegranate juice at different temperatures

intermolecular forces and change to a higher energy state. In addition, the temperature of juice increased during concentration. A similar observation was reported by Yousefi et al. [23]. This can be explained by an increase in the soluble solids concentration of juice, which increases the boiling point of liquid. Maskan [32], who produced pomegranate juice concentrate by various heating methods, reported that the time required to obtain the desired final concentration (60.5°Brix) was 23, 108, and 190 min for microwave, rotary vacuum, and atmospheric heating processes, respectively. Yousefi et al. [23], who compared the effects of microwave and conventional heating methods on the evaporation rate and quality attributes of pomegranate juice concentrate, reported that the desired level of concentration (42°Brix) was obtained after 140, 127, and 109 min using a conventional heating at 100, 38.5, and 12 kPa, respectively, whereas in the case of microwave heating, the required times were 118, 95, and 75 min, respectively, for the three operational pressures. The difference in evaporation times can be attributed to the difference in operation pressures. Thin-layer drying models have found wide application due to their ease of use and lack of required data, such as phenomenological and coupling coefficients, as in complex theoretical models. The correlations are mathematically simple with the characteristic parameters, namely drying constants, providing a combined, but sufficiently informative, measure of the transport properties (moisture diffusivity, thermal diffusivity, heat transfer coefficient, and mass transfer coefficient). In addition, their ease of application provides a standardized process description, independent of the controlling mechanism [26]. Many correlations are available in the literature, with those included in this study (Table 1) being selected as they represent some of the more commonly adopted. Nonlinear regression was used to obtain each constant of the selected mathematical models. Moreover, the criteria R2, adj R2, and SSE were calculated to evaluate the fitting of a model to experimental data. The highest values of R2 and adj R2 and lowest values of SSE were chosen for goodness of fit. The results of the statistical computations are summarized in Table 2. Generally, R2, adj R2, and SSE values were varied between 0.835 and 0.999, 0.725 and 0.999, and 0.022 and 455.397, respectively. Figure 2 shows the comparison of mathematical models considered for a concentration temperature of 45°C, and similar results were obtained at all temperatures. It can be seen that concentration curves of all models tended to underor overestimate the experimental data at different stages. For instance, the two-term and the Henderson and Pabis



Table 2

Parameters of the mathematical models for concentration of pomegranate juice

Temperature (°C)

