THE UNIVERSITY OF DANANG UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN THI TAM THANH RESEARCH ON THE APPLICATION OF MAGNETIC FIELDS TO IMPROVE THE FREEZING EFFICIENCY OF VIETNAMESE PANGASIUS HYPOPHTHALM[.]
THE UNIVERSITY OF DANANG UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN THI TAM THANH RESEARCH ON THE APPLICATION OF MAGNETIC FIELDS TO IMPROVE THE FREEZING EFFICIENCY OF VIETNAMESE PANGASIUS HYPOPHTHALMUS FILLETS Major: Thermal Engineering Code: 9520115 SUMMARY OF DOCTORAL THESIS IN ENGINEERING DA NANG - 2023 The work was completed at the University of Science and Technology, University of Da Nang Supervisor: Assoc Prof Dr Vo Chi Chinh Dr Nguyen Thanh Van Independent Referee 1: Independent Referee 2: Referee 1: Referee 2: Referee 3: The thesis will be examined by Examination Board of University of Science and Technology, at INTRODUCTION The necesssity of the research With the objectives of developing the fisheries sector to 2030 into an important economic sector of the country, producing large goods associated with sustainable development, the promotion of research into technologies related to farming, Processing and preserving seafood is urgent today Pangasius is the most farmed and exported freshwater fish compared to other freshwater aquatic products and is considered a billion-dollar export industry of Vietnam Pangasius fillet is a semi-finished product that has passed the processing stages such as organ, bone and skin separation, It can be used for a variety of processing purposes However, they are easy spoiled if not properly preserved, usually these products are preserved in the form of freezing, the temperature at the center of the pangasius fillet is not greater than -18°C (TCVN 8338:2010) The process of freezing food is affected by their main component is water The final quality of the frozen product depends on the process of converting the phase from water to ice The size of the ice crystals is important for the final quality of frozen food as it can have an irreversible effect on the cellular structure thereby degrading the structure and color of the product For this reason, many novel freezing technologies have been developed to control the crystallization process and improve the rate of formation and development of ice crystals Electrical and magnetic interactions are factors that can rearrange hydrogenbonding networks that exist in water With the above analysis, the study of magnetic assisted freezing process for Pangasius Hypophthalmus fillet in Vietnam is necessary to improve the quality of products after freezing while reducing energy costs during freezing, thereby improving the competitive advantage in the export market for pangasius products in Vietnam Research objectives − Analysis of the effect of the magnetic field on the heat transfer coefficient during the freezing of the pangasius fillet − Assess the effect of technological parameters (temperature, velocity, thickness, magnetic flux density) on the cost and quality of freezing with static magnetic field assisted On that basis, a freezing technological mode suitable for Vietnamese pangasius fillets has been established − The effect of the magnetic field on the quality of pangasius fillet products after freezing based on criteria such as color, hardness, stringiness, gumminess, chewiness and the rate of mass loss after defrosting has been analyzed At the same time, analysis and comparison with commercial frozen pangasius fillet products in the export market Research subjects and scope a Research subjects − Pangasius fillet is raised in the Mekong Delta region (namely, Tien Giang province) − Air blast freezing with magnetic field assisted b Research scope Theoretical and experimental study of pangasius fillet freezing process on a air blast freezing model with magnetic field assisted, yielding kg per batch Research content In order to achieve the above objectives, the thesis needs to solve the following specific problems: (i) Calculate, determine the thermophysical parameters of pangasius fillet according to temperature based on the main components (ii) Calculate the freezing time and simulate the process of freezing pangasius fillet when freezing without magnetic field assisted, thereby comparing it to the freezing process with static magnetic field assisted and OMFs (iii) Simulation of magnetic field distribution in the freezing chamber to determine the appropriate method of measuring the magnetic field and the scope of experimental research Assess the effect of the magnetic field on the freezing process, namely the freezing time (iv) Calculate and compare heat transfer coefficients when freezing with and