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Study of heating characteristics for a continuous 915 MHz pilot scale microwave thawing system
Food Control 104 (2019) 105–114
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Study of heating characteristics for a continuous 915 MHz pilot scale microwave thawing system
T
Roujia Zhanga,b, Yifen Wangc,∗, Xichang Wangb, Donglei Luana,b,∗∗ a
Engineering Research Center of Food Thermal-processing Technology, Shanghai Ocean University, Shanghai, 201306, China College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China c Biosystems Engineering Department, Auburn University, Auburn, AL, 36849, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Microwave thawing Numerical simulation Surimi Edge heating Volume percentage
Heating uniformity is the critical control point on quality and safety during microwave thawing process. The objective of this study was to investigate heating characteristics of a continuous 915 MHz pilot scale microwave thawing system with a numerical simulation model and experiments. The microwave frequency and physical properties of frozen surimi were measured to provide essential information for the numerical simulation model. To validate the developed model, infrared camera and fiber optic sensors were utilized to capture the heating pattern and record the time-temperature profiles of the processed surimi. Volume percentages of the obtained temperature data for both simulated and experimental results were calculated and plotted into curves to intuitively show the heating characteristics. Results showed that the operating frequency of the thawing system was changing with time and affected by presence of load. Simulation results showed that the overall heating pattern was not sensitive to frequency settings. However, different frequency settings brought big difference of local temperature which implied varying microwave energy concentration at different frequencies. Experimental results showed good repeatability and agreed well to simulation results in heating pattern but not in temperature profiles due to varying frequencies. Simulation models with combined frequencies were recommended to improve temperature accuracy. Sample distance and size for continuously moving surimi slabs were studied using developed simulation model. It illustrated that smaller sample distance reduced the edge heating in moving direction. And a larger size of food in moving direction brought better heating uniformity.
1. Introduction Thawing process is becoming more and more significant in food industry along with the wide application of freezing technique in food preservation. Traditional thawing methods, such as air thawing or cold water thawing, require a very long time to achieve it due to low thermal conductivity of frozen food which restricts its heating rate. Thus, the quality and safety of thawed products may decrease a lot caused by drip losses and microbial spoilage. Microwave (MW) heating, in comparison, generates heat throughout the whole volume of frozen food which has the potential to significantly reduce thawing time and improve products’ quality (Arocas, Sanz, Maria Isabel, & Fiszman, 2011; Datta & Davidson, 2000; Tang, 2015; Villarreal-García et al., 2015; Wen, Hu, Zhao, Peng, & Ni, 2015). However, non-uniform heating is the major problem of microwave heating applied in food industry (Soto-Reyes, Temis-Pérez, López-Malo, Rojas-Laguna, & Sosa-Morales, 2015). The
∗
non-uniform heating is caused by uneven electric field distribution within food (Fakhouri & Ramaswamy, 1993; Huang et al., 2016, 2018; Llave, Mori, Kambayashi, Fukuoka, & Sakai, 2016; Ozturk, Kong, Singh, Kuzy, & Li, 2017; Tiwari, Wang, Tang, & Birla, 2011). The electric field distribution is affected by lots of factors such as shape and size of the microwave heating cavity, and physical properties of food. Furthermore, the heat generated within food is proportional to the square of the electric field intensity. Thus, non-uniform heating would be severe when high microwave power was applied, which has restricted the industrial application of MW heating (Luan, Tang, Pedrow, Liu, & Tang, 2016). For a thawing process, microwave heating is more complicated than a common heating process due to the big difference of dielectric properties of food between frozen and thawed statuses. The dielectric loss factor of frozen food sharply increase after thawed (Sosa-Morales, Valerio-Junco, López-Malo, & García, 2010). Thus, during MW thawing,
Corresponding author. Corresponding author. Engineering Research Center of Food Thermal-processing Technology, Shanghai Ocean University, Shanghai, 201306, China. E-mail addresses: [email protected] (Y. Wang), [email protected] (D. Luan).
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https://doi.org/10.1016/j.foodcont.2019.04.030 Received 2 February 2019; Received in revised form 25 April 2019; Accepted 26 April 2019 Available online 27 April 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
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some hot areas that melt first would absorb much more MW energy than the cold areas which results in thermal runaway and aggravates the non-uniform heating (Regier, Knoerzer, & Schubert, 2016, pp. 65–99). Therefore a uniform electric field distribution is the key point for an industrial microwave thawing process. To improve heating uniformity, the typical technique is to continually change the electric field within the microwave heating cavity using multiple feed points and mode stirrers (Kurniawan, Alapati, & Che, 2015; James, James, & Purnell, 2017). And, theoretically, the probability of microwave energy located at each position is equal, which may lead to a uniform heating pattern. However, the presence of food also affects electric field distribution which disturbs the uniform field pattern. Furthermore, edge heating caused by dielectric constant difference between food and surrounding air is another factor lead to non-uniform heating. Although, many companies have claimed that their microwave thawing system was successfully commercialized, few data are reported. Compared with freezing system, the controlled thawing systems are relatively few in commercial application (James et al., 2017) which can be attributed to lack of basic information of heating characteristic within these systems. Microwave heating is a complex process especially for thawing purpose, which is affected by many factors such as microwave frequency, phase change, dielectric properties, position, size and shape of the food material, and design and geometric parameters of the MW system (Lee, Choi, Kim, & Jun, 2015; Picouet, Fernández, Serra, Sunol, & Arnau, 2007; Raj, Birla, Pitchai, Subbiah, & Jones, 2011; Ryynänen & Ohlsson, 1996; Zhang, Lyng, & Brunton, 2007). Therefore, traditional trial-and-error experimental method is time consuming and expensive for MW heating process study. With the development of computer technology, numerical simulation is playing an important role in assisting studies on MW heating and system development (Geedipalli, Rakesh, & Datta, 2007; Knoerzer, Regier, & Schubert, 2008; Liu, Ogiwara, Fukuoka, & Sakai, 2014; Pitchai et al., 2014; Zhu, Kuznetsov, & Sandeep, 2007). The objectives of this study were to develop a numerical simulation model of a self-developed continuous 915 MHz pilot scale microwave thawing system and verify the reliability of the with real heating experiments using an infrared camera and fiber-optic thermal sensors; investigate heating characteristic of the microwave thawing system through the verified simulation model; and analyze the effect of microwave frequency and food conditions (layout, sample distance) on heating pattern to provide useful information for industrial scale microwave thawing system design.
made of polymer which was also transparent to microwaves. Two MW suppressors were arranged at the two end of the heating cavity to prevent MW leakage.
2. Materials and methods
2.1.2. Numerical parameter setting A numerical simulation model was developed based on the MW thawing system using a piece of commercial software QuickWave version 7.5 (QW3D, Warsaw, Poland). The finite difference time domain (FDTD) method was used to numerically solve the coupled electromagnetic and heat transfer equations during MW heating. The detailed governing equations were described in a study by Luan, Tang, Pedrow, Liu, and Tang (2013). Following the rule that the cell size should be less than one tenth of a wavelength (Rattanadecho, 2006), the mesh size was set as 5 × 5 × 12 mm3 in air and 5 × 5 × 2 mm3 in food. One hundred and sixty discrete moving steps were applied to simulate the continuously moving process. Each moving step had a length of 12.5 mm and a heating time of 1 s to match the continuously moving speed of 0.0125 m s−1 in experimental test. Sensitivity studies were performed for mesh size and moving step to balance the computation time and accuracy. The metal wall of the MW heating system was assumed as perfect electric conductor (PEC). A sinusoidal MW source with transverse electromagnetic (TE10) mode (dominant mode of the rectangular waveguide) was set at the MW input port. A net input power was set as 5 kW based on the recorded input and reflected power of the generator. The frequency was set based on actually measured operating frequencies. In this study, frozen surimi (with size of 300 × 200 × 50 mm3 and weight of 3.5 kg) was used as the food sample to verify the heating characteristics obtained from the numerical simulation model. An initial temperature of −18.0 °C for the frozen surimi was set, which was the temperature in common practice of cold storage. The temperature of surrounding air, conveyor belt, and the metal wall were all set as 20.0 °C (room temperature). The measured apparent specific heat and dielectric properties of food sample were saved in a separated file and updated with changing temperatures during the numerical simulation. Reported thermal conductivity values of surimi were used in the simulation (Abudagga & Kolbe, 1997). Sensitivity study was performed for thermal conductivity and proved that it had no effect on heating pattern. Chamchong and Datta (1999) found that the convective heat transfer coefficient values from 0.0001 to 30 W m−2·°C−1 did not impact heating time in microwave heating. Therefore, we selected a reported value of 20 W m−2·°C−1 as the convective heat transfer coefficient between the air and the frozen food (Pitchai et al., 2015). The dielectric loss factor of air was set as 0.
