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Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction
Journal of Food Engineering xxx (2015) 1e8
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Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction pez-Malo* Avelina Franco-Vega, Nelly Ramírez-Corona, Enrique Palou, Aurelio Lo rtir, San Andr Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Am ericas Puebla, Sta. Catarina Ma es Cholula, Puebla 72810, Mexico
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 May 2015 Received in revised form 18 September 2015 Accepted 23 September 2015 Available online xxx
Microwave assisted extraction (MAE) is an emerging technique of extraction that improve yields and reduce process time and energy. The aim of this work was to evaluate the effect of different MAE process parameters in the extraction yield of orange peel essential oil (EO), and the description and simulation of this process with mathematical models based on mass transfer fundamentals. For the assessment of process parameters effects on EO yield, different extractions were carried out following a two-level factorial design varying orange peel particle shape (spheres or plaques) and moisture content (dry or not), as well as microwave potency (360 or 540 W). Results demonstrated that particle size, moisture content, and its interaction significantly affected (p < 0.05) the yield obtained and had an influence on the extraction mechanism. Model parameter values associated to mass transfer coefficients (k1 and k2) indicated that diffusion was the process that defined extraction velocity. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Microwave assisted extraction Essential oil Mass transfer Modeling
1. Introduction Citrus are the most cultivated fruits in the world, being oranges 60% of the world fruit production. The orange peel, depending on the orange variety, may represent 45% of the total bulk. Orange peel includes the epidermis covering the exocarp that consists of irregular parenchymatous cells, which encloses numerous glands ~ ez et al., 2013). The oil or oil sacs (Farhat et al., 2011; Vel azquez-Nun in these sacs represents the citrus essential oil (EO) that is obtained as by-product of citrus processing (Bousbia et al., 2009). Besides its use as a flavoring agent, citrus EO has gained relevance in the food industry due to its antimicrobial effect against both bacteria and zquez-Nun ~ ez et al., 2013; fungi (Rezzoug and Louka, 2009; Vela Lago et al., 2014). Typical processes for obtaining essential oils from citrus include cold pressing, solvent extraction, and different distillation techniques (Bica et al., 2011; Lago et al., 2014). However, these procedures involve several disadvantages, such as the use of volatile and hazardous solvents, low yields, long extraction times, and high energy consumption (Zu et al., 2012). The extraction method is one of the prime factors that determine the quality of EOs (Tongnuanchan and Benjakul, 2014); thus
* Corresponding author. pez-Malo). E-mail address: [email protected] (A. Lo
the use of new extraction techniques for natural substances, which typically use less solvent and energy, such as supercritical fluid extraction, ultrasound extraction, microwave assisted extraction, and sub-critical water extraction are being evaluated (Bousbia et al., rino-Issartier et al., 2010). Among these emergent tech2009; Pe nologies, microwave assisted extraction (MAE) has showed many advantages such as convenience, less processing times, and high efficiency (Zhai et al., 2009; Flamini et al., 2007). MAE uses microwave radiation as the source of heating for the solventesample mixture. Due to the particular effects of microwaves on matter (namely dipole rotation and ionic conductance), heating with microwaves is instantaneous and occurs in the interior of the sample, leading to rapid extractions (Camel, 2001). The rinomain advantage of MAE resides in its heating mechanism (Pe Issartier et al., 2010); acceleration of the extraction can be partly explained by the specific effect of microwaves on plant material (Camel, 2001). Mathematical modeling of extraction processing must be considered as a fundamental step during operation of an efficient industrial process (Xavier et al., 2011; Reyes-Jurado et al., 2014). Mathematical models are used to simulate different process scenarios without the need to run a large number of experimental trials and identify the best conditions for the process (Cassel et al., 2009; Rezzoug and Louka, 2009). Different mathematical approaches have been reported that explain the process involved
http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025 0260-8774/© 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
during traditional methods of extraction. Some authors have described the extraction variables effects on essential oil yield through statistical analysis of experimental data (Ghasemi et al., 2011; Parikh and Desai, 2011; Zhang et al., 2012), being response surface methodology (RSM) successfully applied for optimizing extraction conditions. However, this type of analysis represents parameter values for the optimization process within the experimental values, thereby; it just can be used in the experimental range tested and just represents the evaluated variables effect on analyzed response (Almeida et al., 2008). For this reason, models that try to explain the physical mechanism of the extraction process based on mass transfer fundamentals have been developed (Sovov a and Aleksovski, 2006; Cassel et al., 2009; Moreno et al., 2010; Xavier et al., 2011) with the aim of determining physical parameters that make possible extrapolations. Sovov a and Aleksovski (2006) developed a model for the hydrodistillation process; in their model two different kinds of particles were considered: spherical particles or slabs, with different distribution of the oil in each one. In their assumptions, the only mass transfer process that had to be taken into account was the oil diffusion from the particle core to the region of broken cells, ignoring the initial rate of distillation from intact cells. Similar to and Aleksovski, Xavier et al. (2011) proposed a model based Sovova on mass transfer fundamentals, their model considers a fluidized bed and is based on the concept of broken and intact cells, but it considers the presence of two extraction periods (each one associated with the velocity of EO extraction from cells) for the description of the extraction curve based on mass balance of the solute (EO). Despite that MAE has proven to be a more effective extraction process than traditional methods, to our knowledge, available approaches to describe this process are primarily based in statistical analysis of process variables (Wang et al., 2007; Fang et al., 2010; Farhat et al., 2011; Song et al., 2011), while the study in terms of mass transfer is limited. Since MAE is based in a distillation process with a different source of heating, it should be expected that MAE can be properly modeled by describing the mass transfer mechanisms during distillation, taking into account the microwave effect on the diffusion coefficients. The aim of this work was to evaluate the effect of different MAE process parameters in the extraction yield of orange peel EO, to describe the process through statistical analysis based on a factorial design, and as well as description of this process with a mathematical model based on mass transfer fundamentals.
2.2. Microwave assisted extraction MAE was carried out on a NEOS microwave extraction system (800 W, 60 Hz) (Milestone, Italy), which is designed to utilize different beakers for the extraction of different sample/solvent volumes. The MAE system is equipped with a TFT multicolor liquid crystal screen, a power sensor (power range 0e1000 W), an infrared temperature sensor, a temperature controller, and a magnetic stirrer in the base of the reactor. Fig. 1 displays a schematic description of the hydro-distillation system. During extraction, a sample of 250 g of orange peel and 700 mL of distilled water were placed into the beaker (A), which was introduced and fixed to the MAE system. The sample was then subjected to the extraction process for 50 min at two different irradiation powers (360 or 540 W). A condenser (B) was used to collect the extracted essential oil in a graduated trap (C). Irradiation power and extraction time were controlled from the equipment front panel (D). During the process, system temperature rises until the sample starts to boil and the extraction starts; since each sample was processed at different conditions of moisture content, particle size and microwave power, the time required to achieve the boiling temperature (90e94 C) was also different depending on the characteristics of the sample, such delay time was registered as tCUT. Extracted essential oil was dried with anhydrous sodium sulfate and stored at 4 C in dark vials until used. The amount of essential oil was determined during extraction and gravimetrically after collection; the extraction yield was expressed as the percent ratio
2. Materials and methods 2.1. Plant material The essential oil was extracted from orange (C. sinensis var. Valencia) peel without bagasse obtained from a local juice producer of San Andres Cholula, Puebla (Mexico) during AprileAugust 2014. The peel was used at two moisture levels, high (50% of moisture) and low (10% of moisture), and using two different shapes of particles, plaques (1 cm) and spheres (40 mm). To adjust orange peel moisture content, the peel was dried in a tray dehydrator (Excalibur, USA) for different times at 35 C according to the final target moisture content, the final moisture was determined in a forced air oven at 105 C for 24 h according to the official method reported by A.O.A.C (2000). Peels were ground in a domestic mixer to obtain the orange peel spheres and their particle size was determined with a particle analyzer (Bluewave, Microtrac, USA), while the peel plaques were obtained manually.
Fig. 1. Schematic description of the microwave assisted extraction system. Beaker (A), condenser (B) graduated trap (C), and equipment front panel (D).