Constant

Statistical parameter 2

adj R2

SSE

0.998 0.972 0.999 0.985 0.835

0.998 0.965 0.999 0.981 0.794

20.918 103.148 0.778 65.559 455.397

R Lewis 45 50 55 60 65

K (min−1) –0.070 –0.096 –0.127 –0.126 –0.128

Henderson and Pabis 45 50 55 60 65

a (–) 1.898 3.732 1.155 2.839 7.767

K (min−1) –0.058 –0.062 –0.122 –0.091 –0.059

0.995 0.996 0.999 0.990 0.940

0.992 0.993 0.998 0.983 0.900

5.315 3.744 0.047 9.765 51.618

K (min−1) –0.151 –0.468 –0.149 –0.389 –1.069

n (–) 0.808 0.566 0.952 0.667 0.375

0.993 0.998 0.999 0.987 0.973

0.988 0.997 0.998 0.978 0.955

8.473 1.910 0.018 12.342 24.175

K (min−1) –0.070 –0.084 –0.130 –0.134 –0.131

n (–) 0.999 1.146 0.975 0.937 0.979

0.998 0.972 0.999 0.985 0.835

0.997 0.953 0.998 0.975 0.725

20.918 103.148 0.778 62.559 455.397

Page 45 50 55 60 65 Modified Page 45 50 55 60 65 Two-term 45 50 55 60 65

K (min−1) –0.075 –0.062 –0.122 –0.093 –0.059

n (min−1) –0.018 –0.062 –0.122 –0.088 –0.059

0.998 0.996 0.999 0.990 0.940

0.995 0.990 0.998 0.975 0.850

1.920 3.744 0.047 9.761 51.618

a (–) –0.873 –2.731 –0.155 –1.839 –6.767

K1 (min−1) 0.415 1.693 0.969 1.132 4.861

K2 (min−1) –0.058 –0.062 –0.122 –0.091 –0.059

0.995 0.996 0.999 0.990 0.940

0.988 0.990 0.998 0.975 0.850

5.327 3.744 0.047 9.765 51.618

a (–) 0.899 6.394 1.228 2.427 977.085

b (–) 3.363 –4.688 –0.309 1.011 –984.543

K (min−1) –0.071 –0.051 –0.120 –0.095 –0.002

0.998 0.998 0.999 0.990 0.993

0.995 0.995 0.998 0.975 0.995

2.253 2.028 0.022 9.654 5.655

a (–) 0.647 1.866 0.578 1.395 3.884

b (–) 2.919 1.866 0.578 1.444 3.884

Two-term exponential 45 50 55 60 65 Logarithmic 45 50 55 60 65

(continued )


387

388


Table 2

(Continued )

Temperature (°C)

Constant

Statistical parameter 2

adj R2

SSE

0.966 0.995 0.978 0.975 0.980

0.943 0.992 0.963 0.958 0.967

35.391 4.558 21.171 23.952 18.299

R Wang and Singh a (min−1) –0.166 0.132 –0.724 –0.075 0.916

45 50 55 60 65

50

60

Experimental Lewis Henderson and Pabis Page Modified Page Two-term Two-term exponential Logarithmic Wang and Singh

40 35 30 25 20

50 40 (B−B0)pred

45

B− B0 (ºBrix)

b (min−2) 0.017 0.023 0.070 0.049 0.020

T (°C) 45 50 55 60 65

30 20

15 10

10 5

0 0

0 10

15

20

25

30 35 t (min)

40

45

50

10

55

Figure 2 Experimental and predicted by different models values of total solids concentration change at 45°C

model were tending to a good fit for low temperatures, while the models were tending to underestimate for higher temperatures. The Lewis model is a special case of the Henderson and Pabis model and tended to underestimate in the early stages and overestimate in the later stages of the concentration for all temperatures. The Page model produced a good enough fit except for concentration at 65°C, whereas the modified Page model described concentration of pomegranate juice worse than the original one. The logarithmic model was the best descriptive model as shown in Table 2. The residuals of this model also were found to be close to zero at all temperatures. Figure 3 includes the comparison of predicted and experimental values of total solids concentration change for the logarithmic model. The data were generally banded around a straight line of 55°C. Consequently, it can be said that the logarithmic model could adequately describe the concentration of pomegranate juice. A similar observation was reported by Togrul and Pehlivan [33], Akgun and Doymaz [34], Sacilik and

20

30

40

50

60

(B−B0)exp Figure 3 Experimental and predicted values of total solids concentration change for the logarithmic model at different temperatures

Elicin [35], Wang et al. [36], Doymaz [37], and Mohammadi et al. [38], who modeled the drying process of apricots, olive cake, apple slices, apple pomace, strawberries, and sliced kiwifruit, respectively. On the contrary, Byler et al. [39], Shivhare et al. [40], Tan et al. [41], and Falade and Solademi [42] showed that the Page model could describe the drying behavior of a quantity of biological materials more accurately than the logarithmic model and that the relationship between the rate constant and air temperature could be represented by the Arrhenius law. Moreover, Shivhare et al. [43] claimed that the logarithmic model underestimated the initial section of the drying curve and overestimated it in the latter stages. However, all these works refer to drying processes, and limited research has been achieved with regard to the modeling of a concentration process using thin-layer drying models. Assawarachan and Noomhorm [10] reported that the modified Page model was the best descriptive model for vacuum-microwave concentration of pineapple juice, whereas Yousefi et al.