2.1. Numerical simulation model
2.2. Physical properties measurement of MW system and surimi
2.1.1. Development of MW thawing system A pilot scale 915 MHz microwave thawing system was developed in our laboratory to study heating characteristic of continuously moving thawing processes and collect useful information for industrial scale system design. The pilot scale 915 MHz microwave thawing system consisted of waveguides, a top horn applicator, a MW heating cavity, a conveyor belt and two 915 MHz MW suppressors (Fig. 1). In this system, the operating frequency of the magnetron (Sanle, Nanjing, China) that generated the MW energy was within 915 ± 25 MHz. Several standard WR975 rectangular waveguide elements (the inner cross sectional dimension was 247.7 × 123.8 mm2) were used to transmit microwaves from the generator to the horn applicator. The horn applicator was a tapered shape parallelogram with narrow end connected to the WR975 waveguide and wide end connected to the heating cavity. A window made of high temperature resistant polymer (Ultem® 1000) was installed between the horn applicator and the cavity (Fig. 1). Microwaves could go through the Ultem window but dust or steam within the cavity could not go into the horn applicator and waveguides. The frozen food sample was transported through the heating cavity by a conveyor belt
A 2650A spectrum analyzer and a matched M401 antenna (B&K Precision, Yorba Linda, Calif., U.S.A) were used to measure varying operating frequencies of the generator. The central frequency of the spectrum analyzer was set as 915 MHz with a span of 50 MHz. The microwave signals within 915 ± 25 MHz were recorded every 0.1 MHz. As a result, each frequency profile had 501 data points. Since the operating frequencies of the generator is changing with time, snapshots were taken every 5–6 s to collect the frequency information within 3 min. The frozen surimi was made from Pacific whiting. Surimi was refined fish myofibrillar proteins produced through step-by-step processes including heading, gutting, filleting, deboning, washing, dewatering, refining, mixing with cryoprotectants, and freezing (Park, 2013). Therefore, frozen surimi was a homogeneous food product and could act as stable samples for MW thawing experiments. The moisture, crude protein and fat contents were measured in triplicate following AOAC methods (2005). A differential scanning calorimeter (DSC) (Q2000, TA instruments, CA, USA) was used to measure the specific heat of the surimi at different temperatures. 106
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Fig. 1. The continuously moving 915 MHz plot scale microwave heating system.
The principle of a DSC measurement is comparing the heat flow rate into a sample with that into a standard material when both are subjected to the same linear heating rate. During thawing process, more energy is required for the latent heat of phase change besides sensible heat for temperature increase. However, due to the complexity, so far, commercial computer simulation software did not have any function to directly handle latent heat of a material. Thus, in common numerical simulation models including thawing process, an apparent specific heat was usually applied to combine the latent heat of phase change and sensible heat together as one effective parameter (Erdogdu, Altin, Marra, & Bedane, 2017; Wang & Kolbe, 1991; Taher & Farid, 2001). To determine the apparent specific heat of surimi by DSC, heat flux from the surimi sample was compared with that of a sapphire with known specific heat. Each surimi sample of 20–22 mg weighed on a microbalance (ME204E, Mettler) was sealed in an aluminum pan to perform measurement. The surimi and sapphire were heated in the DSC at a rate of 10 °C/min over a temperature range of −20.0–100.0 °C. For frozen sample, the temperature was controlled by liquid nitrogen. Two heat flux curves of the surimi and sapphire were obtained. Then heat flux data were extracted with 1.0 °C increment to calculate the apparent specific heat of surimi (CP, kJ·kg−1·°C−1) using Eq. (1) (Ferrer, Barreneche, Solé, Martorell, & Cabeza, 2017). This measurement was carried out in triplicates and the average value was used in numerical simulation later on.
CP = CPcal⋅
mcal QS mQcal
programmable circulator and a sample holder. The sample holder (Wang, Wig, Tang, & Hallberg, 2003) made of stainless steel was filled with surimi. And the surimi temperature was adjusted and maintained by circulating a liquid of ethylene glycol and water (V:V = 9:1) through the liquid jacket of the sample holder. A thermal couple was used to measure the central temperature of the surimi to ensure that it reached the required temperature. The dielectric properties were measured from the temperature of −20.0 °C to 100.0 °C with a general interval of 10.0 °C. However, within the phase change temperature range from −5.0 to 0.0 °C, a smaller temperature interval of 1.0 °C was applied. Each measurement covered a frequency range from 500 to 3000 MHz with an interval of 5 MHz which included the frequency points of 915 MHz and 2450 MHz. The measurement was carried out in triplicate. 2.3. Validation experiments To validate the developed numerical simulation model, the frozen surimi sample was processed by the continuous 915 MHz plot scale MW thawing system. The surimi with weight of 3.5 kg was placed on a tray (size of 380 × 260 × 25 mm3) on the moving conveyor belt. For each validating test, a surimi slab moved through the microwave heating cavity on the convey belt with a speed of 0.0125 m s−1 with an overall heating time of 160 s and microwave power of 5 kW. The temperature distribution at top, middle and bottom layers of the surimi slab were captured by an infrared camera (FLIR, Wilsonville, Oregon, USA) immediately after the thawing process. In order to obtain the temperature distribution at the middle layer, the frozen surimi was cut into two pieces along thickness direction and separately stored at −18 °C before thawing process. These two pieces were placed together as a whole surimi slab to perform the test. Besides infrared camera images, a fiber optic temperature measuring system (Ptsensor, Xi'an, Shanxi, China) was used to measure real time-temperature profile of the frozen surimi
(1)
Where CPcal is the specific heat of sapphire (kJ·kg−1·°C−1), mcal is the mass of sapphire (mg), m is the mass of surimi sample (mg), Qs is the surimi heat flux (mW), Qcal is the sapphire heat flux (mW). The dielectric properties of surimi were measured using a dielectric properties measurement system which consisted of a network analyzer (E5071C, Agilent Technologies), a high-temperature probe, a 107
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CP (Ta) = P (T ≤ Ta)
(3)
The curve of cumulative volume percentage could provide an intuitive approach of heating uniformity by showing the whole information of temperature distribution. Theoretically all the statistical values of maximum, minimum, mean and standard deviation are the estimation values of the distribution function. However, the cumulative distribution curve was plotted in this study instead of calculating the estimating values. Although no regression was made to fit the distribution function, the cumulative distribution curve showed all the statistical information visually. To make comparison with the real time-temperature profiles recorded by the fiber optic sensors, 11 time-temperature profiles of simulation results in the corresponding locations were also extracted from 160 temperature matrices. Within a temperature matrix, 27 cells adjacent to each location (shown in Fig. 2) were applied to calculate an average temperature. To evaluate the difference of time-temperature profiles between simulated and validating results, the root mean square error (RMSE) at each location was calculated using Eq. (4). Fig. 2. Location of the fiber-optic sensors in frozen surimi slab (all sensors inserted 25 mm from the top surface.
RMSE =
during MW heating (Wang et al., 2003b). Eleven points (as shown in Fig. 2) of surimi were selected to record the real time-temperature profiles. A 2 mm drill bit was used to drill holes with 25 mm depth from the top of the frozen surimi slab at each location. Eleven fiber-optic sensors were then inserted into holes and sealed with septum tapes. Temperatures were measured and recorded every 1 s during the MW heating process. Both temperature distribution and real time-temperature profile were carried out in triplicate.
2
[Treal (i) − Tpredicition (i)]
ni − 1
(4)
3. Results and discussion 3.1. Thermal and dielectric properties of surimi 3.1.1. Thermal properties The surimi used in this study had 74.0 ± 0.9% moisture content, 13.9 ± 0.5% crude protein and 1.5 ± 0.3% fat. The apparent specific heats of surimi at different temperatures are shown in Fig. 3. At −20.0 °C, the apparent specific heat was 1.88 kJ kg−1·°C−1. It then increased slightly to 3.3 kJ kg−1·°C−1 at −10.0 °C. With temperature rising from −10.0 to −1.0 °C, the apparent specific heat sharply increased to the peak value of 12.6 kJ kg−1·°C−1, and then sharply decreased to 3.6 kJ kg−1·°C−1 at 10.0 °C. This apparent specific heat curve with a large peak around the transition temperature revealed the phase change energy of surimi from frozen to thawed state. From 10.0 to 25.0 °C, the apparent specific heat of surimi decreased slightly to 2.3 kJ kg−1·°C−1 and then slightly increased to 2.5 kJ kg−1·°C−1 from 25 to 100.0 °C. Similar trend was reported by Chen et al. (2013) for whey protein gel and mashed potato. The peak values were 59.6 kJ kg−1·°C−1 at 2.9 °C for whey protein gel, and 32.8 kJ kg−1·°C−1 at 1.6 °C for mashed potato, and after the phase change of melting, the apparent specific heat steadied at around 3.5 kJ kg−1·°C−1. This is similar to the reported data of thawed surimi measured at 25 °C (Abudagga & Kolbe, 1997).
Heating patterns were developed from both numerical simulation and infrared camera snapshot. In numerical simulation, a 3D-dimention temperature matrix was obtained at the end of the calculation for each moving step. Thus, the simulated heating patterns at each layer could be drawn out from the final temperature matrix. A proper color bar was selected to obtain a clear heating pattern. The temperature distribution of a surimi slab measured by infrared camera was converted to a heating pattern by using the same color bar as the simulation result did. Besides heating pattern, the temperature data acquired from numerical simulation and infrared camera were statistically analyzed to calculate the volume percentage (P) density within different temperature ranges. Volume percentage density is the first time utilized to evaluate the temperature distribution in the whole volume of MW processed food samples. It is a probability density of the volume percent at each temperature range. Similar statistical treatment was used by Wang, Yue, Tang, and Chen (2005), in which study probability density frequency of walnut temperatures was used to fit normal distribution and obtain the distribution parameters of mean value and standard deviation. In this study, the volume percentage density was used to replace temperature frequency by taking into account the cell size. The volume percentage within a temperature range was calculated using Eq. (2).