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
of the mass of extracted essential oil to the mass of orange peel in dry basis (Li et al., 2012). 2.3. Process parameters effect Two-level factorial designs allow the simultaneous evaluation of a number of factors' effects on a particular process; as well as permit determining possible interactions between (or among) tested factors (Montgomery, 2009). In our case, each MAE process parameter (initial moisture content, particle size, and microwave power) was evaluated at two levels: low (1) and high (þ1). The coded and un-coded values of each factor and their respective levels are presented in Table 1. The evaluated responses were EO extraction yield, as well as other calculated model parameters described in Section 3. Experiments were replicated three times; extraction yields and other calculated responses are reported as average ± standard deviation values (Table 1). Each evaluated response was analyzed by two-level factorial design to fit polynomial equations and determine significant (p < 0.05) individual and interactions effects:
R ¼ b0 þ
X
bi Xi þ
X
bii Xi Xj þ
X
biii Xi Xj Xk þ 2
(2.1)
where, R is the predicted response; X are the independent variables in coded values; b0 is the constant; bi are linear effects; bii are double interaction effects, and biii is the triple interaction effect. The statistical analysis was performed using Minitab 16 (Minitab Inc., State College, PA, USA). The significance of each term in the polynomial model (linear, double, and triple interactions) was tested by the associated ANOVA in the factorial design analysis, as well as the accuracy of the regression model (Montgomery, 2009). 2.4. Physical properties
2013). The superficial area a0 (cm2/g) of the particle-bed was represented by the surface area of the total particles contained in the bed (Chikazawa and Takei, 2006), geometrically calculated assuming that particle size is uniform for both systems, spherical with a diameter of Di and cubic particles for the bed of plaques with a length of li. The average particle diameter (Dm) was determined with a particle analyzer (Bluewave, Microtrac, Montgomeryville, PA).
3. Mathematical modeling For the modeling of orange peel EO microwave assisted extraction, a model that takes into account the influence of the process parameters considered in the factorial design (Section 2.3) was used. The model was proposed by Xavier et al. (2011), which is based on the concept of broken and intact cells and considers two extraction periods, the first one governed by phase-equilibrium, while the second one is limited by internal diffusion in the particles. The first part of the extraction curve is associated to the extraction of the free solute from broken cells. In this extraction step (hypothetical case with negligible external mass transfer resistance), the fluid phase is in equilibrium with solid phase throughout the extractor. The second period is controlled by the solute diffusion from inner cells of vegetal structure and corresponds to the diffusion from intact cells. In Xavier et al. (2011) model, mass transfer properties of recovered oil were considered constant throughout the process. The general equations considered in this model are the mass balances per unit volume extraction bed consist of equations for the solute in fluid and solid-phases described by (eqs. 3.1 and 3.2).
vY vY þ rf u ¼ Jðx; YÞ vt vt
rf ε
The orange peel samples used for the extraction were considered as a particle bed; apparent density, bulk density, porosity, and surface area were determined for each extraction particle-bed in order to satisfy the mathematical model described in Section 3. Particle density (rp) and bulk density (rS) were determined according to Shrestha et al. (2008). Bulk density is defined as the ratio of the mass of the sample-particle bed in a vessel to the volume of the bed (including the voids among primary particles), for its measurement 20 g of orange peel were weighed in a graduated cylinder of 100 mL and the occupied volume was registered. Apparent density was determined using a pycnometer and calculated as reported by Barbosa-C anovas et al. (2006). With the values lez-Ruíz, of rS and rp, the bed void fraction (ε) was calculated (Ve
3
rS ð1 εÞ
(3.1)
vx ¼ Jðx; YÞ vt
(3.2)
Associated fluxes are:
Jðx; YÞ ¼ kf a0 rf Y * Y
for x > xk ;
Jðx; YÞ ¼ kS a0 rS x for x xk ;
first period
(3.3)
second period
(3.4)
According to Xavier et al. (2011), the model solution can be expressed in terms of the maximum value for the extracted oil (M∞), considering the two extraction periods, described as:
Table 1 Two-level factorial design to evaluate the effect of selected microwave assisted extraction factors on orange peel essential oil yield, modeling parameters, and characteristic times of the process. Run
Microwave power (Watts)
C B H G D E F A
540 360 360 540 360 540 360 540
þ1 1 1 þ1 1 þ1 1 þ1
Moisture content (%) 50 10 10 10 50 50 50 10
þ1 1 1 1 þ1 þ1 þ1 1
Particle shape
Sphere Sphere Plaque Plaque Sphere Plaque Plaque Sphere
1 1 þ1 þ1 1 þ1 þ1 1
Yield (% dry basis)
1.56 2.47 0.92 0.99 1.22 0.94 0.98 2.73
± ± ± ± ± ± ± ±
0.13 0.05 0.42 0.15 0.26 0.12 0.10 0.43
k1 (g/min)
0.20 0.45 0.21 0.34 0.22 0.43 0.14 0.39
± ± ± ± ± ± ± ±
0.07 0.28 0.13 0.12 0.05 0.14 0.02 0.14
k2 (min1)
0.10 0.09 0.33 0.29 0.14 0.38 0.38 0.12
± ± ± ± ± ± ± ±
0.01 0.01 0.07 0.02 0.05 0.12 0.09 0.01
tc (min)
12.00 17.75 3.00 12.75 1.50 2.25 2.50 22.75
± ± ± ± ± ± ± ±
tCUT (min)
1.41 2.10 1.41 1.06 0.71 0.35 0.01 3.47
11.25 15.75 13.50 6.50 19.00 10.50 18.75 8.50
± ± ± ± ± ± ± ±
0.35 1.06 0.71 0.71 0.01 0.71 0.35 0.71
ks (g/min*cm2)
2.64 2.21 0.35 0.15 2.93 0.12 0.05 2.77
± ± ± ± ± ± ± ±
0.22 0.15 0.05 0.05 0.02 0.05 0.01 0.17
k1 constant associated to the equilibrium period; k2 constant associated to the diffusion controlled period; tCUT process time to reach an adequate temperature to start sample boiling and begin the oil extraction; tc time at which the change of phases is observed during the extraction process (related to the slope change); and ks solid-phase mass transfer coefficient.
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
Z Mt ¼ M∞
t 0
8 > > k1 t for the first period _ mYdt < M∞ > M∞ > : 1 ek2 t for the second period
(3.5)
where k1 can be also expressed as eq. (3.6), and k2 is related to the mass transfer coefficient as can be seen in eq. (3.7).