without magnetic field assisted based on the semi-experimental method (v) Develop and conduct single-factor, multi-factor experimental research to determine technological parameters suitable for the process of freezing pangasius fillet with magnetic field assisted, based on reducing energy costs while improving product quality (vi) Analysis of the basic quality indicators of the product after freezing including color, hardness, elasticity, stringiness, gumminess and the rate of mass loss after defrosting From there, it is possible to analyze and compare with commercial frozen pangasius fillet products in the export market Structure of the thesis − Chapter 1: Overview − Chapter 2: The theoretical study of magnetic filed assisted freezing − Chapter 3: Theoretical results and experimental study on influence of magnetic fields on air blast freezing process − Chapter 4: Optimize the operating parameters of the air blast freezing process with assisted static magnetic field − Chapter 5: Quality assessment of frozen products applied magnetic field CHAPTER OVERVIEW Seafood quality and the effects of freezing During food freezing occurs the phenomenon of water displacement due to the difference in the internal and the surface temperature of the product, the water moves outwards in large quantities, so it forms large ice crystals that puncture the cell membrane When defrosted, water flows out of food carrying large amounts of nutrients For rapid freezing processes, a short freezing time will help reduce the size of ice crystals and increase the quality of food after defrosting Therefore, freezing time is the most important factor, determining the quality of food in general as well as the quality of seafood in particular It should be recognized that food safety goes hand in hand with food quality and is a determinant of human health Therefore, major export markets such as Europe and the US often have very strict food safety regulations For fish and fish products, due to the existence of largely free water and high nutrient content makes these products vulnerable to short-term damage when not stored at low temperatures Therefore, the process of freezing products at low temperatures helps to limit adverse biochemical reactions, ensure food quality and increase the competitiveness of products in the export market Therefore, the study of modern freezing technologies helps reduce the time and cost of freezing, increase product quality is an inevitable trend in the seafood export industry, especially pangasius and pangasius products Pangasius and pangasius fillet Pangasius hypophthalmus belongs to catfish in the Pangasiidae family distributed in the Mekong basin Pangasius is the most farmed and exported freshwater fish compared to other freshwater fisheries and is considered a billion-dollar export industry of Vietnam Pangasius fillet is a piece of fish cut / out from the body of the fish, along the spine, this is a semi-finished product that has gone through the processing stages such as organ separation, bone and skin, It can be used for a variety of processing purposes Food freezing Freezing is one of the most common food preservation processes applied to maintain the nutrition and freshness of food, especially fish products The main purpose of food freezing is (1) food preservation, (2) reducing the activity of enzymes and microorganisms, (3) reducing the amount of liquid water – the beneficial ingredient for microbial development and (4) reducing the water activity of food In the process, the food temperature drops to the appropriate level below the freezing point, when the water transitions from a liquid state to a solid state Common freezing methods a) Blast freezing b) Contact freezing c) Cryogenic freezing Novel freezing technologies − High pressure freezing − Electric field assisted freezing − Magnetic field assisted freezing − Radiofrequency assisted freezing − Microwave assisted freezing − Ultrasound assisted freezing Current status of application of freezing technologies with magnetic field assisted − Application of magnetic field assisted freezing on experimental scale Through these studies, the effect of magnetic fields on various experimental samples (water, solution, food) has been demonstrated However, with the limitations in terms of the number of experiments as well as related studies to be able to compare and contrast each other, these research results can not be applied on an industrial scale as well as on many different food subjects − Application of magnetic field assisted freezing on industrial scale In recent years, many patents have been registered to try to take advantage of the effects of the