[23] used a first-order reaction model to describe the change in the soluble solids concentration of pomegranate juice during microwave and conventional heating concentration. The effect of concentration temperature T (in K) on logarithmic model constants (a, b, and K) can be expressed by the following equations: 2 a ¼ 1:46 þ 7:26 10152 expð1:05 T Þ R ¼ 0:999 ð14Þ b¼

2:267 0:007 T 1 0:003 T

K ¼ 86:80

R2 ¼ 0:999

56:69 103 92:43 105 þ T T2

ð15Þ

R2 ¼ 0:903 ð16Þ

3.2 Degradation of total phenolics The total phenolics content in pomegranate juice during each concentration experiment is shown in Figure 4. The phenolic content of unprocessed pomegranate juice was about 65 mg/g of dry solids, whereas final phenolic content has increased by 18, 17.5, 30.5, 31.2, and 33.6%, after concentration at 45, 50, 55, 60, and 65°C, respectively. As can be observed, the maximum content of total phenolics observed during concentration at 65°C, due to the shorter time necessary to attain the specific moisture content, whereas at low temperatures the longer time of exposure to hot air, even at lower temperature, led to less phenolics content. A similar observation was reported by Erenturk et al. [31], who studied the hot air drying of

1.6

T (ºC) 45 50 55 60 65 Predicted

1.5

C/C0 (−)

1.4 1.3 1.2 1.1 1.0 0.9 0

10

20

30 t (min)

40

50

60

Figure 4 Experimental (symbols) and predicted (lines) values of total phenolics retention during concentration of pomegranate juice at 45, 50, 55, 60, and 65°C

389

rosehip fruits. However, Silva et al. [44] reported that during drying of camu camu slices, even considering that in order to attain the same final moisture content, samples dried at higher temperatures were exposed for a shorter period to hot air, raising the drying temperature increased the loss of ascorbic acid. In addition, the phenolic content values (in mg/g dry solids) increased at initial application of heat (0–20 or 0–30 min), but further processing decreased its content to a value 17.5–33.6% higher than the equivalent of unprocessed juice. The initial increase of total phenolics can be attributed to their release due to breakdown of cellular constituents. According to Haard and Chism [45], the phenolics in fruits and vegetables are more concentrated in outer parts of the cell rather than in the vacuoles. Hartley et al. [46] suggested that because phenolic acids are mainly bonded to carbohydrate and proteinaceous moieties, during thermal processing their release might occur due to breakdown of cellular constituents and covalent bonds. Therefore, the released polyphenolics constituents become more amenable for extraction. Although the disruption of cells may also result in the release of oxidative and hydrolytic enzymes, these enzymes are capable of oxidizing endogenous polyphenolics, but exposure to high temperature even for a short time can inactivate these enzymes and protect the polyphenolics from further decomposition [47]. The reduction in the phenolics content observed after concentration for 20–30 min can be attributed to their thermal degradation. However, the final content was higher than the equivalent of unprocessed juice possibly due to the development of new compounds with potential antioxidant capacity. Numerous studies have been conducted on the relationship between different heat treatments and content of phenolic compounds in plant extracts. Very often the results of these studies are contradictive. Processing and heating during jam making (at 104–105°C) decreases the content of total phenolics of some varieties of cherries and plums, whereas no significant change has observed in raspberries and in some varieties of cherries and plums [48]. However, the same study reported an increase or a decrease in antioxidant activity of cherries, plums, and raspberries depending on variety during jam processing. Canning of raspberries and blueberries increases the phenolic content by 50% [49], whereas the total phenolics of sweet corn has increased by 54%, after thermal processing at 100–121°C for 10–50 min [50]. In other studies, antioxidant activities in processed tomatoes [50, 51] and coffee [52] were retained or higher than their fresh equivalents. Kim et al. [53] reported that higher total phenolics content values were determined for whole