_ * k1 ¼ mY k2 ¼
ks a0 ð1 εÞ
(3.6) (3.7)
As can be noticed, the first period is a linear function of time, while the second period follows an exponential expression. Detailed methodology of model development can be found in Xavier et al. (2011). The normalized extraction curves were obtained by plotting the mass fraction of recovered essential oil (Mt/M∞) versus the extraction time. The mass fraction was calculated as the ratio of the EO's mass obtained at time t (Mt) to the EO's mass obtained at the end of the process (M∞). Data were fitted to expressions described in eq. (3.5), and the associated parameters k1 and k2 were determined by means of nonlinear regression. The obtained values of k2 were then used to calculate the mass transfer coefficient ks (eq. (3.7)). Additionally, the time at which the phase change occurs was registered as the point in which the slope changes (tc). Different operation conditions and particle characteristics were evaluated in order to assess the effect of these conditions on the mass transfer mechanisms. 4. Results and discussion 4.1. Effect of process parameters on the efficiency of EO extraction The obtained orange peel EO yields resulting after the proposed MAE process following the two-level factorial design at two microwave powers (360 or 540 W), two initial orange peel moisture content (10 or 50%), and two particle shapes (plaques or spheres) are presented in Table 1, these data are expressed in terms of the oil yield (expressed in dry basis) obtained at the end of each process. In general, extraction yield was higher than 0.9%, achieving in some conditions more than 2.5%. The yields obtained in our study were higher than those reported in the literature; lower yields (0.13%e0.74%) were reported for orange maltaise (Citrus sinensis) by Bourgou et al. (2012) obtained by hydrodistillation and by Strano et al. (2014) for Tarocco orange after three hours of hydrodistillation (2.1% of yield). Rezzoug and Louka (2009) obtained a maximum yield of 2% for orange peel EO by steam distillation but in a process with double the time (100 min) that in our case. Besides the increase in yields achieved by microwave assisted extraction, another of the advantages is its ability to reduce processing times. The greatest yields were obtained when extraction was carried out with spheres of dry orange peel (experiments A and B); at these conditions, no influence of the microwave power was observed with regards to the extraction efficiency (p > 0.05, Table 2). Lower yields were obtained for other evaluated processing conditions. Table 2 presents the obtained coefficients and their probability of being significant after analyzing the two-level factorial design. Good adjustments of eq. (2.1) to the experimental data were verified through the determination coefficients obtained (R2 > 0.85 for the majority of the analyzed responses, except for k1 where R2 was 0.50), representing the proportion of variation in the response
explained by the terms in the model. The results demonstrate the significant effect (p < 0.05) of initial moisture content and particle size on essential oil yield. In the case of particle size, the decrease of it in the sample (spheres) improved extraction (Fig. 2) and this can be related to an increase in the superficial area which promotes a better contact of the sample with the solvent and penetration of and microwaves (Veggi et al., 2012). Different to our results, Sovova Aleksovski (2006) reported that for extraction of thyme oil from the herb Thymus serpyllum by steam distillation, yields for the smallest particles (0.18 mm) were lower than those for large particles; they assumed that part of T. serpyllum oil, easily accessible, was lost from their surface during gridding. Letellier and Budzinski (1999) affirm that a wet matrix (high moisture) improves the extraction recovery in most cases, due to microwaves interacting selectively with the free water molecules present in the gland and vascular systems, leading to rapid heating and temperature increase, followed by rupture of the walls and release of the essential oils into the solvent. Camel (2001) reported that in some cases, drying the matrix before the extraction or adding a drying agent has favored the MAE process. Similar to our results, Moreno et al. (2010) obtained higher yields of extraction using dry samples for the extraction of eucalyptus EO by steam distillation; these authors reported a decrease in yield when the moisture content in the eucalyptus leaf increase from 16.1 to 35.9%. However, these results are associated with a process of higher costs due to the previous processing (drying) step needed before extraction. In our experiments the cost of the process using a trydryer increases, approximately, 10 times the price of using samples with the high moisture content. Microwave power did not affected in EO extraction yield, Farhat et al. (2009) during lavender flowers EO extraction by microwave steam diffusion did not find difference in oil yield when they tested five different microwave powers between 50 and 400 W, observing that the only effect in the process was the time needed for EO extraction. The effect of the interaction between particle size and moisture content observed (Table 2) in the statistical analysis was significant (p < 0.05), but this is not the only interaction that affects the efficacy of EO extraction, since the interaction between particle size and microwave power was also significant (p < 0.05) on EO yield, as can be observed in Fig. 2. Orange peel EO extraction by MAE is favored by the use of samples with low moisture content and with a sphere particle shape. However, these affirmations are only true for a range of levels within the ones tested in this work. Therefore, with the aim to understand the physical mechanism of extraction, a model based on mass transfer was also studied. Due to the relevance that particle size and moisture content have in the process, the model proposed by Xavier et al. (2011) was selected, since the model takes into account both variables. 4.2. Extraction kinetics Fig. 3 presents the evolution of the orange peel EO extraction yield during MAE at selected conditions. Results are presented in terms of the mass fraction of obtained EO at different process times (Mt/M∞). As can be seen in Fig. 3, the extraction process clearly displays an exponential behavior with the presence of two phases. The first phase that corresponds to a rapid increase in the yield, which other authors have associated with an easy accessibility of solvent to the EO (Reverchon et al., 1999; Farhat et al., 2009), and a second phase, almost asymptotic, that defines the end of the process and it is limited by the mass transfer. This kind of behavior in the extraction of EO from plants have been observed not only for microwave extraction, it was found that the presence of the two phases is due to changes in the mass transfer mechanisms and the changes in the surface area from which essential oils evaporates
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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5
Table 2 Coefficient values of microwave assisted extraction process parameters (coded) and interactions on orange peel essential oil yield, modeling parameters, and characteristic times of the process. Term
Yield (% dry basis)
k1 (g/min)
k2 (min1)
tc (min)
tCUT (min)
ks (g/min*cm2)
Constant Microwave power (p) Initial moisture content (%M) Particle size (ps) p*%M p*ps %M*ps p*%M*ps R2
1.096* 0.054 0.509* 0.392* 0.016 0.079* 0.344* 0.029 0.93
0.298* 0.043 0.050 0.018 0.025 0.063* 0.055 0.015 0.50
0.229* 0.006 0.021 0.116* 0.004 0.003 0.014 0.013 0.88
9.313* 3.125* 4.750* 4.188* 0.563 0.750* 2.000* 1.937* 0.97
12.969* 3.781* 1.906* 0.656* 0.219 0.031 0.406* 0.094 0.98
1.403* 0.017 0.033 1.235* 0.072* 0.050 0.115* 0.140* 0.99
k1 constant associated to the equilibrium period; k2 constant associated to the diffusion controlled period; tCUT process time to reach an adequate temperature to start the sample boiling and begin the oil extraction; tc time at which the change of phases is observed during the extraction process (related to the slope change); ks solid-phase mass transfer coefficient; and R2 coefficient of determination. *Coefficients with significant effect (p < 0.05) in the evaluated response.