magnetic field on the characteristics of water to improve the process of freezing food All of these patents suggest that applying magnetic fields during freezing will help control water freezing and increase supercoolation However, the magnetic field with a low magnetic density of magnetic field flux when used raises doubts about the impact of the magnetic field on water, which is a substance with low magnetic sensitivity Shortcomings in the study of magnetic field-assisted freezing technology Theexperimental data published in the papers has yet to clearly demonstrate the impact of magnetic fields on the freezing process and product quality in commercial magnetic field assisted freezers, which the companies have asserted have a positive impact and have been patented However, it should also be noted that, although there are some reports of the effects of magnetic fields in freezing, most studies have not yet been conducted on the same subjects and under the same freezing conditions so it is difficult to compare with each other Therefore, it is essential to have a scientific study that demonstrates the influence of the magnetic field on the freezing process and/or improving the quality of the product or that it is just a promotional publication of the manufacturer CHAPTER THE THEORETICAL STUDY OF MAGNETIC FIELD ASSISTED FREEZING Magnetic field and phase modification of water The magnetic field also has the ability to affect the properties of water Water can be considered a magnetic material, which means that magnetic dipole moments develop under the influence of the external magnetic field The effect of the magnetic field on the process of water crystallization can have positive implications for the quality of the frozen material Based on the initial results of scientists published in this field, it shows the positive properties when applying magnetic fields in food freezing Principles of operation of magnetic field assisted freezing Figure 2.4 Principle diagram of magnetic field assisted freezing Magnetic field assisted freezing is not a refrigeration system but an existing method of supporting the process and method of freezing to improve product quality and freezing speed Combining traditional freezing methods with magnetic fields during freezing can be beneficial in terms of energy costs and ensuring the quality of frozen products Factors affecting the freezing process with magnetic field assisted a Air temperature in freezing environment b Air velocity in freezing environment c The size and shape of the freezing product c Magnetic field flux density d Other factors Pangasius fillet freezing problem model The study model of pangasius fillet freezing with assisted magnetic field is presented in Figure 2.5, in which the pangasius fillet pattern is placed on the support tray surface located between the receiver and magnetic field The cold air is blown by the fan from the cooler parallel through the upper and lower surfaces of the sample Dàn lạnh Bộ lọc Cách nhiệt Bộ thu phát từ tr-ờng 100 Quạt W Fillet cá tra 300 Không khí lạnh, tm, 700 Fillet cá tra 500 Không khí lạnh, tm, t m = 30 40°C 175 100 t m = 30 40°C 100 500 100 100 700 500 100 700 Figure 2.5 Arrange the product in the freezing chamber Thus, the problem that the thesis studied is the process of freezing pangasius fillet in the air environment of forced movement, convection from sides of the sample Methods for determining heat exchange coefficients The heat transfer coefficient k from the freezing medium to the center of the product is determined based on the formula: (2.12) k= + One of the methods for determining convectiven heat transfer coefficient is to use product temperature measurement results over time during freezing, thereby determining according to Biot number Bi = Z (2.13) All air blast freezing processes have a similar curve form Initially, they have a certain lag and then the temperature at the center of the food decreases with the exponential Non-dimension temperature is determined as follows: = tm − t t − tm = tm − ti ti − tm (2.14) The 'lag' between the start of freezing and the non-dimension temperature is measured by a factor j, as shown in Figure Figure 2.7 Typical freezing curves by non-dimension temperature From Figure 7, it can be seen that the linear part of the freezing curve can be described as follows: (2.15) = j exp(−C. ) To find the experiments of the equation (2.11), two non-dimension parameters need to be calculated, which is the Biot number, defined in the equation (2.13), and the Fourier number, which is determined as follows: Fo = a (2.20) Z2 Mathematical model determines theoretical freezing time Methods for