3.1.2. Dielectric properties Dielectric properties are the most important parameters of being heated materials in microwave heating. The dielectric constants of surimi at frequencies of 915 and 2450 MHz are shown in Fig. 4(a). The trends of these two curves were similar to each other over temperatures. When temperature increased from −20.0 to −10.0 °C, dielectric constants of surimi increased slightly. A sharp increase of dielectric constant was observed from −10.0 to 0.0 °C, this might be attributed to a phase change of ice to water. At 0.0 °C, the dielectric constant was 59.5 at 915 MHz and 54.3 at 2450 MHz. When temperature increased from 0.0 to 100.0 °C, the dielectric constant at both frequencies decreased slightly. The dielectric loss factors of surimi at frequencies of 915 and 2450 MHz are shown in Fig. 4(b). At −20.0 °C, the surimi had a relatively low dielectric loss factor for both frequencies. The dielectric
n
∑i = 1 Vi V
n
∑
where n = 160 is the total number of measuring points and moving steps in simulation, i is the data node number, Treal is the averaged real time-temperature profile of three validating tests and Tprediction is the simulated time-temperature profile.
2.4. Data analysis
P (Ta < T < Tb) =
1 n
(2)
Where n is the number of all the cells within the temperature range of Ta to Tb, Vi is the volume of a cell that with temperature belong to this range, V is the whole volume of the food sample. Volume percentage(P) gives the probability distribution of each temperature range. In statistics, cumulative probability is more common in evaluated uniformity of a set of data. Cumulative volume percentage (CP) at a temperature is the summation of each volume percentage that with temperature lower than it: 108
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Fig. 3. Apparent specific heat of surimi for (a) from −20 °C to 100 °C, (b) the detail data nearby the transition temperature.
loss factors increased slightly when temperature reached −10.0 °C, then increased sharply till 0.0 °C. But above 0.0 °C, the dielectric loss factor at 915 MHz increased first and then decreased slightly with increasing temperature. At 2450 MHz, the dielectric loss factor decreased with increasing temperature. This was similar with the trend reported by previous studies (Chen, Warning, Datta, & Chen, 2016). Bedane, Chen, Marra, and Wang (2017) measured the dielectric properties of lean beef within temperature range from −15 to 65 °C. In the thawing region, significant increase of dielectric constant and loss factor was observed. Chen et al. (2013) reported that as the food material thawed, the change in dielectric properties was massive. At 2450 MHz, the dielectric properties of whey protein gel and mashed potato increased rapidly from −20.0 to 0.0 °C, the dielectric constant linearly decreased from 0.0 to 100.0 °C, while dielectric loss factor decreased first and then, linearly increased. Wang et al. (2003 a) investigated the dielectric properties of four kinds of different food materials over a temperature range from 20.0 to 121.1 °C. It was reported that the dielectric constant of all food materials decreased and the dielectric loss factor increased mildly at 915 MHz. The same trend was also observed in this study. The trends of dielectric loss factor were different between 915 and 2450 MHz above 0.0 °C, the increase in the dielectric loss factor is mainly attributed to the increase in ionic conduction with increasing temperature at 915 MHz (Wang et al., 2003 a). This result indicated that the heating rate would be different at these two microwave frequencies.
3.2. Heating patterns and time-temperature profiles 3.2.1. Frequency measurement Microwave frequency is relevant to its wavelength which has significant influence on the electric field distribution and heating characteristics of a microwave heating system. It is an essential information for cavity design and simulation model development. Operating frequencies for empty and loaded cavity were measured with a 2650A spectrum analyzer. For each measurement, 20–30 snapshots were captured. Fig. 5 shows the operating frequency distributions for empty and loaded cavity by plotting the measured snapshots in one graph, which clearly displayed the frequency distribution that varying with time. Results showed that the operating frequency of empty cavity clustered around 890, 900 and 905–910 MHz. However, with presence of moving load the frequency concentrated around 890, 900–905 MHz and occasionally arose at 910 MHz. To study the frequency effect on heating pattern, simulation models with frequency settings of 890, 900, 902.5, 905, 910 MHz were performed, respectively. 3.2.2. Heating pattern, volume percentage and RMSE The simulated heating pattern at different frequencies and heating pattern obtained from experimental validating test are shown in Fig. 6 with the same color bar from −5 to 25 °C. In triplicate experimental tests, the heating patterns of the surimi at different layers were highly consistent which indicated that the heating pattern in the MW heating
Fig. 4. Dielectric properties of surimi at frequencies of 915 and 2450 MHz (a) dielectric constants, (b) dielectric loss factors. 109
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Table 1 Statistical analysis of simulated and experimental results.
890 MHz 900 MHz 902.5 MHz 905 MHz 910 MHz Test 1 Test 2 Test 3
Tmax (°C)
Tmin (°C)
Average
Std
CP(20 °C)
T (95%) = [T1 T2]
28.6 44.9 41.7 31.8 43.2 50.64 41.1 49.84
−5.2 −4.3 −4.3 −5.1 −4.8 −6.22 −5.1 −4.3
1.8 6.7 5.8 3.7 3.4 1.9 2.1 3.6
5.5 8.7 7.7 5.8 7.9 6.01 5.7 6.8
0.9943 0.9049 0.9255 0.9831 0.9444 0.9708 0.9698 0.9608
[-4.4 [-2.9 [-3.0 [-3.6 [-3.8 [-5.3 [-4.1 [-2.7
16.9] 27.3] 25.7] 19.0] 29.8] 21.8] 21.8] 26.1]
CP(20 °C) was the percentage of the volume that with temperatures lower than 20 °C, T(95%) refers to the middle temperature range that covered 95% whole volume.
cavity center, which lead to asymmetric heating at these two edges. To prove this deduction, a simulation with opposite moving direction was performed and the heating pattern results (900 MHz opposite direction) was shown in Fig. 6. Results showed that simulation results for opposite moving direction brought a swap heating pattern for left and right side. Heating pattern only displayed the overall temperature distribution, to obtain more specific information of temperature distribution in statistics and evaluate heating uniformity, the temperature data of each cell in simulation model and data of infrared results were analyzed. Table 1 shows the statistical results. Besides the common statistical values of maximum, minimum, average, and standard deviation, two more indexes based on volume percentage was calculated. The CP (20 °C)was the cumulative volume percentage of 20 °C (Eq. (3)). And T (95%) refers to the temperature range that covered 95% whole volume, which excluded the 2.5% volume that had lowest temperatures and the 2.5% volume had highest temperatures, i.e. CP(T1) = 0.025 and CP (T2) = 0.975. Results showed that simulation models with different frequencies had different average temperature which indicated they absorbed different microwave energy. At 900 MHz the surimi slab was heated most and then 902.5, 905, 910 and 890 MHz in order to reduce. This implied that microwave energy was concentrated more around the frequency range of 900–905 MHz. However, high power microwave heating process brought high standard deviation and Tmax, which implied poor
Fig. 5. Operating frequency distribution of empty (A) and loaded (B) microwave heating cavity.
system was stable and repeatable. Heating patterns obtained from numerical simulation agreed well with the validating ones produced by the infrared camera. Results from both simulation and validating tests showed that hot spots domain located at the two edges in x direction (moving direction) and the cold domain located within the middle area. At different operating frequencies, the heating patterns were similar in the whole with little difference in local domain. Furthermore, the hot spot domain at two edges of the surimi slab was not exactly the same. Take the heating pattern at 900 MHz for an example, the right edge had a higher temperature and relative larger hot area, which lead to the cold area was near to the left edge. That's because the surimi moved from the right side to the left side of the cavity. Although, the two edges experienced the same electric field environments in sequence, the left edge was heated more when it was moving in and the right side was heated more when it was moving out. However, the surimi had a higher temperature and higher dielectric loss factor after moving though the
Fig. 6. Heating pattern of (a) simulation results and (b) validating tests results by an infrared camera, Test 1, Test 2 and Test 3 are experiment tests in triplication. 110
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Fig. 7. Cumulative volume percentage of (a) simulated and (b) experimental results.
stable in a small range from 0.9 to 14.6 °C. Whereas, high RMSE values ranged from 5.8 to 26.2 °C were obtained in chicken nuggets after heating in a multi-mode MW oven (Pitchai et al., 2014). And most of the final temperature difference was below 15.5 °C, except for location 3. In general, the difference of time-temperature profiles between simulated and validating results was relative small. So, the developed model was acceptable while applied in heating uniformity studies.