(Reis-Vasco et al., 2000). Hence, the mechanism of the extraction is related to the physical properties of the sample while the energy source utilized for the EO extraction determines the velocity at which each phase appears. Different to our results, Haj Ammar et al. (2013) tested a mathematical model based only in diffusion during extraction of EOs of myrtle (Myrtus communis L.), rosemary (Rosmarinus officinalis L.), or sour orange (Citrus aurantium L.), observing that the calculated values by their model showed deviations with regards to the experimental ones during the first minutes of the process, these deviations are related to the omission of the first phase of the process (the equilibrium) that in Xavier et al. (2011) model, utilized in our work is taken into account. Comparison among the experimental data and the results of the mathematical modeling of MAE EO yield by the model proposed by Xavier et al. (2011) are presented in Fig. 3. Data are plotted without taking into account the time that the process need to reach an adequate temperature to start the sample boiling and begin the oil extraction (tCUT), since such time only affects the total processing time but not parameters estimation. The analysis of the results indicated that, in general, tested mathematical model adequately fitted the experimental data (R2 > 0.90); the model goodness of fit to experimental data is presented in Fig. 4. The poorest fit for the second phase was observed for case E (Table 1), presented in Fig. 2d; this sample has the largest moisture content and largest particle size, and as described in Table 2 it also corresponds to the larger variability data for k2. In the different curves displayed in Fig. 2, it can be seen that the maximum oil extracted was reached after a time varying between 18 and 40 min, being faster extractions realized at 540 W of microwave power (Fig. 2). Microwave power did not have a significant effect (p > 0.05) on yield (Table 2). However, the effect observed (Fig. 3) in the time needed to reach the maximum yield, it would seem that the use of high power can reduce process time and probably costs; yet, it is necessary to take into account that microwave power is important to ensure the essential oil is extracted quickly; but, the power should not be too high, since higher powers promote a faster temperature rise, such that in some cases the cooling system is not able to condensate all vapor at the same rate and it may result in loss of volatile compounds (Camel, 2001). In Table 1, obtained values for the parameters k1 and k2 (associated with extraction phases) are presented, as well as the values for the time that the processes need to reach an adequate temperature to start the sample boiling and begin the oil extraction (tCUT), the time in which the change of phases is observed during the extraction process, related to the slope change (tc) and the solid-phase mass transfer coefficient for each experiment (ks). Reverchon et al. (1999) suggested that in the first phase of the
extraction curve, the oil is freely available for extraction; this linear part of the extraction process (also called the equilibrium phase) is represented by k1. Observing the curves of extraction; we expected that the equilibrium phase vary according to the physical properties of the sample, experiments E and F (which have the same initial moisture content and particle shape) the slope of this first part of the curve seemed to be higher than in other conditions and this could be related with a higher process velocity in the equilibrium phase; however, particle size and initial moisture content of the sample did not affect (p > 0.05) k1 (Table 2), but the interaction (Fig. 2) of particle size with microwave power had a significant effect (p < 0.05) on k1. These results indicate that the amount of easily accessible EO in orange peel is the same at any condition of the sample, although the interaction between particle size with microwave may enhance the velocity at which the process begins (Fig. 2), and that the only mass transfer process that affects the oil extraction rate is the EO diffusion from the particle core to the region of broken cells (Sovov a and Aleksovski, 2006; Wang and Weller, 2006), which is promoted at higher microwave power levels. The parameter k2 is related with the diffusion of the EO from the inner part of the sample cells, hence with the internal diffusion in the particles. In contrast to k1, k2 was significantly affected (p < 0.05) by particle size (Table 2); hence, the solid-phase mass transfer coefficient (ks) was determined from k2 combined with sample physical properties. The mass transfer coefficient can be determined if the specific surface area is known. The values for ks were determined and are also presented in Table 1; it can be seen that the overall mass transfer coefficient obtained for experiments with plaque shape were lower than the obtained in experiments using a sphere shape (Fig. 2). These results confirm that reducing the particle size of the sample facilitates diffusion (and extraction) of the EO, because it permits increasing surface contact to occur between the plant material and the solvent, therefore facilitating essential oil extraction processes (Haj Ammar et al., 2013). However, the values of ks obtained are low compared with the values obtained by Farhat et al. (2009) for the diffusion of lavender flowers EO during microwave steam diffusion (MSD). In their work they used different particle sizes, but the solvent was steam, thus they assumed that the enhancement in the mass transfer observed during MSD can be due that in MAE all the microwave energy is mainly absorbed by water for heating and vaporization, and only a fraction is absorbed by the essential oils inside the sample. Another explanation for the effect of the initial moisture content observed in the diffusion rates, that can be associated with the presence of higher porosity in the bed of dry samples than in the bed of wet samples, which also promotes the contact of the solvent with the
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
360
540
360
2.0
2.0
1.5
1.5
Y 1.0 (%)
Y 1.0 (%)
0.5
0.5
0.0
0.0 0%
20%
40%
60%
plaque
IniƟal moisture content
360
sphe r e ParƟcle size
540
360
0.5
0.5
0.4
0.4
0.3
540
0.3
k1
k1
0.2
0.2
0.1
0.1
0.0
0.0
0%
20%
40%
60%
plaque
IniƟal moisture content
360
sphe r e ParƟcle size
540
360
0.4
0.4
0.3
0.3
k2 0.2
k2 0.2
0.1
0.1
0.0
540
0.0 0%
20%
40%
60%
plaque
IniƟal moisture content
360
ks
540
sphe r e ParƟcle size
540
360
2.5
2.5
2.0
2.0
1.5
ks
1.5
1.0
1.0
0.5
0.5
0.0
540
0.0 0%
20%
40%
60%
IniƟal moisture content
plaque
sphe r e ParƟcle size
Fig. 2. Interaction plots (each plot displays the interaction between initial moisture content or particle size and microwave power (360 or 540 W) during microwave assisted extraction) on orange peel essential oil yield (Y, % dry basis), and selected modeling parameters: k1 (constant associated to the equilibrium period, g/min), k2 (constant associated to the diffusion controlled period, min1), and ks (solid-phase mass transfer coefficient, g/min*cm2).
particle surface (Eggers and Pliz, 2011). As mentioned above, the microwave power utilized in the process affected only the time of extraction, the time needed for the start of EO extraction (tCUT) (Table 1) shows that the processes in which the microwave power was 360 W needed more time for EO extraction than that required at 540 W. In addition, microwave
power, particle size, initial moisture content, and the interaction between moisture content and particle size also had a significant effect (p < 0.05) in tCUT (Table 2), being lower in samples with high initial moisture contents (50%) than for dry samples (10%). Due to tCUT can be associated with the cost of the process, the best conditions to reduce this time were using a sample with low initial
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Fig. 3. Extraction kinetics of orange peel essential oil during microwave assisted extraction at 540 W and selected moisture conditions and particle size: (a) sphere with 10% moisture content, (b) sphere with 50% moisture content, (c) plaque with 10% moisture content, or (d) plaque with 50% moisture content. Experimental data (A) and mathematical model fit (phase 1ee, phase 2 ).
sample was dry and with a sphere shape (A and B), indicating that in processes with samples at lower initial moisture content and higher surface area the phase of mass transfer in solid phase takes more time to start.
5. Conclusions
Fig. 4. Experimental data vs predicted values for different operation conditions.
moisture content (10%) in a MAE process at 540 W. This can be explained by the fact that at the start of the extraction, rate is effectively limited by the solubility of the oil into the fluid phase and this situation continues until the depletion of oil from the lower portion of the bed has reduced the effective bed height to the point where the fluid is no longer saturated when it leaves the top of the bed (Reverchon et al., 1999). Finally, the time of the process for the change of the equilibrium phase to the mass transfer represented by tc was significantly affected (p < 0.05) by microwave power, moisture content, and particle size, by the interactions between moisture content and particle shape and microwave power and particle size, as well as by the triple interaction of process factors, presenting higher values of tc the processes where the
The evaluated parameters during microwave assisted extraction of orange peel essential oil had a significant effect in the extraction process, demonstrating that the conditions of the sample (particle size and initial moisture content) determine the extraction yield, while microwave power affects the time of extraction. The model based on mass transfer process utilized to predict the physical mechanism of extraction showed to be a satisfactory approach to the mathematical representation of the extraction of orange peel essential oil by microwave assisted extraction, being capable to describe the two observed phases during the extraction processes. According to the parameters of the model (k1 and k2, associated to fluid-phase mass transfer coefficient and solid-phase mass transfer coefficient, respectively) the mass transfer in solid phase was the only phase significantly (p < 0.05) affected by the characteristics of the sample, indicating that in orange peel essential oil extraction by microwave assisted extraction the solute diffusion from the inner cells is the process that defines the extraction velocity.
Acknowledgments Financial support for the project 180748 from the National Council for Science and Technology (CONACyT) of Mexico and ricas Puebla (UDLAP) is gratefully Universidad de las Ame acknowledged. Author Franco-Vega acknowledges financial support for her PhD studies in Food Science from CONACyT and UDLAP.