determining freezing time: Plank (1941), Nagaoka (1956), Levy (1958), Cleland & Earle (1984) and Pham Q T (1986) Simulation of pangasius fillet freezing process The problem of simulating the freezing process of pangasius fillet is set in a 3D domain with a limited size of 210x110x15 mm For simplicity, the underside and side of the head and tail side of the piece of fish are considered flat Set for heat transfer problem: ( h) = ( T ) + ( hF mF ) (2.73) Set for the transmission problem: ( CF ) = ( DF CF ) (2.75) The assumptions of the numerical simulation problem of freezing: - The freezing environment temperature is assumed to be constant during the freezing process under each different mode - The air velocity through the pangasius fillet surface is assumed to be constant during freezing - The thermal conductivity of the tray is ignored, so the boundary condition of the simulation is a symmetry type condition - Neglecting the radiation due to the small size of the freezing chamber, the temperature difference between the environment and the pangasius fillet is low - The freezer chamber is considered to be completely insulated, so heat loss to the environment is ignored - Moisture is considered to be evenly distributed inside the pangasius fillet Simulation of magnetic field distribution in freezing equipment In order to observe the distribution of magnetic fields in the freezing chamber when OMF and SMFs are used, and to help install temperature sensors in the right locations, the magnetic field distribution has been simulated on comsol Multiphysics v5.5 software Conclusion of Chapter The method of measuring unstable temperature combined with the relationship between homogeneous standards is determined to be suitable for calculation of heat transfer coefficient during freezing Due to the combination of theory and experiment, this method is more reliable, suitable for complex freezing processes but also requires temperature measuring equipment during freezing to be more accurate To calculate the theoretical freezing time to compare with simulation results and experimental results, 05 mathematical models: Plank, Nagaoka, Levy, Cleland and Earle, Pham Q T were proposed Theoretical research shows that these 05 models are suitable for calculating theoretical freezing times for products with complex shapes CHAPTER THEORETICAL RESULTS AND EXPERIMENTAL STUDY ON INFLUENCE OF MAGNETIC FIELDS ON AIR BLAST FREEZING PROCESS Survey of magnetic field distribution in the freezing chamber The magnetic field is measured using the Tenmars TM-197 magnetic field meter (range 0~30000 Gauss, error 0.1G) The measuring position is the cross section between the center of the fillet, evenly 02 sets of magnetic fields above and below Each measurement is at points (in figure 3.6), each data for minutes (180s) and take the average result y 40 40 ChiÒu réng miếng cá Phần l-ng Phần bụng 105 105 Phần đuôi x Phần đầu Chiều dài miếng cá Figure 3.6 Magnetic field measurement positions in the experiment The results of measuring the oscillating and static magnetic field are presented on figures 3.7 and figure 3.8 Figure 3.7 OMF distribution Figure SMF distribution Experimental procedure The average size of the pangasius fillet selected in this study was 210x80x15mm (length x width x thickness) and the average weight was 25010 g Pangasius fillets are frozen from 12°C to -18°C (center temperature) in three different methods: conventional ventilation (ABF), air blast freezing assisted oscillator magnetic fields (OMF) and air blast freezing assisted static magnetic fields (SMF) Evaluate the operation of the model To determine the freezing time, the product center temperature must reach no greater than -18°C, according to TCVN 8338:2010 a The freezing process without assisted magnetic field Conduct test freezing runs for pangasius fillets at different air temperatures (30, -35, -40C) and measure the product center temperature as well as air temperature in the freezing chamber Freezing time at -30C, -35C, -40C is 2457s, 2381s and 2210s, respectively The air temperature drops by 5C, the freezing time decreases by about 5% Simulation results of OMF distribution in the freezing chamber The grid type is free tetrahedral, with a total number of elements of 34728, the total number of nodes is 203850 The total number of freedoms of the problem is 284167 Figure 3.18 Magnetic field distribution oscillating at the center, t = 0.005s Compared with experimental results on the model, at the same distance between two 80mm transceivers and transmitters, the average flux density at measurement locations reached 68.2 Gauss, 7.83% lower than the value measured average