heating uniformity. This was also revealed by CP(20 °C), with lowest Tmax, more than 99% volume was below 20 °C at frequency of 890 MHz. However, for frequency 900 MHz, this percentage was only 90.49% while it had highest Tmax of 44.9 °C. The temperature range of T (95%) illustrated the confidence interval of each result, a narrower range demonstrated good heating uniformity. Overall, simulation results for frequency 890 and 905 MHz showed good heating uniformity and frequency of 900 MHz showed worst heating uniformity. Good repeatability was observed in three experimental results. Although experimental results showed higher Tmax, they were more uniform than simulation results referring to smaller standard deviation, higher CP (20 °C) and narrower temperature range of T (95%). Overall, the heating pattern was accessible that the volume percentage for temperature less than 20 °C was more than 95% for experimental results and most of simulation results except for 900 and 902.5 MHz. To clearly describe the volume percentage of each results, cumulative volume percentage was plotted against temperature in Fig. 7 for each simulated and experimental result. For a cumulative percentage curve, steeper slop illustrated concentrated data and uniform distribution. Instead, long tails at the two sides indicated bigger variance and worse uniformity. These curves showed that for simulation results, frequency at 890 and 905 MHz had good uniformity and frequency 900 had worst heating uniformity while the experimental results showed good repeatability. This was same as the conclusion made from statistical analysis in Table 1. Long tail at right side was observed for all the curves which indicated thermal runaway at hot edges. Cumulative percentage curve was more intuitive and clearer to show the heating treatment compared with other statistical parameters. Simulation model with frequency setting of 900 MHz was selected to represent simulation results while it had highest average temperature which implied highest microwave energy concentration at this frequency. Time temperature profile results at 900 MHz was drawn out to make comparison with experimental results. Time-temperature profiles of simulation and validating tests for eleven locations are shown in Fig. 8. Similar to the heating pattern results, high final temperatures were observed at edges of the surimi slab (locations 3 and 5). Generally, the profiles of simulation were parallel to measured results with a higher temperature level. Higher temperatures of simulation result was reasonable because the frequency with highest microwave energy concentration was chosen for comparison. At location 3, 4 and 7, the temperature profiles did not agreed very well, which indicated that simulation at a single frequency provided finite precision. A combination of frequencies that cover all the operation frequency band was recommended in numerical simulation studies to obtain the actual cumulative heating result and improve the temperature accuracy. The RMSE values (Eq. (4)) and final temperature difference calculated for eleven locations are given in Table 2. The RMSE values were
3.3. Heating uniformity study in continuously moving process Edge heating was also the major problem in this thawing process for a surimi slab continuously moving through the microwave heating cavity. Edge heating may be due to two reasons: electric field converging effect due to the big difference of dielectric constant between food and air (Tang, 2015); and the uneven electric field distribution within the cavity in moving direction (Luan et al., 2013). However, edge heating was not observed in y direction which indicated that moving played an important role in edge heating. In practical production, surimi slabs would move through the cavity one by one with certain distance other than one out and then another in immediately. To simulate this process, three surimi slabs were simulated moving together with a distance from 100 to 0 mm. The frequency was set as 900 MHz with microwave power of 10 kW. Heating pattern of the middle suirmi slab was summarized in Fig. 9. Result showed that with decreasing distance, the hot area moved from edges to inner position. Adjusting the sample difference reduced the edge heating in moving direction. Cumulative volume percentage results showed that the right tail was shorter with decreasing sample distance which brought more uniform heating pattern. This was consistent with the observed heating patterns. A sample distance below 50 mm was recommended to effectively avoid edge heating in moving direction. For the heating patterns in Fig. 9 (a), the hot spot domains were discrete which caused worse uniformity in x direction while in y direction, the temperature distributions were more uniform. A bigger size in y direction may improve the heating uniformity. Thus simulations were performed by exchanging the size in x and y direction for surimi slab i.e. the size was changed from 300 × 200 × 50 mm3 to 200 × 300 × 50 mm3. A set of three slabs were simulated together with sample distance varying from 100 mm to 0 mm. The heating pattern results of the middle slab are shown in Fig. 10. Results showed that compared with heating pattern in Fig. 9, there were two hot spots in y direction instead of a bigger hot domain. Cold edges in y direction was obvious which indicated that the electric field intensity was low at these area. In Fig. 10(b), the cumulative volume percentage results revealed that 0 mm distance had a faster increase at initial and a longer tail at the end. From 100 mm to 25 mm, the effect of sample distance on heating uniformity improvement was relatively weak. For this microwave heating cavity a bigger size and close sample distance in moving 111
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Fig. 8. Time-temperature profiles of simulation and validating tests at eleven locations of surimi slab (Refer locations in Fig. 2).
direction (x direction) was recommended to obtain better heating uniformity.
Table 2 Root mean square error (RMSE) and final temperature difference between validating tests and simulation. Location
RMSE (°C)
Tv (°C)
Ts (°C)
ΔT (°C)
4. Conclusions
1 2 3 4 5 6 7 8 9 10 11
4.3 11.0 13.8 9.3 5.0 3.3 14.6 9.6 1.0 0.9 2.4
−4.1 −4.0 63.8 −4.9 31.6 −4.2 −1.9 −1.1 −3.7 −1.6 −1.3
−0.5 10.2 17.3 5.7 16.4 −0.2 11.2 10.3 −2.5 0.1 4.2
−3.6 −14.2 46.5 −10.6 15.2 −4.0 −13.1 −11.4 −1.2 −1.7 −5.5
It was observed that the operating frequency of the MW thawing system was changing with time and affected by the presence of load. With different frequency settings the simulated heating patterns were similar to each other with two hot edges in moving direction. However, the local temperatures showed big difference which implied varying microwave energy concentrations at different frequencies. The heating pattern obtained from both simulation and validating tests agreed well to each other. Simulation with combining frequency setting was recommended to obtain the actual cumulative heating result and improve the temperature accuracy. Closer sample distance reduced the edge heating effect in moving direction. And a bigger size in moving direction was suggested for food to obtain better heating uniformity, consequently, to ensure quality and safety of thawed food products. These
Tv is the final averaged temperature of validating result, Ts is the final temperature of simulation result, ΔT = Tv -Ts.
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Fig. 9. Heating pattern and volume percentage of numerical simulation results for surimi slabs with different sample difference.
Fig. 10. Heating pattern and volume percentage results for new layout surimi slabs with different sample distance.
results provide useful information for designing industrial scale microwave thawing system.
34(1), 9–21. Chen, J., Pitchai, K., Birla, S., Gonzalez, R., Jones, D., & Subbiah, J. (2013). Temperaturedependent dielectric and thermal properties of whey protein gel and mashed potato. Transactions of the ASABE, 56(6), 1457–1467. Chen, F. Y., Warning, A. D., Datta, A. K., & Chen, X. (2016). Thawing in a microwave cavity: Comprehensive understanding of inverter and cycled heating. Journal of Food Engineering, 180, 87–100. Datta, A. K., & Davidson, P. M. (2000). Microwave and radio frequency processing. Journal of Food Science, 65(s8), 32–41. Erdogdu, F., Altin, O., Marra, F., & Bedane, T. F. (2017). A computational study to design process conditions in industrial radio-frequency tempering/thawing process. Journal of Food Engineering. https://doi.org/10.1016/j.foodeng.2017.05.003. Fakhouri, M. O., & Ramaswamy, H. S. (1993). Temperature uniformity of microwave heated foods as influenced by product type and composition. Food Research International, 26(2), 89–95. Ferrer, G., Barreneche, C., Solé, A., Martorell, I., & Cabeza, L. F. (2017). New proposed methodology for specific heat capacity determination of materials for thermal energy storage (TES) by DSC. Journal of Energy Storage, 11, 1–6. Geedipalli, S. S. R., Rakesh, V., & Datta, A. K. (2007). Modeling the heating uniformity contributed by a rotating turntable in microwave ovens. Journal of Food Engineering, 82(3), 359–368. Huang, Z., Marra, F., Subbiah, J., & Wang, S. (2016). Computer simulation for improving radio frequency (RF) heating uniformity of food products: A review. Critical Reviews in Food Science and Nutrition, 1. Huang, Z., Marra, F., Subbiah, J., & Wang, S. (2018). Computer simulation for improving radio frequency (RF) heating uniformity of food products: A review. Critical Reviews in Food Science and Nutrition, 58(6), 1033–1057. James, S. J., James, C., & Purnell, G. (2017). Microwave-assisted thawing and tempering. In M. Regier, K. Knoerzer, & H. Schubert (Eds.). The microwave processing of foods (pp.