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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Nomenclature tCUT
rp Ɛ a0
Di Li Dm
rf rs
u J kf ks Y* Y x xk k1 k2 ṁ t Mt M∞ R
bi Xi 2
Time at which the essential oil extraction starts, min Particle density, g/cm3 Porosity (bed void fraction), dimensionless Superficial area, (cm2/g) (specific surface area per unit volume of extraction bed (m2/m3) Diameter of sphere particles, cm Length of plaque particles, cm Average particle diameter, cm Solvent density, g/cm3 Solid density (bulk density), g/cm3 Superficial fluid velocity, cm/s Flux of solute, g/cm3 s Fluid phase mass transfer coefficient, m/s Solid phase mass transfer coefficient, m/s Equilibrium fluid phase mass fraction, g/g Mass fraction in fluid phase, g/g Mass fraction in solid phase, g/g Easily accessible solute in solid phase, g/g Constant associated to the equilibrium period, g/min Constant associated to the diffusion controlled period, 1/ min Solvent flow rate, g/s Extraction time, min Mass of extract at time t, g Maximum value of the mass extracted, g Predicted response from the factorial design Coefficients from the polynomial model from the factorial design Independent factors in the factorial design Error
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(Eds.), Microwave- Assisted Extraction for Bioactive Compounds: Theory and Practice. Springer, New York, pp. 16e35. zquez-Nun ~ ez, M., Avila-Sosa, R., Palou, E., Lo pez-Malo, A., 2013. Antifungal Vela activity of orange (Citrus sinensis var. Valencia) peel essential oil. Food control. 31, 1e4. lez-Ruíz, F., 2013. Introduccio n a las Propiedades Físicas de los Alimentos, p. 92. Ve mica Espan ~ ola. Germany. Editorial Acade Wang, L., Weller, C., 2006. Recent advances in extraction of nutraceuticals from plants. Trends Food Sci. Technol. 17, 300e312. Wang, S., Chen, F., Wu, J., Wang, Z., Liao, X., Hu, X., 2007. Optimization of pectin extraction assisted by microwave from apple pomace using response surface methodology. J. Food Eng. 78, 693e700. Xavier, V.B., Vargas, R.M.F., Cassel, E., Lucas, A.M., Santos, M.A., Mondinc, C.A., Santarem, E.R., Astarita, L.V., Sartor, T., 2011. Mathematical modeling for extraction of essential oil from Baccharis spp. by steam distillation. Ind. 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Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction pez-Malo* Avelina Franco-Vega, Nelly Ramírez-Corona, Enrique Palou, Aurelio Lo rtir, San Andr Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Am ericas Puebla, Sta. Catarina Ma es Cholula, Puebla 72810, Mexico
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 May 2015 Received in revised form 18 September 2015 Accepted 23 September 2015 Available online xxx
Microwave assisted extraction (MAE) is an emerging technique of extraction that improve yields and reduce process time and energy. The aim of this work was to evaluate the effect of different MAE process parameters in the extraction yield of orange peel essential oil (EO), and the description and simulation of this process with mathematical models based on mass transfer fundamentals. For the assessment of process parameters effects on EO yield, different extractions were carried out following a two-level factorial design varying orange peel particle shape (spheres or plaques) and moisture content (dry or not), as well as microwave potency (360 or 540 W). Results demonstrated that particle size, moisture content, and its interaction significantly affected (p < 0.05) the yield obtained and had an influence on the extraction mechanism. Model parameter values associated to mass transfer coefficients (k1 and k2) indicated that diffusion was the process that defined extraction velocity. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Microwave assisted extraction Essential oil Mass transfer Modeling
1. Introduction Citrus are the most cultivated fruits in the world, being oranges 60% of the world fruit production. The orange peel, depending on the orange variety, may represent 45% of the total bulk. Orange peel includes the epidermis covering the exocarp that consists of irregular parenchymatous cells, which encloses numerous glands ~ ez et al., 2013). The oil or oil sacs (Farhat et al., 2011; Vel azquez-Nun in these sacs represents the citrus essential oil (EO) that is obtained as by-product of citrus processing (Bousbia et al., 2009). Besides its use as a flavoring agent, citrus EO has gained relevance in the food industry due to its antimicrobial effect against both bacteria and zquez-Nun ~ ez et al., 2013; fungi (Rezzoug and Louka, 2009; Vela Lago et al., 2014). Typical processes for obtaining essential oils from citrus include cold pressing, solvent extraction, and different distillation techniques (Bica et al., 2011; Lago et al., 2014). However, these procedures involve several disadvantages, such as the use of volatile and hazardous solvents, low yields, long extraction times, and high energy consumption (Zu et al., 2012). The extraction method is one of the prime factors that determine the quality of EOs (Tongnuanchan and Benjakul, 2014); thus
* Corresponding author. pez-Malo). E-mail address: [email protected] (A. Lo
the use of new extraction techniques for natural substances, which typically use less solvent and energy, such as supercritical fluid extraction, ultrasound extraction, microwave assisted extraction, and sub-critical water extraction are being evaluated (Bousbia et al., rino-Issartier et al., 2010). Among these emergent tech2009; Pe nologies, microwave assisted extraction (MAE) has showed many advantages such as convenience, less processing times, and high efficiency (Zhai et al., 2009; Flamini et al., 2007). MAE uses microwave radiation as the source of heating for the solventesample mixture. Due to the particular effects of microwaves on matter (namely dipole rotation and ionic conductance), heating with microwaves is instantaneous and occurs in the interior of the sample, leading to rapid extractions (Camel, 2001). The rinomain advantage of MAE resides in its heating mechanism (Pe Issartier et al., 2010); acceleration of the extraction can be partly explained by the specific effect of microwaves on plant material (Camel, 2001). Mathematical modeling of extraction processing must be considered as a fundamental step during operation of an efficient industrial process (Xavier et al., 2011; Reyes-Jurado et al., 2014). Mathematical models are used to simulate different process scenarios without the need to run a large number of experimental trials and identify the best conditions for the process (Cassel et al., 2009; Rezzoug and Louka, 2009). Different mathematical approaches have been reported that explain the process involved
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Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
2
A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
during traditional methods of extraction. Some authors have described the extraction variables effects on essential oil yield through statistical analysis of experimental data (Ghasemi et al., 2011; Parikh and Desai, 2011; Zhang et al., 2012), being response surface methodology (RSM) successfully applied for optimizing extraction conditions. However, this type of analysis represents parameter values for the optimization process within the experimental values, thereby; it just can be used in the experimental range tested and just represents the evaluated variables effect on analyzed response (Almeida et al., 2008). For this reason, models that try to explain the physical mechanism of the extraction process based on mass transfer fundamentals have been developed (Sovov a and Aleksovski, 2006; Cassel et al., 2009; Moreno et al., 2010; Xavier et al., 2011) with the aim of determining physical parameters that make possible extrapolations. Sovov a and Aleksovski (2006) developed a model for the hydrodistillation process; in their model two different kinds of particles were considered: spherical particles or slabs, with different distribution of the oil in each one. In their assumptions, the only mass transfer process that had to be taken into account was the oil diffusion from the particle core to the region of broken cells, ignoring the initial rate of distillation from intact cells. Similar to and Aleksovski, Xavier et al. (2011) proposed a model based Sovova on mass transfer fundamentals, their model considers a fluidized bed and is based on the concept of broken and intact cells, but it considers the presence of two extraction periods (each one associated with the velocity of EO extraction from cells) for the description of the extraction curve based on mass balance of the solute (EO). Despite that MAE has proven to be a more effective extraction process than traditional methods, to our knowledge, available approaches to describe this process are primarily based in statistical analysis of process variables (Wang et al., 2007; Fang et al., 2010; Farhat et al., 2011; Song et al., 2011), while the study in terms of mass transfer is limited. Since MAE is based in a distillation process with a different source of heating, it should be expected that MAE can be properly modeled by describing the mass transfer mechanisms during distillation, taking into account the microwave effect on the diffusion coefficients. The aim of this work was to evaluate the effect of different MAE process parameters in the extraction yield of orange peel EO, to describe the process through statistical analysis based on a factorial design, and as well as description of this process with a mathematical model based on mass transfer fundamentals.
2.2. Microwave assisted extraction MAE was carried out on a NEOS microwave extraction system (800 W, 60 Hz) (Milestone, Italy), which is designed to utilize different beakers for the extraction of different sample/solvent volumes. The MAE system is equipped with a TFT multicolor liquid crystal screen, a power sensor (power range 0e1000 W), an infrared temperature sensor, a temperature controller, and a magnetic stirrer in the base of the reactor. Fig. 1 displays a schematic description of the hydro-distillation system. During extraction, a sample of 250 g of orange peel and 700 mL of distilled water were placed into the beaker (A), which was introduced and fixed to the MAE system. The sample was then subjected to the extraction process for 50 min at two different irradiation powers (360 or 540 W). A condenser (B) was used to collect the extracted essential oil in a graduated trap (C). Irradiation power and extraction time were controlled from the equipment front panel (D). During the process, system temperature rises until the sample starts to boil and the extraction starts; since each sample was processed at different conditions of moisture content, particle size and microwave power, the time required to achieve the boiling temperature (90e94 C) was also different depending on the characteristics of the sample, such delay time was registered as tCUT. Extracted essential oil was dried with anhydrous sodium sulfate and stored at 4 C in dark vials until used. The amount of essential oil was determined during extraction and gravimetrically after collection; the extraction yield was expressed as the percent ratio
2. Materials and methods 2.1. Plant material The essential oil was extracted from orange (C. sinensis var. Valencia) peel without bagasse obtained from a local juice producer of San Andres Cholula, Puebla (Mexico) during AprileAugust 2014. The peel was used at two moisture levels, high (50% of moisture) and low (10% of moisture), and using two different shapes of particles, plaques (1 cm) and spheres (40 mm). To adjust orange peel moisture content, the peel was dried in a tray dehydrator (Excalibur, USA) for different times at 35 C according to the final target moisture content, the final moisture was determined in a forced air oven at 105 C for 24 h according to the official method reported by A.O.A.C (2000). Peels were ground in a domestic mixer to obtain the orange peel spheres and their particle size was determined with a particle analyzer (Bluewave, Microtrac, USA), while the peel plaques were obtained manually.
Fig. 1. Schematic description of the microwave assisted extraction system. Beaker (A), condenser (B) graduated trap (C), and equipment front panel (D).
