Research article

Evaluation of storage stability of refrigerated buffalo meat coated with hydrothermally treated potato starch incorporated with thyme (Thymus vulgaris) and ginger (Zingiber officinale) essential oil

  • Received: 25 July 2024 Revised: 04 October 2024 Accepted: 29 October 2024 Published: 02 December 2024
  • The present study was carried out to prepare thyme essential oil (TEO) and ginger essential oil (GEO)-incorporated edible starch coating on buffalo meat to extend its refrigerated shelf-life. Edible coatings incorporated with antimicrobials can act as an active packaging system for the preservation of meat using biopolymers and plant-based essential oils. Buffalo meat samples were coated by hydrothermally treated starch solution incorporated with thyme and ginger essential oil at five different proportions (total of 2.5% of starch solution). A total of five treatments (S1, S2, S3, S4, and S5) along with two controls (S6 and S7) were subjected to microbiological [total viable count (TVC), Staphylococcus aureus count, psychrotrophic bacteria count (PTC), and coliform count] and physico-chemical analyses such as thiobarbituric acid reactive substance (TBARS) value, total volatile basic nitrogen (TVBN) content, extract release volume (ERV), metmyoglobin (Met-Mb), pH, weight loss, and water activity at 0, 3, 6, 9, and 12 days of storage. These metrics were compared between days and between treatments. Compared with the uncoated control (S7), S4 decreased TVC by 2.60 log, and S5 decreased PTC, Staphylococcus aureus, and coliform by 4.71 log, 1.18 log, and 3.01 log, respectively, in 12 days. S4 reduced TBARS and TVBN by 46.14% and 27.86%, respectively, while S5 increased the ERV by 40.94% in 12 days when compared to S7. Metmyoglobin content, pH, ERV, and TVBN were found to have a high correlation with TVC, while pH was found to have a high correlation with TVBN and ERV. It can be concluded that the increase in TEO concentrations on starch coating increases the ability of buffalo meat to resist microbiological as well as chemical spoilage.

    Citation: Sangam Dahal, Basanta Kumar Rai, Anish Dangal, Kishor Rai, Prekshya Timsina, Ramesh Koirala, Sanjay Chaudhary, Pankaj Dahal, Tanka Bhattarai, Angelo Maria Giuffrè. Evaluation of storage stability of refrigerated buffalo meat coated with hydrothermally treated potato starch incorporated with thyme (Thymus vulgaris) and ginger (Zingiber officinale) essential oil[J]. AIMS Agriculture and Food, 2024, 9(4): 1110-1133. doi: 10.3934/agrfood.2024058

    Related Papers:

    [1] Mikaeel Ahmadi, Mir Sayed Shah Danish, Mohammed Elsayed Lotfy, Atsushi Yona, Ying-Yi Hong, Tomonobu Senjyu . Multi-objective time-variant optimum automatic and fixed type of capacitor bank allocation considering minimization of switching steps. AIMS Energy, 2019, 7(6): 792-818. doi: 10.3934/energy.2019.6.792
    [2] Santosh Kumar Sharma, D. K. Palwalia, Vivek Shrivastava . Distributed generation integration optimization using fuzzy logic controller. AIMS Energy, 2019, 7(3): 337-348. doi: 10.3934/energy.2019.3.337
    [3] Abdollah Kavousi-Fard, Amin Khodaei . Multi-objective optimal operation of smart reconfigurable distribution grids. AIMS Energy, 2016, 4(2): 206-221. doi: 10.3934/energy.2016.2.206
    [4] Minh Y Nguyen . Optimal voltage controls of distribution systems with OLTC and shunt capacitors by modified particle swarm optimization: A case study. AIMS Energy, 2019, 7(6): 883-900. doi: 10.3934/energy.2019.6.883
    [5] HVV Priyadarshana, MA Kalhan Sandaru, KTMU Hemapala, WDAS Wijayapala . A review on Multi-Agent system based energy management systems for micro grids. AIMS Energy, 2019, 7(6): 924-943. doi: 10.3934/energy.2019.6.924
    [6] V. V. Thang, Thanhtung Ha . Optimal siting and sizing of renewable sources in distribution system planning based on life cycle cost and considering uncertainties. AIMS Energy, 2019, 7(2): 211-226. doi: 10.3934/energy.2019.2.211
    [7] Md Mashud Hyder, Kaushik Mahata . Reconfiguration of distribution system using a binary programming model. AIMS Energy, 2016, 4(3): 461-480. doi: 10.3934/energy.2016.3.461
    [8] Kushan Tharuka Lulbadda, K. T. M. U. Hemapala . The additional functions of smart inverters. AIMS Energy, 2019, 7(6): 971-988. doi: 10.3934/energy.2019.6.971
    [9] Baseem Khan, Hassan Haes Alhelou, Fsaha Mebrahtu . A holistic analysis of distribution system reliability assessment methods with conventional and renewable energy sources. AIMS Energy, 2019, 7(4): 413-429. doi: 10.3934/energy.2019.4.413
    [10] Zijian Hu, Hong Zhu, Chen Deng . Distribution network reconfiguration optimization method based on undirected-graph isolation group detection and the whale optimization algorithm. AIMS Energy, 2024, 12(2): 484-504. doi: 10.3934/energy.2024023
  • The present study was carried out to prepare thyme essential oil (TEO) and ginger essential oil (GEO)-incorporated edible starch coating on buffalo meat to extend its refrigerated shelf-life. Edible coatings incorporated with antimicrobials can act as an active packaging system for the preservation of meat using biopolymers and plant-based essential oils. Buffalo meat samples were coated by hydrothermally treated starch solution incorporated with thyme and ginger essential oil at five different proportions (total of 2.5% of starch solution). A total of five treatments (S1, S2, S3, S4, and S5) along with two controls (S6 and S7) were subjected to microbiological [total viable count (TVC), Staphylococcus aureus count, psychrotrophic bacteria count (PTC), and coliform count] and physico-chemical analyses such as thiobarbituric acid reactive substance (TBARS) value, total volatile basic nitrogen (TVBN) content, extract release volume (ERV), metmyoglobin (Met-Mb), pH, weight loss, and water activity at 0, 3, 6, 9, and 12 days of storage. These metrics were compared between days and between treatments. Compared with the uncoated control (S7), S4 decreased TVC by 2.60 log, and S5 decreased PTC, Staphylococcus aureus, and coliform by 4.71 log, 1.18 log, and 3.01 log, respectively, in 12 days. S4 reduced TBARS and TVBN by 46.14% and 27.86%, respectively, while S5 increased the ERV by 40.94% in 12 days when compared to S7. Metmyoglobin content, pH, ERV, and TVBN were found to have a high correlation with TVC, while pH was found to have a high correlation with TVBN and ERV. It can be concluded that the increase in TEO concentrations on starch coating increases the ability of buffalo meat to resist microbiological as well as chemical spoilage.



    Distribution systems usually consist of feeders with a radial configuration in which approximately 13% of the generated power is wasted as power losses [1]. The increasing demand and load led to the development of distribution systems which causes a further voltage drop, increases losses, as a result reduction of the bus voltage stability and load imbalance. Therefore, an appropriate installation of DGs and SCBs can reduce network losses, improve network performance, and postpone investment.