value Thus, the simulation results show more clearly the magnetic field distribution over the entire cross-section of the fish Simulation results of SMF distribution in the freezing chamber The total number of elements of 20492, the total number of nodes is 115896 The total number of freedoms of the problem is 159225 a) b) b Figure 23 Distribute the magnetic field at the cross section at the center of the fish) piece with the distance between 02 magnets: a) 80mm; b) 70mm; The area between the magnetic fish pieces differs from the fish head area at the distance between the two magnets 30mm and 40mm up to 732, 544 Gauss, 70% and 64% respectively Therefore, in experimental research, it is necessary to avoid experiments with too low distances between two magnets Results simulating the freezing process without magnetic fields a) -30C b) -35C c)-40C Figure 3.28 Freezing curve at the surface and center of pangasius fillet 10 When compared to the results of calculations using calculus models , the difference between the method of calculus and simulation is very large (from 22.6% to 55%), it is recommended to use in cases of simple calculation a) 500 seconds b) 1000 seconds c) 1500 seconds d) 2000 seconds Figure 3.29 Temperature simulation results during freezing at 11 cross sections Calculation of heat transfer coefficient during freezing In fact, to evaluate the accuracy of experimental data in the method of calculating the heat transfer coefficient as described in section 2.4, the pangasius fillet temperature sensors during freezing are installed at the surface of the pangasius fillet and the product center, as shown in Figure 3.32a An 8-channel temperature data logger with type J thermocouple (VersaLog TC, Canada) with an accuracy of 0.2% was used to determine pangasius fillet temperature during freezing a) Temperature sensor location b) Temperature datalogger Figure 3.32 Description of the method of measuring temperature during freezing Table 3.15 Compare mean heat transfer coefficient Mean heat transfer coefficient, W/(m2.K) Temperature sensor location Freezing method At surface At center Error (%) ABF 35,38 34,75 1,78 OMF 36,83 35,76 2,90 SMF 36,77 36,24 1,45 The temperature data measured during the freezing process are then used to determine the coefficients j and C in equation (2.15) using nonlinear regression with the help of Minitab software 19 The results of the regression analysis are 11 divided into two cases: (a) the location of the sensors at the center; (b) location of the sensors at the surface Comments on calculation and simulation results: - The results of the calculation of the heat transfer coefficient are applied only in the range of experimental data recording, that is, when the pangasius fillet temperature varies between 12C and -18C - When using both temperature data sources recorded in two cases: (a) the location of the installation of sensors at the center; (b) the location of the installation of sensors at the surface; to calculate the heat transfer coefficient, the results show that the k-value calculated in case (b) is always higher than the case (a) from 1.45 to 2.9% - The results of factor 1 have a greater error of 4.62 to 9.31% while the error when calculating the shape factor E ranges from 2.9 to 5.58% This result shows that the heat transfer coefficient value and the shape coefficient calculated from the semi-theoretical half-experimental method are reliable - Compared with the freezing process without magnetic field, the heat transfer coefficient of the freezing process with OMF is about 3.0% higher on average Similarly, when freezing with assisted static magnetic, the difference in average heat transfer coefficient is about higher 9.4% Conclusion of Chapter 3: On the basis of inheriting mathematical models of freezing time calculations, the theoretical results for pangasius fillets show that Pham Q.T model has the lowest error so it is acceptable in simple computational cases For further verification, the process of freezing pangasius fillets without magnetic field assisted is simulated based on the actual 3D size of the piece of fish The results showed that the freezing time was lower than the theoretical calculation (Pham Q T model) of 27.2%, 26.2% and 22.6% respectively at temperatures of -30, 35 and -40C, respectively When compared to the experimental results, the simulated freezing time was 4%, 15%, and 17% respectively at temperatures of -30, -35 and -40C, respectively The experimental results showed that both magnetic fields had an effect on freezing time, the OMF with an average density above 40 Gauss which reduced freezing time by about 2% to 25% compared to normal