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
of the mass of extracted essential oil to the mass of orange peel in dry basis (Li et al., 2012). 2.3. Process parameters effect Two-level factorial designs allow the simultaneous evaluation of a number of factors' effects on a particular process; as well as permit determining possible interactions between (or among) tested factors (Montgomery, 2009). In our case, each MAE process parameter (initial moisture content, particle size, and microwave power) was evaluated at two levels: low (1) and high (þ1). The coded and un-coded values of each factor and their respective levels are presented in Table 1. The evaluated responses were EO extraction yield, as well as other calculated model parameters described in Section 3. Experiments were replicated three times; extraction yields and other calculated responses are reported as average ± standard deviation values (Table 1). Each evaluated response was analyzed by two-level factorial design to fit polynomial equations and determine significant (p < 0.05) individual and interactions effects:
R ¼ b0 þ
X
bi Xi þ
X
bii Xi Xj þ
X
biii Xi Xj Xk þ 2
(2.1)
where, R is the predicted response; X are the independent variables in coded values; b0 is the constant; bi are linear effects; bii are double interaction effects, and biii is the triple interaction effect. The statistical analysis was performed using Minitab 16 (Minitab Inc., State College, PA, USA). The significance of each term in the polynomial model (linear, double, and triple interactions) was tested by the associated ANOVA in the factorial design analysis, as well as the accuracy of the regression model (Montgomery, 2009). 2.4. Physical properties
2013). The superficial area a0 (cm2/g) of the particle-bed was represented by the surface area of the total particles contained in the bed (Chikazawa and Takei, 2006), geometrically calculated assuming that particle size is uniform for both systems, spherical with a diameter of Di and cubic particles for the bed of plaques with a length of li. The average particle diameter (Dm) was determined with a particle analyzer (Bluewave, Microtrac, Montgomeryville, PA).
3. Mathematical modeling For the modeling of orange peel EO microwave assisted extraction, a model that takes into account the influence of the process parameters considered in the factorial design (Section 2.3) was used. The model was proposed by Xavier et al. (2011), which is based on the concept of broken and intact cells and considers two extraction periods, the first one governed by phase-equilibrium, while the second one is limited by internal diffusion in the particles. The first part of the extraction curve is associated to the extraction of the free solute from broken cells. In this extraction step (hypothetical case with negligible external mass transfer resistance), the fluid phase is in equilibrium with solid phase throughout the extractor. The second period is controlled by the solute diffusion from inner cells of vegetal structure and corresponds to the diffusion from intact cells. In Xavier et al. (2011) model, mass transfer properties of recovered oil were considered constant throughout the process. The general equations considered in this model are the mass balances per unit volume extraction bed consist of equations for the solute in fluid and solid-phases described by (eqs. 3.1 and 3.2).
vY vY þ rf u ¼ Jðx; YÞ vt vt
rf ε
The orange peel samples used for the extraction were considered as a particle bed; apparent density, bulk density, porosity, and surface area were determined for each extraction particle-bed in order to satisfy the mathematical model described in Section 3. Particle density (rp) and bulk density (rS) were determined according to Shrestha et al. (2008). Bulk density is defined as the ratio of the mass of the sample-particle bed in a vessel to the volume of the bed (including the voids among primary particles), for its measurement 20 g of orange peel were weighed in a graduated cylinder of 100 mL and the occupied volume was registered. Apparent density was determined using a pycnometer and calculated as reported by Barbosa-C anovas et al. (2006). With the values lez-Ruíz, of rS and rp, the bed void fraction (ε) was calculated (Ve
3
rS ð1 εÞ
(3.1)
vx ¼ Jðx; YÞ vt
(3.2)
Associated fluxes are:
Jðx; YÞ ¼ kf a0 rf Y * Y
for x > xk ;
Jðx; YÞ ¼ kS a0 rS x for x xk ;
first period
(3.3)
second period
(3.4)
According to Xavier et al. (2011), the model solution can be expressed in terms of the maximum value for the extracted oil (M∞), considering the two extraction periods, described as:
Table 1 Two-level factorial design to evaluate the effect of selected microwave assisted extraction factors on orange peel essential oil yield, modeling parameters, and characteristic times of the process. Run
Microwave power (Watts)
C B H G D E F A
540 360 360 540 360 540 360 540
þ1 1 1 þ1 1 þ1 1 þ1
Moisture content (%) 50 10 10 10 50 50 50 10
þ1 1 1 1 þ1 þ1 þ1 1
Particle shape
Sphere Sphere Plaque Plaque Sphere Plaque Plaque Sphere
1 1 þ1 þ1 1 þ1 þ1 1
Yield (% dry basis)
1.56 2.47 0.92 0.99 1.22 0.94 0.98 2.73
± ± ± ± ± ± ± ±
0.13 0.05 0.42 0.15 0.26 0.12 0.10 0.43
k1 (g/min)
0.20 0.45 0.21 0.34 0.22 0.43 0.14 0.39
± ± ± ± ± ± ± ±
0.07 0.28 0.13 0.12 0.05 0.14 0.02 0.14
k2 (min1)
0.10 0.09 0.33 0.29 0.14 0.38 0.38 0.12
± ± ± ± ± ± ± ±
0.01 0.01 0.07 0.02 0.05 0.12 0.09 0.01
tc (min)
12.00 17.75 3.00 12.75 1.50 2.25 2.50 22.75
± ± ± ± ± ± ± ±
tCUT (min)
1.41 2.10 1.41 1.06 0.71 0.35 0.01 3.47
11.25 15.75 13.50 6.50 19.00 10.50 18.75 8.50
± ± ± ± ± ± ± ±
0.35 1.06 0.71 0.71 0.01 0.71 0.35 0.71
ks (g/min*cm2)
2.64 2.21 0.35 0.15 2.93 0.12 0.05 2.77
± ± ± ± ± ± ± ±
0.22 0.15 0.05 0.05 0.02 0.05 0.01 0.17
k1 constant associated to the equilibrium period; k2 constant associated to the diffusion controlled period; tCUT process time to reach an adequate temperature to start sample boiling and begin the oil extraction; tc time at which the change of phases is observed during the extraction process (related to the slope change); and ks solid-phase mass transfer coefficient.
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
Z Mt ¼ M∞
t 0
8 > > k1 t for the first period _ mYdt < M∞ > M∞ > : 1 ek2 t for the second period
(3.5)
where k1 can be also expressed as eq. (3.6), and k2 is related to the mass transfer coefficient as can be seen in eq. (3.7).