grape seed extract when subjected to thermal treatment of 150°C for 40 min and powdered grape seed extract at 100°C for 10 min as compared to non-treated control samples. Chang et al. [54] found a significant increase in phenolics content of processed tomatoes as compared to fresh ones, indicating that drying treatments resulted in an increase in phenolics content values. Several researchers have reported that when temperatures below 70°C are employed during concentration/drying processes, the integrity of fruit polyphenolics is retained. Nindo et al. [55] observed that at 60°C by heated air or a microwave spouted bed drying method, most phenolic compounds were preserved. In their study on the effect of drying temperature on red grape pomace, Larrauri et al. [56] also concluded that a dehydration temperature of 60°C resulted in no significant changes in the endogenous polyphenolics relative to freeze dried pomace. The increase or retention of antioxidant activities in processed foods is attributed to the development of new compounds with potential antioxidant capacity, although the content of naturally occurring antioxidants has significantly decreased due to the heat processing [52, 57]. Antioxidant compounds depletion in thermally treated fruits and vegetables may also be attributed to consumption of ascorbic acid and polyphenols as reactants in the Maillard reaction [58]. Although a decrease in the antioxidant potential is found for short heat treatments, a recovery of these properties has been reported during prolonged heat treatment. For example, Jiratanan and Liu [59] observed 12% reduction in phenolic content of the beets at initial application of heat (115°C for 15–30 min), but further processing raised its content back to the equivalent of unprocessed beets and eventually increased by 14% after processing at 115°C for 45 min. Similar results in the antioxidant capacity of aged citrus juice and orange juice have been reported as they became more discolored [60]. The antioxidant activity of Maillard reaction products (MRP) can be mainly attributed to the high molecular weight brown compounds, which are formed in the advanced stages of reaction. The initial reduction in the antioxidative activity can be attributed not only to thermal degradation of naturally occurring antioxidants but also to formation of early MRPs with pro-oxidant properties. The gain in antioxidant activity coincided with the formation of brown MRPs. Figure 4 also presents the adjustment of the empirical dependence approach model to the experimental data of total phenolics retention during each concentration experiment. Estimated parameters for the model are presented in Table 3. R2 and SSE values were 0.958 and

Table 3 Parameters of the empirical dependence model for total phenolics degradation during concentration of pomegranate juice Value

R2

SSE

9,056.8 78 75.9 1 424,573.3 2,031 449,967.9 2,306 –253,160.3 1,125

0.958

0.0030

Parameter ko (min−1) Ea (kJ/mol) A1 (–) A2 (g dry solids/g H2O) A3 (g dry solids/g H2O)2

0.0030, respectively. The R2 value is very good, but this value must be taken with caution, since R2 assumes model linearity, and in strongly nonlinear models it can be a poor estimate of the percentage of variance explained by the model [28]. The empirical model was also evaluated by comparing its predictions and experimental data during concentration of pomegranate juice at 58°C. Figure 5 shows the model’s predictions compared with the experimental values of total phenolics retention. To evaluate the goodness of fit of the model, linear regression and paired t-test were applied. The slope and the intercept of predicted values versus experimental values are not significantly different (p ¼ 0.05) from the theoretical values 1.00 and 0.00, respectively, whereas applying paired t-test the calculated t values were found lower than the theoretical t values (p ¼ 0.05). Therefore, the null hypothesis was retained: the empirical dependence approach model is suitable for modeling the degradation of total phenolics during concentration of pomegranate juice. 1.5 25 min C/C0(−) - predicted

390

15 min

1.4

20 min 1.3

30 min 10 min

1.2 5 min

1.1 3 min 1.0 1.0

1.1

1.2 1.3 1.4 C/C0 (−)- experimental

1.5

Figure 5 Experimental and predicted values of total phenolics retention at various times of pomegranate juice concentration at 58°C

4 Conclusions Concentration kinetics of pomegranate juice was investigated in a rotary vacuum evaporator at different



temperatures. Experimental data were compared with values predicted by eight thin-layer drying models. It was evident that the evaporation rate was better described by the logarithmic model. The temperature dependence of the concentration constants was also described by polynomial equations. In addition, during concentration of pomegranate juice, the maximum content of total phenolics observed at 65°C, due to the shorter time necessary to attain the specific moisture

391

content. The phenolic content, in dry solids basis, increased at initial application of heat, but further processing decreased its content to a value 17.5–33.6% higher than the equivalent of unprocessed juice. The retention of phenolics during concentration can be described by an empirical dependence approach model, which is a first-order decay model with an Arrhenius and a polynomial dependency on temperature and water content, respectively.