freezing while SMF reduced the time by up to 56% but required a magnetic flux above 400 Gauss The results of the calculation of the heat transfer coefficient based on experimental data showed that the heat transfer coefficient of the freezing process with OMF is about 3.0% higher on average Similarly, when assisted freezing with SMF, the difference in average heat transfer coefficient is about 9.4% higher than conventional air blast freezing 12 CHAPTER OPTIMIZE THE OPERATING PARAMETERS OF THE AIR BLAST FREEZING PROCESS WITH ASSISTED STATIC MAGNETIC FIELD Multi-factor experimental planning method The second-level model built from the second-level direct planning takes the form of: k Y = b0 + b j X j + j =1 k b j , l =1 j l k jl X j X l + b jj X j (4.2) j =1 Specify technological parameters As presented in section 3.1.4, the technological parameters include parameters: freezing temperature, air velocity, thickness of fish samples and magnetic flux density Specify output parameters (target function) a Freezing time According to the content presented in section 3.1.5, the freezing time in this thesis is determined by the time it takes to reduce the temperature of the fish sample from the initial (12C) to the final (-18C) at the location with the longest duration on 07 measuring positions as shown on figure 3.11 b Mass loss rate after freezing Pangasius fillet samples before and after freezing are determined the rate of mass loss after freezing based on the following formula: FL = G1 − G2 100% G1 (4.20) c The color of the pangasius fillet after freezing The color determination method is performed using a colorimeter (CR-410, Konica-Minolta, Japan), and color index in the CIE system consists of L*, a* and b* which are defined as indicators of brightness, red, and yellow, respectively The color is taken from (nine) random positions on the surface of the pangasius fillet The total color difference (E) is calculated using the following formula: ΔE = ( ΔL ) + ( Δa ) + ( Δb ) (4.21) Results of multifactorial experimental research Set up a multi-factor experimental problem Figure 4.2 Illustration of the input variables and target functions of the experiment 13 Establish an experimental regression equation a Freezing time The regression equation describing the freezing time (Y1) is established: Y1 = 1612,31+270,104 X1 − 431,093 X + 179,674 X − 156,762 X (4.25) − 210,875 X X − 145,125 X X + 133, 465 X 22 The graph shows the correlations presented on figure 3: Standardized Pareto Chart for Y1 Estimated Response Surface X2=0.0,X3=0.0 Y1 B:X2 A:X1 AB C:X3 D:X4 CD BB AD AA AC BC CC DD BD + - 0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 (X 1000) Y1 -2 -1 -2 -1 X4 X1 Standardized effect 12 15 a) Pareto graph b) Y1 = f(X1, X4) with X2 = 0; X3 = Figure 4.3 Graph of the response surface of the freezing time function Rewritten regression equations for freezing time functions in the form of distortions: Y1 = 2777,077+138,371Z1 − 976, 431Z + 380,087Z3 − 9,317Z (4.26) − 16,87 Z1Z − 0,7256Z3 Z + 21,354Z 22 b Mass loss rate after freezing Therepatriation process describes the mass loss rate after freezing (Y2) which is established as follows: Y2 = 1,39865 + 0, 408205 X − 0,676427 X + 0,302907 X − 0, 417856 X (4.27) − 0,372125 X1 X + 0,510032 X 22 + 0,19599 X 32 The graph shows the correlations presented on figure 4: Estimated Response Surface X2=0.0,X3=0.0 Standardized Pareto Chart for Y2 Y2 B:X2 BB D:X4 A:X1 AB C:X3 CC AD CD AA BC DD AC BD 0.0 1.0 2.0 3.0 4.0 5.0 + 4.8 Y2 3.8 2.8 1.8 0.8 -0.2 -2 Standardized effect 10 -1 -2 -1 X4 X1 12 a) Pareto graph b) Y2 = f(X1, X4) with X2 = 0; X3 = Figure 4.4 Graph of the response surface of the mass loss rate The regression equation written for the mass loss rate after freezing function in the form: Y2 = 35,994 + 1,659.Z1 − 1,626.Z + 0,151.Z3 − 0,004.Z − 0,03.Z1.Z + 0,02.Z 22 + 0,031.Z 32 (4.28) c Total average color difference Based on experimental results and multi-factor regression analysis, after excluding coefficients with meaningful levels of P>0.05 (b13, b14, b23, b44), the 14 regression equation describing the total average color difference (Y3) is established as follows: Y3 = 18,7674 + 1,14711 X1 − 1,92516 X + 0,837128 X − 1,13132 X (4.29) − 1,00938 X1 X + 0,804375 X X − 0,623125 X X + 0,646683 X12 + 1,30545 X 22 + 0,749559 X 32 The graph shows the correlations presented on figure 4 The regression equation written for the average total color difference function in the form of distortion: Y3 = 0,700 − 0,0119 Z1 − 0,3096 Z − 0,0776 Z3 + 4, 28.10 −4 Z (4.30) + 0,0024Z1Z − 0,0015Z1Z3 − 1,95.10−6 Z1Z + 6,30.10−5 Z12 + 0,0264 Z 22 + 0,1648Z32 − 1,83.10 −7 Z 42 Standardized Pareto