_ * k1 ¼ mY k2 ¼
ks a0 ð1 εÞ
(3.6) (3.7)
As can be noticed, the first period is a linear function of time, while the second period follows an exponential expression. Detailed methodology of model development can be found in Xavier et al. (2011). The normalized extraction curves were obtained by plotting the mass fraction of recovered essential oil (Mt/M∞) versus the extraction time. The mass fraction was calculated as the ratio of the EO's mass obtained at time t (Mt) to the EO's mass obtained at the end of the process (M∞). Data were fitted to expressions described in eq. (3.5), and the associated parameters k1 and k2 were determined by means of nonlinear regression. The obtained values of k2 were then used to calculate the mass transfer coefficient ks (eq. (3.7)). Additionally, the time at which the phase change occurs was registered as the point in which the slope changes (tc). Different operation conditions and particle characteristics were evaluated in order to assess the effect of these conditions on the mass transfer mechanisms. 4. Results and discussion 4.1. Effect of process parameters on the efficiency of EO extraction The obtained orange peel EO yields resulting after the proposed MAE process following the two-level factorial design at two microwave powers (360 or 540 W), two initial orange peel moisture content (10 or 50%), and two particle shapes (plaques or spheres) are presented in Table 1, these data are expressed in terms of the oil yield (expressed in dry basis) obtained at the end of each process. In general, extraction yield was higher than 0.9%, achieving in some conditions more than 2.5%. The yields obtained in our study were higher than those reported in the literature; lower yields (0.13%e0.74%) were reported for orange maltaise (Citrus sinensis) by Bourgou et al. (2012) obtained by hydrodistillation and by Strano et al. (2014) for Tarocco orange after three hours of hydrodistillation (2.1% of yield). Rezzoug and Louka (2009) obtained a maximum yield of 2% for orange peel EO by steam distillation but in a process with double the time (100 min) that in our case. Besides the increase in yields achieved by microwave assisted extraction, another of the advantages is its ability to reduce processing times. The greatest yields were obtained when extraction was carried out with spheres of dry orange peel (experiments A and B); at these conditions, no influence of the microwave power was observed with regards to the extraction efficiency (p > 0.05, Table 2). Lower yields were obtained for other evaluated processing conditions. Table 2 presents the obtained coefficients and their probability of being significant after analyzing the two-level factorial design. Good adjustments of eq. (2.1) to the experimental data were verified through the determination coefficients obtained (R2 > 0.85 for the majority of the analyzed responses, except for k1 where R2 was 0.50), representing the proportion of variation in the response
explained by the terms in the model. The results demonstrate the significant effect (p < 0.05) of initial moisture content and particle size on essential oil yield. In the case of particle size, the decrease of it in the sample (spheres) improved extraction (Fig. 2) and this can be related to an increase in the superficial area which promotes a better contact of the sample with the solvent and penetration of and microwaves (Veggi et al., 2012). Different to our results, Sovova Aleksovski (2006) reported that for extraction of thyme oil from the herb Thymus serpyllum by steam distillation, yields for the smallest particles (0.18 mm) were lower than those for large particles; they assumed that part of T. serpyllum oil, easily accessible, was lost from their surface during gridding. Letellier and Budzinski (1999) affirm that a wet matrix (high moisture) improves the extraction recovery in most cases, due to microwaves interacting selectively with the free water molecules present in the gland and vascular systems, leading to rapid heating and temperature increase, followed by rupture of the walls and release of the essential oils into the solvent. Camel (2001) reported that in some cases, drying the matrix before the extraction or adding a drying agent has favored the MAE process. Similar to our results, Moreno et al. (2010) obtained higher yields of extraction using dry samples for the extraction of eucalyptus EO by steam distillation; these authors reported a decrease in yield when the moisture content in the eucalyptus leaf increase from 16.1 to 35.9%. However, these results are associated with a process of higher costs due to the previous processing (drying) step needed before extraction. In our experiments the cost of the process using a trydryer increases, approximately, 10 times the price of using samples with the high moisture content. Microwave power did not affected in EO extraction yield, Farhat et al. (2009) during lavender flowers EO extraction by microwave steam diffusion did not find difference in oil yield when they tested five different microwave powers between 50 and 400 W, observing that the only effect in the process was the time needed for EO extraction. The effect of the interaction between particle size and moisture content observed (Table 2) in the statistical analysis was significant (p < 0.05), but this is not the only interaction that affects the efficacy of EO extraction, since the interaction between particle size and microwave power was also significant (p < 0.05) on EO yield, as can be observed in Fig. 2. Orange peel EO extraction by MAE is favored by the use of samples with low moisture content and with a sphere particle shape. However, these affirmations are only true for a range of levels within the ones tested in this work. Therefore, with the aim to understand the physical mechanism of extraction, a model based on mass transfer was also studied. Due to the relevance that particle size and moisture content have in the process, the model proposed by Xavier et al. (2011) was selected, since the model takes into account both variables. 4.2. Extraction kinetics Fig. 3 presents the evolution of the orange peel EO extraction yield during MAE at selected conditions. Results are presented in terms of the mass fraction of obtained EO at different process times (Mt/M∞). As can be seen in Fig. 3, the extraction process clearly displays an exponential behavior with the presence of two phases. The first phase that corresponds to a rapid increase in the yield, which other authors have associated with an easy accessibility of solvent to the EO (Reverchon et al., 1999; Farhat et al., 2009), and a second phase, almost asymptotic, that defines the end of the process and it is limited by the mass transfer. This kind of behavior in the extraction of EO from plants have been observed not only for microwave extraction, it was found that the presence of the two phases is due to changes in the mass transfer mechanisms and the changes in the surface area from which essential oils evaporates
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A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
5
Table 2 Coefficient values of microwave assisted extraction process parameters (coded) and interactions on orange peel essential oil yield, modeling parameters, and characteristic times of the process. Term
Yield (% dry basis)
k1 (g/min)
k2 (min1)
tc (min)
tCUT (min)
ks (g/min*cm2)
Constant Microwave power (p) Initial moisture content (%M) Particle size (ps) p*%M p*ps %M*ps p*%M*ps R2
1.096* 0.054 0.509* 0.392* 0.016 0.079* 0.344* 0.029 0.93
0.298* 0.043 0.050 0.018 0.025 0.063* 0.055 0.015 0.50
0.229* 0.006 0.021 0.116* 0.004 0.003 0.014 0.013 0.88
9.313* 3.125* 4.750* 4.188* 0.563 0.750* 2.000* 1.937* 0.97
12.969* 3.781* 1.906* 0.656* 0.219 0.031 0.406* 0.094 0.98
1.403* 0.017 0.033 1.235* 0.072* 0.050 0.115* 0.140* 0.99
k1 constant associated to the equilibrium period; k2 constant associated to the diffusion controlled period; tCUT process time to reach an adequate temperature to start the sample boiling and begin the oil extraction; tc time at which the change of phases is observed during the extraction process (related to the slope change); ks solid-phase mass transfer coefficient; and R2 coefficient of determination. *Coefficients with significant effect (p < 0.05) in the evaluated response.
(Reis-Vasco et al., 2000). Hence, the mechanism of the extraction is related to the physical properties of the sample while the energy source utilized for the EO extraction determines the velocity at which each phase appears. Different to our results, Haj Ammar et al. (2013) tested a mathematical model based only in diffusion during extraction of EOs of myrtle (Myrtus communis L.), rosemary (Rosmarinus officinalis L.), or sour orange (Citrus aurantium L.), observing that the calculated values by their model showed deviations with regards to the experimental ones during the first minutes of the process, these deviations are related to the omission of the first phase of the process (the equilibrium) that in Xavier et al. (2011) model, utilized in our work is taken into account. Comparison among the experimental data and the results of the mathematical modeling of MAE EO yield by the model proposed by Xavier et al. (2011) are presented in Fig. 3. Data are plotted without taking into account the time that the process need to reach an adequate temperature to start the sample boiling and begin the oil extraction (tCUT), since such time only affects the total processing time but not parameters estimation. The analysis of the results indicated that, in general, tested mathematical model adequately fitted the experimental data (R2 > 0.90); the model goodness of fit to experimental data is presented in Fig. 4. The poorest fit for the second phase was observed for case E (Table 1), presented in Fig. 2d; this sample has the largest moisture content and largest particle size, and as described in Table 2 it also corresponds to the larger variability data for k2. In the different curves displayed in Fig. 2, it can be seen that the maximum oil extracted was reached after a time varying between 18 and 40 min, being faster extractions realized at 540 W of microwave power (Fig. 2). Microwave power did not have a significant effect (p > 0.05) on yield (Table 2). However, the effect observed (Fig. 3) in the time needed to reach the maximum yield, it would seem that the use of high power can reduce process time and probably costs; yet, it is necessary to take into account that microwave power is important to ensure the essential oil is extracted quickly; but, the power should not be too high, since higher powers promote a faster temperature rise, such that in some cases the cooling system is not able to condensate all vapor at the same rate and it may result in loss of volatile compounds (Camel, 2001). In Table 1, obtained values for the parameters k1 and k2 (associated with extraction phases) are presented, as well as the values for the time that the processes need to reach an adequate temperature to start the sample boiling and begin the oil extraction (tCUT), the time in which the change of phases is observed during the extraction process, related to the slope change (tc) and the solid-phase mass transfer coefficient for each experiment (ks). Reverchon et al. (1999) suggested that in the first phase of the