References 1. Fazaeli M, Yousefi S, Emam-Djomeh Z. Investigation on the effects of microwave and conventional heating methods on the phytochemicals of pomegranate (Punica granatum L.) and black mulberry juices. Food Res Int 2013;50:568–73. 2. Mohagheghi M, Rezaei K, Labbafi M, Mousavi SM. Pomegranate seed oil as a functional ingredient in beverages. Eur J Lipid Sci Technol 2011;113:730–6. 3. Türkyılmaz M, Tağı Ş, Dereli U, Özkan M. Effects of various pressing programs and yields on the antioxidant activity, antimicrobial activity, phenolic content and colour of pomegranate juices. Food Chem 2013;138:1810–18. 4. Cassano A, Drioli E. Concentration of clarified kiwifruit juice by osmotic distillation. J Food Eng 2007;79:1397–404. 5. Barbe AM, Bartley JP, Jacobs AL, Johnson RA. Retention of volatile organic flavor/fragrance components in the concentration of liquid foods by osmotic distillation. J Membrane Sci 1998;145:67–75. 6. Maccarone E, Campisi S, Lupo MC, Fallico B, Asmundo CN. Thermal treatments effects on the red orange juice constituents. Industria Bevande 1996;25:335–41. 7. Cassano A, Jiao B, Drioli E. Production of concentrated kiwifruit juice by integrated membrane process. Food Res Int 2004;37:139–48. 8. Perez NE, Schmalko ME. Convective drying of pumpkin: influence of pretreatment and drying temperature. J Food Process Eng 2009;32:88–103. 9. Bingol G, Pan Z, Roberts JS, Devres YO, Balaban MO. Mathematical modeling of microwave-assisted convective heating and drying of grapes. Int J Food Agric Biol Eng 2008;1:46–55. 10. Assawarachan R, Noomhorm A. Mathematical models for vacuum-microwave concentration behavior of pineapple juice. J Food Proc Eng 2011;34:1485–505. 11. Cihan A, Kahveci K, Hacihafizoglu O. Modelling of intermittent drying of thin layer rough rice. J Food Eng 2007;79:293–8. 12. Lee JH, Kim HJ. Vacuum drying kinetics of Asian white radish (Raphanus sativus L.) slices. LWTechnol – Food Sci Technol 2009;42:180–6. 13. Koca N, Burdurlu HS, Karadeniz F. Kinetic of colour changes in dehydrated carrots. J Food Eng 2007;78:449–55. 14. Sokhansanj S, Jayas DS. Drying of foodstuffs. In: Mujumdar AS, editor. Handbook of industrial drying. New York: Marcel Dekker, 1995.