Acknowledgements This work was supported by the National Natural Science Foundation of China [grant number 31571866]; the Program of Special Capability Development for Local Colleges and Universities in Shanghai, China [grant number 16050502200]; and Shanghai Pujiang Program under, China [grant number 17PJ1403300]. References Abudagga, Y., & Kolbe, E. (1997). Thermophysical properties of surimi paste at cooking temperature. Journal of Food Engineering, 32(3), 325–337. AOAC (2005). Official methods of analysis (17th ed.). Washington, DC: Association of Analytical Chemists. Arocas, A., Sanz, T., Maria Isabel, H., & Fiszman, S. M. (2011). Comparing microwave and water bath-thawed starch-based sauces: Infrared thermography, rheology and microstructure. Food Hydrocolloids, 25(6), 1554–1562. Bedane, T. F., Chen, L., Marra, F., & Wang, S. (2017). Experimental study of radio frequency (RF) thawing of foods with movement on conveyor belt. Journal of Food Engineering, 201, 17–25. Chamchong, M., & Datta, A. K. (1999). Thawing of foods in a microwave oven: I. Effect of power levels and power cycling. Journal of Microwave Power & Electromagnetic Energy,
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252–272). . Knoerzer, K., Regier, M., & Schubert, H. (2008). A computational model for calculating temperature distributions in microwave food applications. Innovative Food Science & Emerging Technologies, 9(3), 0–384. Kurniawan, H., Alapati, S., & Che, W. S. (2015). Effect of mode stirrers in a multimode microwave-heating applicator with the conveyor belt. International Journal of Precision Engineering and Manufacturing-Green Technology, 2(1), 31–36. Lee, S. H., Choi, W., Kim, C. T., & Jun, S. (2015). Development of a dual cylindrical microwave and ohmic combination heater for minimization of thermal lags in the processing of particulate foods. LWT-Food Science and Technology, 63(2), 1220–1228. Liu, S., Ogiwara, Y., Fukuoka, M., & Sakai, N. (2014). Investigation and modeling of temperature changes in food heated in a flatbed microwave oven. Journal of Food Engineering, 131, 142–153. Llave, Y., Mori, K., Kambayashi, D., Fukuoka, M., & Sakai, N. (2016). Dielectric properties and model food application of tylose water pastes during microwave thawing and heating. Journal of Food Engineering, 178, 20–30. Luan, D., Tang, J., Pedrow, P. D., Liu, F., & Tang, Z. (2013). Using mobile metallic temperature sensors in continuous microwave assisted sterilization (MATS) systems. Journal of Food Engineering, 119(3), 552–560. Luan, D., Tang, J., Pedrow, P. D., Liu, F., & Tang, Z. (2016). Analysis of electric field distribution within a microwave assisted thermal sterilization (MATS) system by computer simulation. Journal of Food Engineering, 188, 87–97. Ozturk, S., Kong, F., Singh, R. K., Kuzy, J. D., & Li, C. (2017). Radio frequency heating of corn flour: Heating rate and uniformity. Innovative Food Science & Emerging Technologies, 44, 191–201. Park, J. W. (2013). Surimi and surimi seafood (3rd ed.). CRC Press. Picouet, P. A., Fernández, A., Serra, X., Sunol, J. J., & Arnau, J. (2007). Microwave heating of cooked pork patties as a function of fat content. Journal of Food Science, 72(2), E57–E63. Pitchai, K., Chen, J., Birla, S., Gonzalez, R., Jones, D., & Subbiah, J. (2014). A microwave heat transfer model for a rotating multi-component meal in a domestic oven: Development and validation. Journal of Food Engineering, 128, 60–71. Pitchai, K., Chen, J., Birla, S., Jones, D., Gonzalez, R., & Subbiah, J. (2015). Multiphysics modeling of microwave heating of a frozen heterogeneous meal rotating on a turntable. Journal of Food Science, 80(12), E2803–E2814. Raj, J. D., Birla, S., Pitchai, K., Subbiah, J., & Jones, D. (2011). Modeling of microwave heating of a rotating object in domestic oven. Boston, MA: COMSOL Conference. Rattanadecho, P. (2006). The simulation of microwave heating of wood using a rectangular wave guide: Influence of frequency and sample size. Chemical Engineering Science, 61(14), 4798–4811. Regier, M., Knoerzer, K., & Schubert, H. (2016). Impact of microwave processing on nutritional, sensory, and other quality attributes. The microwave processing of foods.
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Study of heating characteristics for a continuous 915 MHz pilot scale microwave thawing system
T
Roujia Zhanga,b, Yifen Wangc,∗, Xichang Wangb, Donglei Luana,b,∗∗ a
Engineering Research Center of Food Thermal-processing Technology, Shanghai Ocean University, Shanghai, 201306, China College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China c Biosystems Engineering Department, Auburn University, Auburn, AL, 36849, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Microwave thawing Numerical simulation Surimi Edge heating Volume percentage
Heating uniformity is the critical control point on quality and safety during microwave thawing process. The objective of this study was to investigate heating characteristics of a continuous 915 MHz pilot scale microwave thawing system with a numerical simulation model and experiments. The microwave frequency and physical properties of frozen surimi were measured to provide essential information for the numerical simulation model. To validate the developed model, infrared camera and fiber optic sensors were utilized to capture the heating pattern and record the time-temperature profiles of the processed surimi. Volume percentages of the obtained temperature data for both simulated and experimental results were calculated and plotted into curves to intuitively show the heating characteristics. Results showed that the operating frequency of the thawing system was changing with time and affected by presence of load. Simulation results showed that the overall heating pattern was not sensitive to frequency settings. However, different frequency settings brought big difference of local temperature which implied varying microwave energy concentration at different frequencies. Experimental results showed good repeatability and agreed well to simulation results in heating pattern but not in temperature profiles due to varying frequencies. Simulation models with combined frequencies were recommended to improve temperature accuracy. Sample distance and size for continuously moving surimi slabs were studied using developed simulation model. It illustrated that smaller sample distance reduced the edge heating in moving direction. And a larger size of food in moving direction brought better heating uniformity.
1. Introduction Thawing process is becoming more and more significant in food industry along with the wide application of freezing technique in food preservation. Traditional thawing methods, such as air thawing or cold water thawing, require a very long time to achieve it due to low thermal conductivity of frozen food which restricts its heating rate. Thus, the quality and safety of thawed products may decrease a lot caused by drip losses and microbial spoilage. Microwave (MW) heating, in comparison, generates heat throughout the whole volume of frozen food which has the potential to significantly reduce thawing time and improve products’ quality (Arocas, Sanz, Maria Isabel, & Fiszman, 2011; Datta & Davidson, 2000; Tang, 2015; Villarreal-García et al., 2015; Wen, Hu, Zhao, Peng, & Ni, 2015). However, non-uniform heating is the major problem of microwave heating applied in food industry (Soto-Reyes, Temis-Pérez, López-Malo, Rojas-Laguna, & Sosa-Morales, 2015). The
∗
non-uniform heating is caused by uneven electric field distribution within food (Fakhouri & Ramaswamy, 1993; Huang et al., 2016, 2018; Llave, Mori, Kambayashi, Fukuoka, & Sakai, 2016; Ozturk, Kong, Singh, Kuzy, & Li, 2017; Tiwari, Wang, Tang, & Birla, 2011). The electric field distribution is affected by lots of factors such as shape and size of the microwave heating cavity, and physical properties of food. Furthermore, the heat generated within food is proportional to the square of the electric field intensity. Thus, non-uniform heating would be severe when high microwave power was applied, which has restricted the industrial application of MW heating (Luan, Tang, Pedrow, Liu, & Tang, 2016). For a thawing process, microwave heating is more complicated than a common heating process due to the big difference of dielectric properties of food between frozen and thawed statuses. The dielectric loss factor of frozen food sharply increase after thawed (Sosa-Morales, Valerio-Junco, López-Malo, & García, 2010). Thus, during MW thawing,
Corresponding author. Corresponding author. Engineering Research Center of Food Thermal-processing Technology, Shanghai Ocean University, Shanghai, 201306, China. E-mail addresses: [email protected] (Y. Wang), [email protected] (D. Luan).
∗∗
https://doi.org/10.1016/j.foodcont.2019.04.030 Received 2 February 2019; Received in revised form 25 April 2019; Accepted 26 April 2019 Available online 27 April 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
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some hot areas that melt first would absorb much more MW energy than the cold areas which results in thermal runaway and aggravates the non-uniform heating (Regier, Knoerzer, & Schubert, 2016, pp. 65–99). Therefore a uniform electric field distribution is the key point for an industrial microwave thawing process. To improve heating uniformity, the typical technique is to continually change the electric field within the microwave heating cavity using multiple feed points and mode stirrers (Kurniawan, Alapati, & Che, 2015; James, James, & Purnell, 2017). And, theoretically, the probability of microwave energy located at each position is equal, which may lead to a uniform heating pattern. However, the presence of food also affects electric field distribution which disturbs the uniform field pattern. Furthermore, edge heating caused by dielectric constant difference between food and surrounding air is another factor lead to non-uniform heating. Although, many companies have claimed that their microwave thawing system was successfully commercialized, few data are reported. Compared with freezing system, the controlled thawing systems are relatively few in commercial application (James et al., 2017) which can be attributed to lack of basic information of heating characteristic within these systems. Microwave heating is a complex process especially for thawing purpose, which is affected by many factors such as microwave frequency, phase change, dielectric properties, position, size and shape of the food material, and design and geometric parameters of the MW system (Lee, Choi, Kim, & Jun, 2015; Picouet, Fernández, Serra, Sunol, & Arnau, 2007; Raj, Birla, Pitchai, Subbiah, & Jones, 2011; Ryynänen & Ohlsson, 1996; Zhang, Lyng, & Brunton, 2007). Therefore, traditional trial-and-error experimental method is time consuming and expensive for MW heating process study. With the development of computer technology, numerical simulation is playing an important role in assisting studies on MW heating and system development (Geedipalli, Rakesh, & Datta, 2007; Knoerzer, Regier, & Schubert, 2008; Liu, Ogiwara, Fukuoka, & Sakai, 2014; Pitchai et al., 2014; Zhu, Kuznetsov, & Sandeep, 2007). The objectives of this study were to develop a numerical simulation model of a self-developed continuous 915 MHz pilot scale microwave thawing system and verify the reliability of the with real heating experiments using an infrared camera and fiber-optic thermal sensors; investigate heating characteristic of the microwave thawing system through the verified simulation model; and analyze the effect of microwave frequency and food conditions (layout, sample distance) on heating pattern to provide useful information for industrial scale microwave thawing system design.
made of polymer which was also transparent to microwaves. Two MW suppressors were arranged at the two end of the heating cavity to prevent MW leakage.