    Many meta-heuristics swarm techniques, which mimic the social behavior of swarms, herds, flocks, or schools of creatures in nature, have been proposed to resolve the issue of placement and sizing of DGs and SCBs. A Chaotic Artificial Bee Colony (CABC) algorithm was offered to find the best candidate locations and optimum sizes for DG units [2]. A Cuckoo Search Algorithm (CSA) was proposed to find optimal DG allocation and sizing in a radial distribution system [3]. A Whale Optimization Algorithm (WOA) was adopted for optimal allocation and sizing of DGs in RDSs [4]. In [5], the researchers used Particle Swarm Optimization (PSO), the Firefly algorithm (FA), and the Novel Bat Algorithm (NBA) methods to solve a multiobjective problem for various DG cases. A hybrid method of an improved PSO and Bat-Inspired Algorithm (BIA) for the optimal placement and sizing of DGs in RDSs was proposed in [6].

    An Artificial Bee Colony (ABC) method was proposed for solving the SCBs problem [7,8,9]. A hybrid technique of the fuzzy and ABC approaches was introduced to obtain the optimal locations and sizing of SCBs [10]. Two stage approach of the fuzzy and Bat Algorithm (BA) methods was proposed to optimize the locations and sizes of SCBs in a radial distribution network [11]. The loss sensitivity and Cuckoo Search Algorithm (CSA) methods were suggested for solving the SCBs problem [12]. A Firefly Algorithm (FFA) method was offered [13,14]. The loss sensitivity and Fruit Fly optimization algorithm (FOA) methods were proposed in [15]. An Oppositional Krill Herd (OKH) approach was suggested to solve the SCBs problem in [16]. A PSO was proposed by other researchers in [17,18].

    A fuzzy-dragonfly and fuzzy multi-verse optimizer methods were proposed to solve the SCBs problem in RDSs, and also a hybrid of the fuzzy and genetic algorithms was proposed to study the impact of temperature on SCBs placement in distorted RDSs [19,20,21,22]. A Multiobjective Particle Swarm Optimization (MOPSO) was suggested to determine optimal candidate locations and size of DGs and SCBs simultaneously in RDSs [23]. FFA and PSO methods were proposed to solve the DGs and SCBs placements problem in distribution systems [24,25].

    Besides these swarm techniques, many remarkable researches have been carried out to solve either DGs/ SCBs placement problem or both stimulatingly. For example, A harmony search algorithm [26] was introduced to optimize the placement and sizing of DGs problem, in distribution network, aiming to improve the voltage profile, to reduce the power loss and to minimize the Total harmonic Distortion (THD). A hybrid of fuzzy decision-making tool and Multi-Objective Non-Sorting Genetic Algorithm (MONSGA) [27] was proposed to find the optimal placement and sizing of DGs in distribution networks through considering three objective functions i.e. power loss reduction, minimization of the comprehensive costs, and minimization of stability and security indices. In [28], two scenarios for DG placement in a distributions system were presented. In the first scenario, only real power loss reduction was considered. Both the optimal size and location of DGs were obtained as outputs from the system loss formula. In the next scenario, the voltage stability index was considered.

    A water cycle algorithm [29] was presented to find the optimal placement and sizing of DGs and SCBs in RDSs aiming to minimize power losses, voltage deviation, total electrical energy cost, total emissions produced by generation sources and to improve the voltage stability. An elephant herding optimization algorithm [30] was introduced to find the optimal placement and sizing of DGs in distribution systems to improve the voltage profile and stability of system buses, to reduce the system loses, and to maximize the cost-savings.

    PSO [31] was proposed to solve a multi-objective DGs sizing and placement problem for optimizing benefit-cost analysis of their installation and the total power losses during failures in distribution systems. Also, adaptive PSO [32] was introduced to efficiently tackle the problem of simultaneous placement of DG and CB to revamp voltage magnitude and reduce power loses in RDS. A Salp Swarm Algorithm (SSA) [33] was presented to find the optimal allocation of DGs to reduce active power loss and SCBs to improve bus voltages in RDS. Also, SSA [34] was proposed for locating and optimal sizing of DGs and SCBs on RDS for assuring the power quality (PQ) through enhancing the voltage level, minimizing the power losses, and the whole operating cost of the grid.