extraction curve, the oil is freely available for extraction; this linear part of the extraction process (also called the equilibrium phase) is represented by k1. Observing the curves of extraction; we expected that the equilibrium phase vary according to the physical properties of the sample, experiments E and F (which have the same initial moisture content and particle shape) the slope of this first part of the curve seemed to be higher than in other conditions and this could be related with a higher process velocity in the equilibrium phase; however, particle size and initial moisture content of the sample did not affect (p > 0.05) k1 (Table 2), but the interaction (Fig. 2) of particle size with microwave power had a significant effect (p < 0.05) on k1. These results indicate that the amount of easily accessible EO in orange peel is the same at any condition of the sample, although the interaction between particle size with microwave may enhance the velocity at which the process begins (Fig. 2), and that the only mass transfer process that affects the oil extraction rate is the EO diffusion from the particle core to the region of broken cells (Sovov a and Aleksovski, 2006; Wang and Weller, 2006), which is promoted at higher microwave power levels. The parameter k2 is related with the diffusion of the EO from the inner part of the sample cells, hence with the internal diffusion in the particles. In contrast to k1, k2 was significantly affected (p < 0.05) by particle size (Table 2); hence, the solid-phase mass transfer coefficient (ks) was determined from k2 combined with sample physical properties. The mass transfer coefficient can be determined if the specific surface area is known. The values for ks were determined and are also presented in Table 1; it can be seen that the overall mass transfer coefficient obtained for experiments with plaque shape were lower than the obtained in experiments using a sphere shape (Fig. 2). These results confirm that reducing the particle size of the sample facilitates diffusion (and extraction) of the EO, because it permits increasing surface contact to occur between the plant material and the solvent, therefore facilitating essential oil extraction processes (Haj Ammar et al., 2013). However, the values of ks obtained are low compared with the values obtained by Farhat et al. (2009) for the diffusion of lavender flowers EO during microwave steam diffusion (MSD). In their work they used different particle sizes, but the solvent was steam, thus they assumed that the enhancement in the mass transfer observed during MSD can be due that in MAE all the microwave energy is mainly absorbed by water for heating and vaporization, and only a fraction is absorbed by the essential oils inside the sample. Another explanation for the effect of the initial moisture content observed in the diffusion rates, that can be associated with the presence of higher porosity in the bed of dry samples than in the bed of wet samples, which also promotes the contact of the solvent with the
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A. Franco-Vega et al. / Journal of Food Engineering xxx (2015) 1e8
360
540
360
2.0
2.0
1.5
1.5
Y 1.0 (%)
Y 1.0 (%)
0.5
0.5
0.0
0.0 0%
20%
40%
60%
plaque
IniƟal moisture content
360
sphe r e ParƟcle size
540
360
0.5
0.5
0.4
0.4
0.3
540
0.3
k1
k1
0.2
0.2
0.1
0.1
0.0
0.0
0%
20%
40%
60%
plaque
IniƟal moisture content
360
sphe r e ParƟcle size
540
360
0.4
0.4
0.3
0.3
k2 0.2
k2 0.2
0.1
0.1
0.0
540
0.0 0%
20%
40%
60%
plaque
IniƟal moisture content
360
ks
540
sphe r e ParƟcle size
540
360
2.5
2.5
2.0
2.0
1.5
ks
1.5
1.0
1.0
0.5
0.5
0.0
540
0.0 0%
20%
40%
60%
IniƟal moisture content
plaque
sphe r e ParƟcle size
Fig. 2. Interaction plots (each plot displays the interaction between initial moisture content or particle size and microwave power (360 or 540 W) during microwave assisted extraction) on orange peel essential oil yield (Y, % dry basis), and selected modeling parameters: k1 (constant associated to the equilibrium period, g/min), k2 (constant associated to the diffusion controlled period, min1), and ks (solid-phase mass transfer coefficient, g/min*cm2).
particle surface (Eggers and Pliz, 2011). As mentioned above, the microwave power utilized in the process affected only the time of extraction, the time needed for the start of EO extraction (tCUT) (Table 1) shows that the processes in which the microwave power was 360 W needed more time for EO extraction than that required at 540 W. In addition, microwave
power, particle size, initial moisture content, and the interaction between moisture content and particle size also had a significant effect (p < 0.05) in tCUT (Table 2), being lower in samples with high initial moisture contents (50%) than for dry samples (10%). Due to tCUT can be associated with the cost of the process, the best conditions to reduce this time were using a sample with low initial
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
1.0
1.0
0.8
0.8
0.6
0.6
Mt/M∞
Mt/M∞
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0.4
(a)
0.2
0.4
(b)
0.2 0.0
0.0 0
10
20 me (min)
30
0
40
1.0
1.0
0.8
0.8 (Mt/M∞)
Mt/M∞
7
0.6 0.4 0.2
10
20
me (min)
30
0.6 0.4 0.2
(c)
40
(d)
0.0
0.0 0
10
20
30
40
0
10
me (min)
20 me (min)
30
40
Fig. 3. Extraction kinetics of orange peel essential oil during microwave assisted extraction at 540 W and selected moisture conditions and particle size: (a) sphere with 10% moisture content, (b) sphere with 50% moisture content, (c) plaque with 10% moisture content, or (d) plaque with 50% moisture content. Experimental data (A) and mathematical model fit (phase 1ee, phase 2 ).
sample was dry and with a sphere shape (A and B), indicating that in processes with samples at lower initial moisture content and higher surface area the phase of mass transfer in solid phase takes more time to start.
5. Conclusions
Fig. 4. Experimental data vs predicted values for different operation conditions.
moisture content (10%) in a MAE process at 540 W. This can be explained by the fact that at the start of the extraction, rate is effectively limited by the solubility of the oil into the fluid phase and this situation continues until the depletion of oil from the lower portion of the bed has reduced the effective bed height to the point where the fluid is no longer saturated when it leaves the top of the bed (Reverchon et al., 1999). Finally, the time of the process for the change of the equilibrium phase to the mass transfer represented by tc was significantly affected (p < 0.05) by microwave power, moisture content, and particle size, by the interactions between moisture content and particle shape and microwave power and particle size, as well as by the triple interaction of process factors, presenting higher values of tc the processes where the
The evaluated parameters during microwave assisted extraction of orange peel essential oil had a significant effect in the extraction process, demonstrating that the conditions of the sample (particle size and initial moisture content) determine the extraction yield, while microwave power affects the time of extraction. The model based on mass transfer process utilized to predict the physical mechanism of extraction showed to be a satisfactory approach to the mathematical representation of the extraction of orange peel essential oil by microwave assisted extraction, being capable to describe the two observed phases during the extraction processes. According to the parameters of the model (k1 and k2, associated to fluid-phase mass transfer coefficient and solid-phase mass transfer coefficient, respectively) the mass transfer in solid phase was the only phase significantly (p < 0.05) affected by the characteristics of the sample, indicating that in orange peel essential oil extraction by microwave assisted extraction the solute diffusion from the inner cells is the process that defines the extraction velocity.
Acknowledgments Financial support for the project 180748 from the National Council for Science and Technology (CONACyT) of Mexico and ricas Puebla (UDLAP) is gratefully Universidad de las Ame acknowledged. Author Franco-Vega acknowledges financial support for her PhD studies in Food Science from CONACyT and UDLAP.
Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025
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Nomenclature tCUT
rp Ɛ a0
Di Li Dm
rf rs
u J kf ks Y* Y x xk k1 k2 ṁ t Mt M∞ R
bi Xi 2
Time at which the essential oil extraction starts, min Particle density, g/cm3 Porosity (bed void fraction), dimensionless Superficial area, (cm2/g) (specific surface area per unit volume of extraction bed (m2/m3) Diameter of sphere particles, cm Length of plaque particles, cm Average particle diameter, cm Solvent density, g/cm3 Solid density (bulk density), g/cm3 Superficial fluid velocity, cm/s Flux of solute, g/cm3 s Fluid phase mass transfer coefficient, m/s Solid phase mass transfer coefficient, m/s Equilibrium fluid phase mass fraction, g/g Mass fraction in fluid phase, g/g Mass fraction in solid phase, g/g Easily accessible solute in solid phase, g/g Constant associated to the equilibrium period, g/min Constant associated to the diffusion controlled period, 1/ min Solvent flow rate, g/s Extraction time, min Mass of extract at time t, g Maximum value of the mass extracted, g Predicted response from the factorial design Coefficients from the polynomial model from the factorial design Independent factors in the factorial design Error
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Please cite this article in press as: Franco-Vega, A., et al., Estimation of mass transfer coefficients of the extraction process of essential oil from orange peel using microwave assisted extraction, Journal of Food Engineering (2015), http://dx.doi.org/10.1016/j.jfoodeng.2015.09.025