2. Materials and methods
2.1.2. Numerical parameter setting A numerical simulation model was developed based on the MW thawing system using a piece of commercial software QuickWave version 7.5 (QW3D, Warsaw, Poland). The finite difference time domain (FDTD) method was used to numerically solve the coupled electromagnetic and heat transfer equations during MW heating. The detailed governing equations were described in a study by Luan, Tang, Pedrow, Liu, and Tang (2013). Following the rule that the cell size should be less than one tenth of a wavelength (Rattanadecho, 2006), the mesh size was set as 5 × 5 × 12 mm3 in air and 5 × 5 × 2 mm3 in food. One hundred and sixty discrete moving steps were applied to simulate the continuously moving process. Each moving step had a length of 12.5 mm and a heating time of 1 s to match the continuously moving speed of 0.0125 m s−1 in experimental test. Sensitivity studies were performed for mesh size and moving step to balance the computation time and accuracy. The metal wall of the MW heating system was assumed as perfect electric conductor (PEC). A sinusoidal MW source with transverse electromagnetic (TE10) mode (dominant mode of the rectangular waveguide) was set at the MW input port. A net input power was set as 5 kW based on the recorded input and reflected power of the generator. The frequency was set based on actually measured operating frequencies. In this study, frozen surimi (with size of 300 × 200 × 50 mm3 and weight of 3.5 kg) was used as the food sample to verify the heating characteristics obtained from the numerical simulation model. An initial temperature of −18.0 °C for the frozen surimi was set, which was the temperature in common practice of cold storage. The temperature of surrounding air, conveyor belt, and the metal wall were all set as 20.0 °C (room temperature). The measured apparent specific heat and dielectric properties of food sample were saved in a separated file and updated with changing temperatures during the numerical simulation. Reported thermal conductivity values of surimi were used in the simulation (Abudagga & Kolbe, 1997). Sensitivity study was performed for thermal conductivity and proved that it had no effect on heating pattern. Chamchong and Datta (1999) found that the convective heat transfer coefficient values from 0.0001 to 30 W m−2·°C−1 did not impact heating time in microwave heating. Therefore, we selected a reported value of 20 W m−2·°C−1 as the convective heat transfer coefficient between the air and the frozen food (Pitchai et al., 2015). The dielectric loss factor of air was set as 0.
2.1. Numerical simulation model
2.2. Physical properties measurement of MW system and surimi
2.1.1. Development of MW thawing system A pilot scale 915 MHz microwave thawing system was developed in our laboratory to study heating characteristic of continuously moving thawing processes and collect useful information for industrial scale system design. The pilot scale 915 MHz microwave thawing system consisted of waveguides, a top horn applicator, a MW heating cavity, a conveyor belt and two 915 MHz MW suppressors (Fig. 1). In this system, the operating frequency of the magnetron (Sanle, Nanjing, China) that generated the MW energy was within 915 ± 25 MHz. Several standard WR975 rectangular waveguide elements (the inner cross sectional dimension was 247.7 × 123.8 mm2) were used to transmit microwaves from the generator to the horn applicator. The horn applicator was a tapered shape parallelogram with narrow end connected to the WR975 waveguide and wide end connected to the heating cavity. A window made of high temperature resistant polymer (Ultem® 1000) was installed between the horn applicator and the cavity (Fig. 1). Microwaves could go through the Ultem window but dust or steam within the cavity could not go into the horn applicator and waveguides. The frozen food sample was transported through the heating cavity by a conveyor belt
A 2650A spectrum analyzer and a matched M401 antenna (B&K Precision, Yorba Linda, Calif., U.S.A) were used to measure varying operating frequencies of the generator. The central frequency of the spectrum analyzer was set as 915 MHz with a span of 50 MHz. The microwave signals within 915 ± 25 MHz were recorded every 0.1 MHz. As a result, each frequency profile had 501 data points. Since the operating frequencies of the generator is changing with time, snapshots were taken every 5–6 s to collect the frequency information within 3 min. The frozen surimi was made from Pacific whiting. Surimi was refined fish myofibrillar proteins produced through step-by-step processes including heading, gutting, filleting, deboning, washing, dewatering, refining, mixing with cryoprotectants, and freezing (Park, 2013). Therefore, frozen surimi was a homogeneous food product and could act as stable samples for MW thawing experiments. The moisture, crude protein and fat contents were measured in triplicate following AOAC methods (2005). A differential scanning calorimeter (DSC) (Q2000, TA instruments, CA, USA) was used to measure the specific heat of the surimi at different temperatures. 106
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Fig. 1. The continuously moving 915 MHz plot scale microwave heating system.
The principle of a DSC measurement is comparing the heat flow rate into a sample with that into a standard material when both are subjected to the same linear heating rate. During thawing process, more energy is required for the latent heat of phase change besides sensible heat for temperature increase. However, due to the complexity, so far, commercial computer simulation software did not have any function to directly handle latent heat of a material. Thus, in common numerical simulation models including thawing process, an apparent specific heat was usually applied to combine the latent heat of phase change and sensible heat together as one effective parameter (Erdogdu, Altin, Marra, & Bedane, 2017; Wang & Kolbe, 1991; Taher & Farid, 2001). To determine the apparent specific heat of surimi by DSC, heat flux from the surimi sample was compared with that of a sapphire with known specific heat. Each surimi sample of 20–22 mg weighed on a microbalance (ME204E, Mettler) was sealed in an aluminum pan to perform measurement. The surimi and sapphire were heated in the DSC at a rate of 10 °C/min over a temperature range of −20.0–100.0 °C. For frozen sample, the temperature was controlled by liquid nitrogen. Two heat flux curves of the surimi and sapphire were obtained. Then heat flux data were extracted with 1.0 °C increment to calculate the apparent specific heat of surimi (CP, kJ·kg−1·°C−1) using Eq. (1) (Ferrer, Barreneche, Solé, Martorell, & Cabeza, 2017). This measurement was carried out in triplicates and the average value was used in numerical simulation later on.
CP = CPcal⋅
mcal QS mQcal
programmable circulator and a sample holder. The sample holder (Wang, Wig, Tang, & Hallberg, 2003) made of stainless steel was filled with surimi. And the surimi temperature was adjusted and maintained by circulating a liquid of ethylene glycol and water (V:V = 9:1) through the liquid jacket of the sample holder. A thermal couple was used to measure the central temperature of the surimi to ensure that it reached the required temperature. The dielectric properties were measured from the temperature of −20.0 °C to 100.0 °C with a general interval of 10.0 °C. However, within the phase change temperature range from −5.0 to 0.0 °C, a smaller temperature interval of 1.0 °C was applied. Each measurement covered a frequency range from 500 to 3000 MHz with an interval of 5 MHz which included the frequency points of 915 MHz and 2450 MHz. The measurement was carried out in triplicate. 2.3. Validation experiments To validate the developed numerical simulation model, the frozen surimi sample was processed by the continuous 915 MHz plot scale MW thawing system. The surimi with weight of 3.5 kg was placed on a tray (size of 380 × 260 × 25 mm3) on the moving conveyor belt. For each validating test, a surimi slab moved through the microwave heating cavity on the convey belt with a speed of 0.0125 m s−1 with an overall heating time of 160 s and microwave power of 5 kW. The temperature distribution at top, middle and bottom layers of the surimi slab were captured by an infrared camera (FLIR, Wilsonville, Oregon, USA) immediately after the thawing process. In order to obtain the temperature distribution at the middle layer, the frozen surimi was cut into two pieces along thickness direction and separately stored at −18 °C before thawing process. These two pieces were placed together as a whole surimi slab to perform the test. Besides infrared camera images, a fiber optic temperature measuring system (Ptsensor, Xi'an, Shanxi, China) was used to measure real time-temperature profile of the frozen surimi
(1)
Where CPcal is the specific heat of sapphire (kJ·kg−1·°C−1), mcal is the mass of sapphire (mg), m is the mass of surimi sample (mg), Qs is the surimi heat flux (mW), Qcal is the sapphire heat flux (mW). The dielectric properties of surimi were measured using a dielectric properties measurement system which consisted of a network analyzer (E5071C, Agilent Technologies), a high-temperature probe, a 107
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CP (Ta) = P (T ≤ Ta)
(3)
The curve of cumulative volume percentage could provide an intuitive approach of heating uniformity by showing the whole information of temperature distribution. Theoretically all the statistical values of maximum, minimum, mean and standard deviation are the estimation values of the distribution function. However, the cumulative distribution curve was plotted in this study instead of calculating the estimating values. Although no regression was made to fit the distribution function, the cumulative distribution curve showed all the statistical information visually. To make comparison with the real time-temperature profiles recorded by the fiber optic sensors, 11 time-temperature profiles of simulation results in the corresponding locations were also extracted from 160 temperature matrices. Within a temperature matrix, 27 cells adjacent to each location (shown in Fig. 2) were applied to calculate an average temperature. To evaluate the difference of time-temperature profiles between simulated and validating results, the root mean square error (RMSE) at each location was calculated using Eq. (4). Fig. 2. Location of the fiber-optic sensors in frozen surimi slab (all sensors inserted 25 mm from the top surface.