    A hybrid of weight improved PSO and GSA algorithm [35] was proposed for optimizing the DGs and SCBs problem to achieve the reduction of power loss and enhancement of load ability in RDSs. A hybrid of fuzzy logic controller and ant-lion optimization algorithm with PSO based combination [36] was proposed to allocate the optimal placement of DGs in radial distribution systems. An enhanced genetic algorithm [37] was introduced to identify the optimal location and capacity of the simultaneous DGs/SCBs placement in the radial systems. A spring search algorithm [38] was proposed to find the optimal sizing and placement of SCBs and DGs in RDS with aiming to attain economic, technical, and environmental advantages.

    In this study, a Multiobjective Grey Wolf Optimizer (MOGWO) is proposed to find the optimal DGs and SCBs placement locations and sizing simultaneously in distorted RDSs. The contributions proposed by this study are as follows:

    1. To solve the DGs and SCBs placement problem by considering a a multi-objective method with Pareto fronts solutions.

    2. To investigate the impact of ambient temperature on solving the DGs and SCBs placement problem.

    3. To apply fuzzy set theory to select the optimal solution of non-dominated solutions.

    4. To perform a MOGWO method on standard and real case studies.

    5. To compare the obtained results with the MONSGA and MOPSO methods to show the effectiveness and capability of the proposed method in solving multi-objective problems when considering the temperature.

    The rest of the paper is organized as follows. Section 2 describes a problem formulation which consist of objective functions, constrains, cost and temperature modelling, and a fuzzy decision-making mechanism. Section 3 introduces the proposed solution technique used in this paper. Section 4 describe results and discussion of standard and real case studies. Finally, Section 5 presents the conclusion, acknowledgments, and references.

    The optimizing of DGs and SCBs placement problem involves identifying the number and locations of DGs and SCBs to be placed simultaneously on distorted RDSs. The goals and objectives of this study are to reduce the total power loss (Ploss), enhance the Voltage Stability Index (VSI), balance the loading of the Section Currents Index (SCI), reduce the Total Harmonic Distortion (THD) and maximize the cost-savings. A simplified system model that used for obtaining the objective functions i.e., the total Ploss, VSI, and SCI values is

    Figure 1.  Representation of simple distribution system.

    The optimization problem (f(X)) that need to minimized is

    minf(X)=min[f1(X),f2(X),f3(X)] (1)
    X=(x1,x2,...,xn)Rn
    s.t.{gi(X)0,i=0,...,nhi(X)=0,j=0,...,n

    Here, f1(X)=TotalPloss, f2(X)=SCI, f3(X)=VSI, and X is a solution vector subjected to gi(X) and hi(X) which are inequality and equality constraints, and Rn is the decision variable space.

    The objective functions i.e., the total Ploss, VSI, and SCI were calculated using the load follow studies. A load flow tool called a backward-forward sweep algorithm, was used to compute the fundamental system power loss [39].

    Pfloss=Nbi=2(PgqiPdqiVpiVqiYqicos(δpiδqi+θqi)) (2)

    Here, Pfloss is the total fundamental active system loss, Pgqi is the active power output of the DG at bus qi, Pdqi is the active power demand at bus qi, Vpi and Vqi are the voltages at buses pi and qi, respectively. Nb is the number of system buses, Yqi is the admittance between bus pi and bus qi, δpi is the phase angle of voltage at bus pi, δqi is the phase angle of voltage at bus qi and θqi is the phase angle of Yi (Yqiejθqi)

    The harmonic power flow method [40] was utilized to determine the harmonic power loss for the distorted RDSs:

    Phloss=Hh=2Nb1qi=0Rqi,qi+1[Vhqi+1,1Vhqi][yhqi,qi+1]2, (3)
    TotalPloss=Phloss+Pfloss, (4)

    The system loading is calculated as follows:

    SCI=Nb1k=1|VpiVqiZk|, (5)

    The stability index (SI)can be evaluated as follows [41]:

    SI(qi)=|Vpi|44[Pqi(qi)XkQqi(qi)Rk]24[Pqi(qi)Rk+Qqi(qi)Xk]|Vpi|2, (6)

    SI(qi) should be maximized. Therefore, the voltage stability index (VSI) will be:

    VSI=Nbi=2|1SI(qi)|, (7)

    Equations (4–7), which are used as the objective functions for solving the DGs and SCBs placement problem are subject to the following constraints:

    ● Bus voltage:VminiViVmaxi

    ● Power flow:|PFi|PFmaxi

    ● System power factor: |pfsystem   |pminfall

    ● Installed reactive power: (QTotalC)QTotalL

    ● The DG source utilized must be permissible in the range of the size and power factor: pfDGmin(pfDGqipfDGmax

    ● The THD must be within the allowable limit [42].

    The IEEE 69-bus and 38-bus SEC RDSs (real data) were considered for this study. This was evaluated based on weather information using a least square method in which the power consumption increased by 300 MW per C based on the following equation derived from Figure 2 [43]:

    y=300x+10400, (8)
    Figure 2.  Power consumption vs. ambient temperature in the central region of Saudi Arabia.

    For an IEEE 69-bus RDS with Pd = 3.80 MW, the slope of increasing power consumption can be found by (300× 3.80 MW)/(10.4 GW) = 110 kW/C. Then, the relationship between power consumed by this system and the ambient temperature is

    y=0.110x+3.8, (9)

    For a 38-bus SEC RDS with Pd = 8.34 MW,

    y=0.241x+8.34, (10)

    Pd and y are in MW, and x is in the interval [2550].

    The cost of installation of SCBs and DG can be calculated as follows [44]:

    CSCBs=NSCBsn=1KcQc, (11)
    CDGs=NDGsn=1CDGi+KIDG, (12)

    The cost of power from substation before Cbef.plac and after Caft.plac installation of SCBs and DG can be calculated as follows:

    Cbef.plac=Nyrn=1PWnKEMP×(real(VSIS))bef.×T, (13)
    Caft.plac=Nyrn=1PWnKEMP×(real(VSIS))aft.×T, (14)

    The presenting worth (PW) factor is formulated as follows:

    PW=1+InfR1+IntR, (15)

    The following equations for calculating the cost saving (S) and Benefit-to-Cost Ratio (BCR) are used:

    S=Cbef.plac(Caft.plac+CSCBs+CDGs), (16)
    BCR=SCSCBs   +CDGs, (17)

    CSCBs is the installation cost of SCBs, KC is the cost of commercial SCBs [45], and QC is the injected reactive power in kVAr. CDG is the cost of DG installation, CDGi is the installed capacity of a DG and KIDG is the cost of DG investment ($318,000/MW). KEMP is the energy cost ($49/MWh), Vs is the slack bus voltage, and Is is the current fed from the slack bus to the entire system. PW is the worth factor, InfR is the inflation rate (2.2%), IntR is interest rate (3%), T is 2190 h per year, and Nyr is 10 years.

    A fuzzy decision-maker is utilized to select the optimal solution from the non-dominated solutions of MOGWO output. In the membership function Mki, its largest value is the best optimal non-dominated solution, which computed as follows:

    Mki=mi=1  μkiNndk=1mi=1  μki (18)

    μki is a membership function for each Pareto front solution k, Nnd is the number of non-dominated solutions, and m is the number of targeted objective functions.