Chart for Y3 Estimated Response Surface X2=0.0,X3=0.0 B:X2 A:X1 D:X4 BB AB C:X3 BD CC AA CD BC AC DD AD Y3 + - 15.0 17.0 19.0 21.0 23.0 25.0 27.0 29.0 31.0 33.0 35.0 32 29 Y3 26 23 20 Standardized effect 17 10 -2 -1 -2 -1 X4 X1 a) Pareto graph b) Y3 = f(X1, X4) with X2 = 0; X3 = Figure 4.5 Graph of the response surface of average total color difference function Observe: Freezing temperature and thickness affect in a negative direction, that is, the lower the freezing time, the shorter the freezing time, while the air velocity and magnetic flux density in the opposite direction The rate of mass loss after freezing is also inversely proportional to the magnetic flux density, so the introduction of SMFs to support the freezing process also helps reduce mass loss For pangasius fillets after freezing, the color will be inferior to the fresh sample, i.e E* will increase (>16) Therefore, the technological parameters significantly affect the total average color difference Optimize the freezing process For multi-target optimization in the process of freezing with SMF assisted, the optimal value of the variables X1, X2, X3, X4 is calculated so that the value of the target functions Y1, Y2, Y3, the same best in the range -1,664 X1, X2, X3, X4 1,664 15 Y1 = f1min ( X 1opt , X 2opt , X 3opt , X 4opt ) = f1 ( X , X , X , X ) Y2 = f ( X 1opt , X 2opt , X 3opt , X 4opt ) = f ( X , X , X , X ) Y3 = f ( X 1opt , X 2opt , X 3opt , X 4opt ) = f ( X , X , X , X ) X = ( X , X , X , X ) −1,664 X , X , X , X 1,664 (4 1) The Response Surface Method (RSM) is used in this case to determine the optimal technological parameters for the freezing process The results have determined the value of optimal variables as follows: X1opt = −1,5371; X 2opt = 0,3198; X 3opt = 0, 2089; X 4opt = 1,645 , corresponding to the value of the target functions: Y1min = 906s; Y2min = 0,174%; Y3min = 17,31 The results showed that the mass loss rate met TCVN 8338:2010 and the freezing time was the lowest The level of compromise between these mathematical models reached 90.4% Converts to the value of real variables: Z1opt = −42,7o C; Z2opt = 5,8m/s; Z3opt = 15, 4mm; Z4opt = 564,5Gauss Optimizing target functions at the same time with input variables is a complex problem when analyzing, sometimes impossible to solve Therefore, the application of software to solve the complex optimization problem is necessary For target functions according to the level experimental planning, the response surface method (RSM) is considered suitable for optimal problem solving although there are also limitations in the lack of accuracy Observe: Single-factor experimental results show the right direction in the study to put the assisted magnetic field for pangasius fillet freezing in order to reduce freezing time, reduce energy costs and improve product quality The results of the data analysis have built up 03 regression equations that correlate between the target functions and input variables, and show that these parameters greatly affect the freezing process At the same time, the multiobjectives optimization method is also applied to determine the suitable value of technological parameters when conducting freezing with SMF assisted The result has been the optimal mode for the freezing system with SMF assisted should be set at the levels: freezing temperature -42.7C, air velocity 5.8 m/s, product thickness 15.4 mm and magnetic flux density of 564.5 Gauss With the results of these studies, it is necessary to conduct more experiments to evaluate the quality of products after freezing such as hardness, stringiness, defrosting time, the rate of mass loss after defrosting, to get the basis to demonstrate the influence of the magnetic field on product quality 16 CHAPTER QUALITY ASSESSMENT OF FROZEN PRODUCTS APPLIED MAGNETIC FIELD The importance of quality to frozen products Changes that occur in food quality during freezing, freezing and defrosting play an important role in the success of marketed freezing products such as pangasius fillets Therefore, determining the quality of frozen food in all stages, from fresh products, frozen products to defrosted products is a must Moreover, for many frozen food products, they are consumed after the next processing, such as cooking, the quality of the food also needs to be determined after processing The quality parameters are often widely studied and are considered important to affect the frozen product as color, texture, deliciousness and taste Method of researching the quality of pangasius fillet products Material Pangasius fillet with an average size of 210x80x15mm (length x width x thickness) is frozen to a center temperature of -18°C by 