RMSE =
during MW heating (Wang et al., 2003b). Eleven points (as shown in Fig. 2) of surimi were selected to record the real time-temperature profiles. A 2 mm drill bit was used to drill holes with 25 mm depth from the top of the frozen surimi slab at each location. Eleven fiber-optic sensors were then inserted into holes and sealed with septum tapes. Temperatures were measured and recorded every 1 s during the MW heating process. Both temperature distribution and real time-temperature profile were carried out in triplicate.
2
[Treal (i) − Tpredicition (i)]
ni − 1
(4)
3. Results and discussion 3.1. Thermal and dielectric properties of surimi 3.1.1. Thermal properties The surimi used in this study had 74.0 ± 0.9% moisture content, 13.9 ± 0.5% crude protein and 1.5 ± 0.3% fat. The apparent specific heats of surimi at different temperatures are shown in Fig. 3. At −20.0 °C, the apparent specific heat was 1.88 kJ kg−1·°C−1. It then increased slightly to 3.3 kJ kg−1·°C−1 at −10.0 °C. With temperature rising from −10.0 to −1.0 °C, the apparent specific heat sharply increased to the peak value of 12.6 kJ kg−1·°C−1, and then sharply decreased to 3.6 kJ kg−1·°C−1 at 10.0 °C. This apparent specific heat curve with a large peak around the transition temperature revealed the phase change energy of surimi from frozen to thawed state. From 10.0 to 25.0 °C, the apparent specific heat of surimi decreased slightly to 2.3 kJ kg−1·°C−1 and then slightly increased to 2.5 kJ kg−1·°C−1 from 25 to 100.0 °C. Similar trend was reported by Chen et al. (2013) for whey protein gel and mashed potato. The peak values were 59.6 kJ kg−1·°C−1 at 2.9 °C for whey protein gel, and 32.8 kJ kg−1·°C−1 at 1.6 °C for mashed potato, and after the phase change of melting, the apparent specific heat steadied at around 3.5 kJ kg−1·°C−1. This is similar to the reported data of thawed surimi measured at 25 °C (Abudagga & Kolbe, 1997).
Heating patterns were developed from both numerical simulation and infrared camera snapshot. In numerical simulation, a 3D-dimention temperature matrix was obtained at the end of the calculation for each moving step. Thus, the simulated heating patterns at each layer could be drawn out from the final temperature matrix. A proper color bar was selected to obtain a clear heating pattern. The temperature distribution of a surimi slab measured by infrared camera was converted to a heating pattern by using the same color bar as the simulation result did. Besides heating pattern, the temperature data acquired from numerical simulation and infrared camera were statistically analyzed to calculate the volume percentage (P) density within different temperature ranges. Volume percentage density is the first time utilized to evaluate the temperature distribution in the whole volume of MW processed food samples. It is a probability density of the volume percent at each temperature range. Similar statistical treatment was used by Wang, Yue, Tang, and Chen (2005), in which study probability density frequency of walnut temperatures was used to fit normal distribution and obtain the distribution parameters of mean value and standard deviation. In this study, the volume percentage density was used to replace temperature frequency by taking into account the cell size. The volume percentage within a temperature range was calculated using Eq. (2).
3.1.2. Dielectric properties Dielectric properties are the most important parameters of being heated materials in microwave heating. The dielectric constants of surimi at frequencies of 915 and 2450 MHz are shown in Fig. 4(a). The trends of these two curves were similar to each other over temperatures. When temperature increased from −20.0 to −10.0 °C, dielectric constants of surimi increased slightly. A sharp increase of dielectric constant was observed from −10.0 to 0.0 °C, this might be attributed to a phase change of ice to water. At 0.0 °C, the dielectric constant was 59.5 at 915 MHz and 54.3 at 2450 MHz. When temperature increased from 0.0 to 100.0 °C, the dielectric constant at both frequencies decreased slightly. The dielectric loss factors of surimi at frequencies of 915 and 2450 MHz are shown in Fig. 4(b). At −20.0 °C, the surimi had a relatively low dielectric loss factor for both frequencies. The dielectric
n
∑i = 1 Vi V
n
∑
where n = 160 is the total number of measuring points and moving steps in simulation, i is the data node number, Treal is the averaged real time-temperature profile of three validating tests and Tprediction is the simulated time-temperature profile.
2.4. Data analysis
P (Ta < T < Tb) =
1 n
(2)
Where n is the number of all the cells within the temperature range of Ta to Tb, Vi is the volume of a cell that with temperature belong to this range, V is the whole volume of the food sample. Volume percentage(P) gives the probability distribution of each temperature range. In statistics, cumulative probability is more common in evaluated uniformity of a set of data. Cumulative volume percentage (CP) at a temperature is the summation of each volume percentage that with temperature lower than it: 108
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Fig. 3. Apparent specific heat of surimi for (a) from −20 °C to 100 °C, (b) the detail data nearby the transition temperature.
loss factors increased slightly when temperature reached −10.0 °C, then increased sharply till 0.0 °C. But above 0.0 °C, the dielectric loss factor at 915 MHz increased first and then decreased slightly with increasing temperature. At 2450 MHz, the dielectric loss factor decreased with increasing temperature. This was similar with the trend reported by previous studies (Chen, Warning, Datta, & Chen, 2016). Bedane, Chen, Marra, and Wang (2017) measured the dielectric properties of lean beef within temperature range from −15 to 65 °C. In the thawing region, significant increase of dielectric constant and loss factor was observed. Chen et al. (2013) reported that as the food material thawed, the change in dielectric properties was massive. At 2450 MHz, the dielectric properties of whey protein gel and mashed potato increased rapidly from −20.0 to 0.0 °C, the dielectric constant linearly decreased from 0.0 to 100.0 °C, while dielectric loss factor decreased first and then, linearly increased. Wang et al. (2003 a) investigated the dielectric properties of four kinds of different food materials over a temperature range from 20.0 to 121.1 °C. It was reported that the dielectric constant of all food materials decreased and the dielectric loss factor increased mildly at 915 MHz. The same trend was also observed in this study. The trends of dielectric loss factor were different between 915 and 2450 MHz above 0.0 °C, the increase in the dielectric loss factor is mainly attributed to the increase in ionic conduction with increasing temperature at 915 MHz (Wang et al., 2003 a). This result indicated that the heating rate would be different at these two microwave frequencies.
3.2. Heating patterns and time-temperature profiles 3.2.1. Frequency measurement Microwave frequency is relevant to its wavelength which has significant influence on the electric field distribution and heating characteristics of a microwave heating system. It is an essential information for cavity design and simulation model development. Operating frequencies for empty and loaded cavity were measured with a 2650A spectrum analyzer. For each measurement, 20–30 snapshots were captured. Fig. 5 shows the operating frequency distributions for empty and loaded cavity by plotting the measured snapshots in one graph, which clearly displayed the frequency distribution that varying with time. Results showed that the operating frequency of empty cavity clustered around 890, 900 and 905–910 MHz. However, with presence of moving load the frequency concentrated around 890, 900–905 MHz and occasionally arose at 910 MHz. To study the frequency effect on heating pattern, simulation models with frequency settings of 890, 900, 902.5, 905, 910 MHz were performed, respectively. 3.2.2. Heating pattern, volume percentage and RMSE The simulated heating pattern at different frequencies and heating pattern obtained from experimental validating test are shown in Fig. 6 with the same color bar from −5 to 25 °C. In triplicate experimental tests, the heating patterns of the surimi at different layers were highly consistent which indicated that the heating pattern in the MW heating
Fig. 4. Dielectric properties of surimi at frequencies of 915 and 2450 MHz (a) dielectric constants, (b) dielectric loss factors. 109
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Table 1 Statistical analysis of simulated and experimental results.