15. Pan YK, Zhao LJ, Hu WB. The effect of tempering-intermittent drying on quality and energy of plant materials. Drying Technol 1999;17:1795–812. 16. Karel M. Prediction of nutrient losses and optimization of processing conditions. In: Tannenbaum SR, editor. Nutritional and safety aspects of food processing. New York: Marcel Dekker, 1979. 17. Alper N, Onsekizoglu P, Acar J. Effects of various clarification treatments on phenolic compounds and organic acid compositions of pomegranate (Punica granatum L.) juice. J Food Proc Preservation 2011;35:313–19. 18. Adams LS, Seeram NP, Aggarwal BB, Takada Y, Sand D, Heber D. Pomegranate juice, total pomegranate ellagitannins, and punicalagin suppress inflammatory cell signaling in colon cancer cells. J Agric Food Chem 2006;54:980–5. 19. Gil MI, Tomas-Barbara FA, Hess-Pierce B, Holcroft DM, Kader AA. Antioxidant activity of pomegranate juice and its relationship with phenolic composition and processing. J Agric Food Chem 2000;48:4581–9. 20. Kaplan M, Hayek T, Raz A, Coleman R, Dornfeld L, Vaya J, et al. Pomegranate juice supplementation to atherosclerotic mice reduces macrophage lipid peroxidation, cellular cholesterol accumulation and development of atherosclerosis. J Nutr 2001;131:2082–9. 21. Alper N, Bahceci KS, Acar J. Influence of processing and pasteurization on color values and total phenolic compounds of pomegranate juice. J Food Proc Preservation 2005;29:359–68. 22. Jettanopornsumran M. Copigmentation reactions of boysenberry juice. Master thesis, Massey University, Nelson, New Zealand, 2009. 23. Yousefi S, Djomeh ZE, Mousavi SM, Askari GR. Comparing the effects of microwave and conventional heating methods on the evaporation rate and quality attributes of pomegranate (Punica granatum L.) juice concentrate. Food Bioprocess Technol 2012;5:1328–39. 24. Singleton VL, Rossi JA. Colorimetry of total phenolics with phosphomolybdic–phosphotungstic acid reagents. Am J Enol Viticulture 1965;16:144–58. 25. Doymaz I. Thin-layer drying of spinach leaves in a convective dryer. J Food Process Eng 2009;32:112–25. 26. McMinn WA. Thin-layer modeling of the convective, microwave, microwave-convective and microwave-vacuum drying of lactose power. J Food Eng 2006;72:113–23.


392


27. Sharma GP, Verma RC, Pathare P. Mathematical modeling of infrared radiation thin layer drying of onion slices. J Food Eng 2005;71:282–6. 28. Frias JM, Oliveira JC. Kinetic models of ascorbic acid thermal degradation during hot air drying of maltodextrin solutions. J Food Eng 2001;47:255–62. 29. Mishkin M, Saguy I, Karel M. Optimization of nutrient retention during processing: ascorbic acid in potato dehydration. J Food Sci 1984;49:1262–6. 30. Lavelli V, Zanoni B, Zaniboni A. Effect of water activity on carotenoids degradation in dehydrated carrots. Food Chem 2007;104:1705–11. 31. Erenturk S, Gulaboglu MS, Gultekin S. The effects of cutting and drying medium on the vitamin C content of rosehip during drying. J Food Eng 2005;68:513–18. 32. Maskan M. Production of pomegranate (Punica granatum L.) juice concentrate by various heating methods: colour degradation and kinetics. J Food Eng 2006;72:218–24. 33. Toğrul IT, Pehlivan D. Modelling of drying kinetics of single apricot. J Food Eng 2003;58:23–32. 34. Akgun NA, Doymaz I. Modelling of olive cake thin-layer drying process. J Food Eng 2005;68:455–61. 35. Sacilik A, Elicin AK, Unal G. Drying kinetics of uryani plum in a convective hot-air dryer. J Food Eng 2006;76:362–8. 36. Wang J, Sun J, Liao X, Chen F, Zhao G, Wu J. Mathematical modeling on hot air drying of thin layer apple pomace. Food Res Int 2007;40:39–46. 37. Doymaz I. Convective drying kinetics of strawberry. Chem Eng Process 2008;47:914–19. 38. Mohammadi A, Rafiee S, Keyhani A, Emam-Djomeh Z. Moisture content modeling of sliced kiwifruit (cv. Hayward) during drying. Pakistan J Nutr 2009;8:78–82. 39. Byler RK, Anderson CR, Brook RC. Statistical methods in thin layer parboiled rice-drying models. Trans ASAE 1987;30:533–8. 40. Shivhare US, Gupta A, Bawa AS, Gupta P. Drying characteristics and product quality of okra. Drying Technol 2000;18:409–19. 41. Tan DL, Miyamoto K, Ishibashi K, Matsuda K, Satow T. Thinlayer drying of sweet potato chips and pressed grates. Trans ASAE 2001;44:669–74. 42. Falade KO, Solademi OJ. Modelling of air drying of fresh and blanched sweet potato slices. Int J Food Sci Technol 2010;45:278–88. 43. Shivhare US, Raghavan GS, Bosisio RG, Mujumdar AS, Vande-Voort FR. Particulate drying in microwave environment with varying initial moisture content. Powder Handling Proc 1991;3:153–7. 44. Silva MA, Pinedo RA, Kieckbusch TG. Ascorbic acid thermal degradation during hot air drying of camu-camu (Myrciaria dubia [H. B. K.] McVaugh) slices at different air temperatures. Drying Technol 2005;23:2277–87.