02 methods: air blast freezing does not support magnetic field wind level with SMF assisted The samples are vacuum-packed and stored in storage cabinets at -20°C for weeks before being tested The fish samples were defrosted in the refrigerator at a temperature of 4°C before measuring Apparatus The experiments were conducted on the model of air blast freezing, product storage cabinets and refrigerators located in Room, Faculty, University a Color of pangasius fillet after freezing The color determination method is carried out using a colorimeter (CR-410, Konica-Minolta, Japan) Figure 5.4 Cr-410 measuring positions and colorimeters b Rate of mass loss after defrosting The rate of mass loss after defrosting is the parameter calculated from the results of measuring the volume of pangasius fillet before and after defrosting to the temperature at the center reaching 0C TL = G2 − G3 100% G2 c Mechanical analysis of pangasius fillet 17 (5.8) The mechanical tests were performed on the CT3-4500 Brookfield food structure analyzer Pangasius samples are mechanically analyzed under 02 methods: shear test to determine hardness, peak stress and TPA method to determine elasticity, stringiness and gumminess Since the measurement cannot be made with a fish sample that is too large, each sample will be taken with a size of 80 x 30 (length x width, mm) in the same position as shown below: Figure 5.7 Structural analysis sampling location Color analysis results - The average values L* (brightness), a* (red) and b* (yellow) are relatively uniform between patterns in the same freezing method with standard deviations of 1.7; 1.16; 1.08 and 1.4; 1,68, respectively; 0.88 Compared to the fresh pangasius fillet, the L* and b* values of the freezing samples both increased - The L* brightness value when freezing has lower SMF assisted when freezing without a magnetic field is 15% This suggests that samples of frozen fish with magnetic field assisted will be darker and have a higher concentration of carotene in the carcasses than conventional frozen fish samples - The red a* color in fish meat muscles is characteristic of carotene content, so for samples of frozen fish with SMF assisted, this value is much greater, which will be beneficial in terms of carotene content when processing - The xanthophyll content in pangasius fillets is proportional to the b* value, so with the b* value of frozen fish samples with a SMF higher than 31.2%, the xanthophyll content is also higher than that of freezing products without a supporting magnetic field - The total E* color difference of frozen fish samples had SMF assisted 9.7% lower than conventionally ventilated samples This also reflects somewhat on product quality when freezing with magnetic field assisted is kept more stable Assessment of mass loss rate after defrosting Frozen pangasius fillet samples with magnetic field assisted have an average TL value of 1.287%, which is 55.7% lower than conventional air blast freezing samples This shows that the quality of pangasius fillets is kept more stable when 18 freezing has a magnetic field and is also consistent with the results of mechanical analysis as well as color analysis Figure 5.10 Mass loss rate when defrosted Results of mechanical analysis a Cutting Test Analysis The results of the analysis by cutting method showed that the hardness and peak stress of the pangasius fillet when freezing had magnetic field assisted about 6.1% greater than the conventional air blast freezing method In addition, since the stringiness of the fish is related to hardness (stringiness = hardness x cohesion), the result also reflects the stringiness of the frozen fish piece without a magnetic field is lower b TPA method The freezing method has SMF assisted for products with greater elasticity, stringiness and gumminess than the conventional air blast freezing method of 2.9%, 27.5% and 33.2%, respectively The effect of the magnetic field during freezing to greater stringiness and gumminess than hardness and elasticity Compare quality with commercial products The mechanical properties measured from experimental research samples when freezing without magnetic fields (ABF) and with magnetic field assisted (SMF) are also used to compare with the mechanical properties measured from the Glorious Commercial Product Sample (VQ), shown in Figure 5.15 Figure 5.15 Graph comparing the mechanical properties of VQ-ABF-SMF models In terms of hardness, the results of the analysis showed that the difference between the VQ model and the ABF model was 2.1%, compared to the SMF model was 4% lower It can be seen that the difference in postfreeze hardness value for samples is negligible (