890 MHz 900 MHz 902.5 MHz 905 MHz 910 MHz Test 1 Test 2 Test 3
Tmax (°C)
Tmin (°C)
Average
Std
CP(20 °C)
T (95%) = [T1 T2]
28.6 44.9 41.7 31.8 43.2 50.64 41.1 49.84
−5.2 −4.3 −4.3 −5.1 −4.8 −6.22 −5.1 −4.3
1.8 6.7 5.8 3.7 3.4 1.9 2.1 3.6
5.5 8.7 7.7 5.8 7.9 6.01 5.7 6.8
0.9943 0.9049 0.9255 0.9831 0.9444 0.9708 0.9698 0.9608
[-4.4 [-2.9 [-3.0 [-3.6 [-3.8 [-5.3 [-4.1 [-2.7
16.9] 27.3] 25.7] 19.0] 29.8] 21.8] 21.8] 26.1]
CP(20 °C) was the percentage of the volume that with temperatures lower than 20 °C, T(95%) refers to the middle temperature range that covered 95% whole volume.
cavity center, which lead to asymmetric heating at these two edges. To prove this deduction, a simulation with opposite moving direction was performed and the heating pattern results (900 MHz opposite direction) was shown in Fig. 6. Results showed that simulation results for opposite moving direction brought a swap heating pattern for left and right side. Heating pattern only displayed the overall temperature distribution, to obtain more specific information of temperature distribution in statistics and evaluate heating uniformity, the temperature data of each cell in simulation model and data of infrared results were analyzed. Table 1 shows the statistical results. Besides the common statistical values of maximum, minimum, average, and standard deviation, two more indexes based on volume percentage was calculated. The CP (20 °C)was the cumulative volume percentage of 20 °C (Eq. (3)). And T (95%) refers to the temperature range that covered 95% whole volume, which excluded the 2.5% volume that had lowest temperatures and the 2.5% volume had highest temperatures, i.e. CP(T1) = 0.025 and CP (T2) = 0.975. Results showed that simulation models with different frequencies had different average temperature which indicated they absorbed different microwave energy. At 900 MHz the surimi slab was heated most and then 902.5, 905, 910 and 890 MHz in order to reduce. This implied that microwave energy was concentrated more around the frequency range of 900–905 MHz. However, high power microwave heating process brought high standard deviation and Tmax, which implied poor
Fig. 5. Operating frequency distribution of empty (A) and loaded (B) microwave heating cavity.
system was stable and repeatable. Heating patterns obtained from numerical simulation agreed well with the validating ones produced by the infrared camera. Results from both simulation and validating tests showed that hot spots domain located at the two edges in x direction (moving direction) and the cold domain located within the middle area. At different operating frequencies, the heating patterns were similar in the whole with little difference in local domain. Furthermore, the hot spot domain at two edges of the surimi slab was not exactly the same. Take the heating pattern at 900 MHz for an example, the right edge had a higher temperature and relative larger hot area, which lead to the cold area was near to the left edge. That's because the surimi moved from the right side to the left side of the cavity. Although, the two edges experienced the same electric field environments in sequence, the left edge was heated more when it was moving in and the right side was heated more when it was moving out. However, the surimi had a higher temperature and higher dielectric loss factor after moving though the
Fig. 6. Heating pattern of (a) simulation results and (b) validating tests results by an infrared camera, Test 1, Test 2 and Test 3 are experiment tests in triplication. 110
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Fig. 7. Cumulative volume percentage of (a) simulated and (b) experimental results.
stable in a small range from 0.9 to 14.6 °C. Whereas, high RMSE values ranged from 5.8 to 26.2 °C were obtained in chicken nuggets after heating in a multi-mode MW oven (Pitchai et al., 2014). And most of the final temperature difference was below 15.5 °C, except for location 3. In general, the difference of time-temperature profiles between simulated and validating results was relative small. So, the developed model was acceptable while applied in heating uniformity studies.
heating uniformity. This was also revealed by CP(20 °C), with lowest Tmax, more than 99% volume was below 20 °C at frequency of 890 MHz. However, for frequency 900 MHz, this percentage was only 90.49% while it had highest Tmax of 44.9 °C. The temperature range of T (95%) illustrated the confidence interval of each result, a narrower range demonstrated good heating uniformity. Overall, simulation results for frequency 890 and 905 MHz showed good heating uniformity and frequency of 900 MHz showed worst heating uniformity. Good repeatability was observed in three experimental results. Although experimental results showed higher Tmax, they were more uniform than simulation results referring to smaller standard deviation, higher CP (20 °C) and narrower temperature range of T (95%). Overall, the heating pattern was accessible that the volume percentage for temperature less than 20 °C was more than 95% for experimental results and most of simulation results except for 900 and 902.5 MHz. To clearly describe the volume percentage of each results, cumulative volume percentage was plotted against temperature in Fig. 7 for each simulated and experimental result. For a cumulative percentage curve, steeper slop illustrated concentrated data and uniform distribution. Instead, long tails at the two sides indicated bigger variance and worse uniformity. These curves showed that for simulation results, frequency at 890 and 905 MHz had good uniformity and frequency 900 had worst heating uniformity while the experimental results showed good repeatability. This was same as the conclusion made from statistical analysis in Table 1. Long tail at right side was observed for all the curves which indicated thermal runaway at hot edges. Cumulative percentage curve was more intuitive and clearer to show the heating treatment compared with other statistical parameters. Simulation model with frequency setting of 900 MHz was selected to represent simulation results while it had highest average temperature which implied highest microwave energy concentration at this frequency. Time temperature profile results at 900 MHz was drawn out to make comparison with experimental results. Time-temperature profiles of simulation and validating tests for eleven locations are shown in Fig. 8. Similar to the heating pattern results, high final temperatures were observed at edges of the surimi slab (locations 3 and 5). Generally, the profiles of simulation were parallel to measured results with a higher temperature level. Higher temperatures of simulation result was reasonable because the frequency with highest microwave energy concentration was chosen for comparison. At location 3, 4 and 7, the temperature profiles did not agreed very well, which indicated that simulation at a single frequency provided finite precision. A combination of frequencies that cover all the operation frequency band was recommended in numerical simulation studies to obtain the actual cumulative heating result and improve the temperature accuracy. The RMSE values (Eq. (4)) and final temperature difference calculated for eleven locations are given in Table 2. The RMSE values were
3.3. Heating uniformity study in continuously moving process Edge heating was also the major problem in this thawing process for a surimi slab continuously moving through the microwave heating cavity. Edge heating may be due to two reasons: electric field converging effect due to the big difference of dielectric constant between food and air (Tang, 2015); and the uneven electric field distribution within the cavity in moving direction (Luan et al., 2013). However, edge heating was not observed in y direction which indicated that moving played an important role in edge heating. In practical production, surimi slabs would move through the cavity one by one with certain distance other than one out and then another in immediately. To simulate this process, three surimi slabs were simulated moving together with a distance from 100 to 0 mm. The frequency was set as 900 MHz with microwave power of 10 kW. Heating pattern of the middle suirmi slab was summarized in Fig. 9. Result showed that with decreasing distance, the hot area moved from edges to inner position. Adjusting the sample difference reduced the edge heating in moving direction. Cumulative volume percentage results showed that the right tail was shorter with decreasing sample distance which brought more uniform heating pattern. This was consistent with the observed heating patterns. A sample distance below 50 mm was recommended to effectively avoid edge heating in moving direction. For the heating patterns in Fig. 9 (a), the hot spot domains were discrete which caused worse uniformity in x direction while in y direction, the temperature distributions were more uniform. A bigger size in y direction may improve the heating uniformity. Thus simulations were performed by exchanging the size in x and y direction for surimi slab i.e. the size was changed from 300 × 200 × 50 mm3 to 200 × 300 × 50 mm3. A set of three slabs were simulated together with sample distance varying from 100 mm to 0 mm. The heating pattern results of the middle slab are shown in Fig. 10. Results showed that compared with heating pattern in Fig. 9, there were two hot spots in y direction instead of a bigger hot domain. Cold edges in y direction was obvious which indicated that the electric field intensity was low at these area. In Fig. 10(b), the cumulative volume percentage results revealed that 0 mm distance had a faster increase at initial and a longer tail at the end. From 100 mm to 25 mm, the effect of sample distance on heating uniformity improvement was relatively weak. For this microwave heating cavity a bigger size and close sample distance in moving 111
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Fig. 8. Time-temperature profiles of simulation and validating tests at eleven locations of surimi slab (Refer locations in Fig. 2).
direction (x direction) was recommended to obtain better heating uniformity.
Table 2 Root mean square error (RMSE) and final temperature difference between validating tests and simulation. Location
RMSE (°C)
Tv (°C)
Ts (°C)
ΔT (°C)
4. Conclusions
1 2 3 4 5 6 7 8 9 10 11
4.3 11.0 13.8 9.3 5.0 3.3 14.6 9.6 1.0 0.9 2.4
−4.1 −4.0 63.8 −4.9 31.6 −4.2 −1.9 −1.1 −3.7 −1.6 −1.3
−0.5 10.2 17.3 5.7 16.4 −0.2 11.2 10.3 −2.5 0.1 4.2
−3.6 −14.2 46.5 −10.6 15.2 −4.0 −13.1 −11.4 −1.2 −1.7 −5.5
It was observed that the operating frequency of the MW thawing system was changing with time and affected by the presence of load. With different frequency settings the simulated heating patterns were similar to each other with two hot edges in moving direction. However, the local temperatures showed big difference which implied varying microwave energy concentrations at different frequencies. The heating pattern obtained from both simulation and validating tests agreed well to each other. Simulation with combining frequency setting was recommended to obtain the actual cumulative heating result and improve the temperature accuracy. Closer sample distance reduced the edge heating effect in moving direction. And a bigger size in moving direction was suggested for food to obtain better heating uniformity, consequently, to ensure quality and safety of thawed food products. These
Tv is the final averaged temperature of validating result, Ts is the final temperature of simulation result, ΔT = Tv -Ts.
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Fig. 9. Heating pattern and volume percentage of numerical simulation results for surimi slabs with different sample difference.
Fig. 10. Heating pattern and volume percentage results for new layout surimi slabs with different sample distance.
results provide useful information for designing industrial scale microwave thawing system.
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