45. Haard NF, Chism GW. Characteristics of edible plant tissues. In: Fennema OW, editor. Food chemistry, 3rd ed. New York: Marcel Dekker, 1996:944–1011. 46. Hartley RD, Morrisson WH, Hilmmelsbach DS, Borneman WS. Cross-linking of cell wall phenolic arabinoxylans in graminaceous plants. Phytochemistry 1990;29:3705–9. 47. Wojdylo A, Oszmianski J, Czemerys R. Antioxidant activity and phenolic compounds in 32 selected herbs. Food Chem 2007;105:940–9. 48. Kim DO, Padilla-Zakour OI. Jam processing effect on phenolics and antioxidant capacity in anthocyanin-rich fruits: cherry, plum and raspberry. J Food Sci 2004;69:395–400. 49. Sablani SS, Andrews PK, Davies NM, Walters T, Saez H, Syamaladevi RM, et al. Effect of thermal treatments on phytochemicals in conventionally and organically grown berries. J Sci Food Agric 2010;90:769–78. 50. Dewanto V, Wu XZ, Adom KK, Liu RH. Thermal processing enhances the nutritional value of tomatoes by increasing total antioxidant activity. J Agric Food Chem 2002;50:3010–14. 51. Re R, Bramley PM, Rice-Evans C. Effects of food processing on flavonoids and lycopene status in a Mediterranean tomato variety. Free Rad Res 2002;36:803–10. 52. Nicoli MC, Anese M, Parpinael MT, Franceschi S, Lerici CR. Study on loss and formation of antioxidants during processing and storage. Cancer Lett 1997;114:71–4. 53. Kim K-H, Tsao R, Yang R, Cui SW. Phenolic acid profiles and antioxidant activities of wheat bran extracts and the effect of hydrolysis conditions. Food Chem 2006;95:466–73. 54. Chang CH, Lin HY, Chang CY, Liu YC. Comparisons on the antioxidant properties of fresh, freeze dried and hot air dried tomatoes. J Food Eng 2006;77:478–85. 55. Nindo CI, Sun T, Wang SW, Tang J, Powers JR. Evaluation of drying technologies for retention of physical quality and antioxidants in asparagus. LWT – Food Sci Technol 2003;36:507–16. 56. Larrauri JA, Ruperez P, Saura-Calixto F. Effect of drying temperature on the stability of polyphenols and antioxidant activity of red grape pomace peels. J Agric Food Chem 1997;44:1390–3. 57. Anese M, Manzocco L, Nicoli MC, Lerici CR. Antioxidant properties of tomato juice as affected by heating. J Agric Food Chem 1999;79:750–4. 58. Kaanane A, Kane D, Labuza TP. Time and temperature effect on stability of moroccan processed orange juice during storage. J Food Sci 1988;53:1470–1473, & 1489. 59. Jiratanan T, Liu RH. Antioxidant activity of processed table beets (Beta vulgaris var. conditiva) and green beans (Phaseolus vulgaris L.). J Agric Food Chem 2004;52:2659–70. 60. Lee HS. Antioxidative activity of browning reaction products isolated from storage-aged orange juice. J Agric Food Chem 1992;46:550–2.