    The MOGWO method is suggested to identify the optimal location placement and sizing of DGs and SCBs simultaneously in a distorted RDS. This method mimics the leadership hierarchy and hunting tactic of grey wolves in nature and is guided by paired leaders (α), subordinate wolves (β), subordinate wolves (δ), and scapegoat wolves (ω)[46]. The mathematical model of the suggested approach is as follow:

    D=|CXp(t)X(t)|, (19)
    X(t+1)=|Xp(t)AD|, (20)

    t is the current iteration, A and D are coefficient vectors that help the GWO algorithm to explore and exploit the search space, Xp(t) is the position vector of the prey, and X(t) is the position vector of the grey wolf. The vectors A and C are calculated as follows:

    A=2αr1α, (21)
    C=2r2, (22)

    The α value linearly decreases from 2 to 0 during the course of the iterations and r1 and r2 are random vectors in [0, 1]. The range of C is 2C0 which improves the exploration when C>1 and the exploitation when C<1. The C vector is generated randomly whose aim is to ensure that the exploration/exploitation at any stage can obtain the global optima while avoiding local optima.

    The grey wolf optimizer begins by generating a set of random solutions as the first population. During the optimization, the three best-obtained solutions are saved and treated as α, β, and δ solutions. The next equations (21–23) update α, β, and δ wolf positions from the prey in order to simulate the hunting and discovery of promising areas of the search space:

    Dα=|C1XαX|, (23)
    Dβ=|C2XβX|, (24)
    Dδ=|C3XδX|, (25)

    The positions of wolves in the promising areas of the search space are modelled as follows:

    X1=XαA1Dα, (26)
    X2=XβA2Dβ, (27)
    X3=XδA3Dδ, (28)
    X(t+1)=X1+X2+X33 (29)

    Xα, Xβ, and Xδ indicate wolf positions in the search space. X(t+1) represents the random position of a wolf. The MOGWO is integrated with a repository and leader selection in order to perform the proposed multi-objective optimization. The repository is used to store the best solution (non-dominated solutions) obtained from the optimization algorithm. The leader strategy selects the better α, β, and δ wolves from the repository. Figure 3 shows a flowchart of the MOGWO technique for optimum multi-objective placement and sizing of DGs and SCBs with a fuzzy decision-maker.

    Figure 3.  Flowchart of MOGWO technique with fuzzy decision-maker.

    The injection of harmonic currents is in the order of odd numbers (3–49) at a nominal frequency of 60 Hz, where 15% of each load at each load bus is assumed to be non-linear. The proposed method applied to two cases study with two scenarios as explained in Table 1.

    Table 1.  The scenarios of the study.
    No. Scenario
    1 No consideration of ambient temperature
    2 Consideration of ambient temperature

     | Show Table
    DownLoad: CSV

    A typical 69-bus and 68-branch RDS is an IEEE standard RDS as shown in Figure 4. The system voltage and total load are 12.66 kV and 4.66 MVA, respectively [47].

    Figure 4.  Single line diagram of 69-bus RDS.

    In this scenario, the impact of ambient temperature was not considered. The MONSGA, MOPSO and MOGWO algorithms discussed above were implemented for this system while considering all the objective functions and constraints. Various optimal results such as the optimal location, size, VSI, SCI and THD values, real power losses, reduction cost of power and energy losses and benefit-cost ratio (BCR) before and after installing of DGs and SCBs in the distorted 69-bus RDS were calculated. It can be observed that for the base case (before the installation of DGs and SCBs), the real power loss was 232.92 kW. By applying the MOGWO method, this loss was reduced to 30.13 kW, which was less than that in other methods as shown in Table 3. The VSI, SCI, and THD had permissible values which were 1.058 p.u., 0.331 p.u., and 2.20, respectively. The total annual reduction cost of the power losses was $ 1,265,500 for a cost-saving of $ 1,000,000. It can be seen that the calculated BCR was 1.22.

    Table 3.  Effect of DGs and SCBs placement in a 69-bus distorted RDS compared with different algorithms.
    Method Base Case MONSGA MOPSO Proposed MOGWO
    TNo TWith TNo TWith TNo TWith TNo TWith
    Location bus for DG 0 0 62 61 8 17 54 61
    0 0 61 62 62 63 62 62
    0 0 53 11 19
    Size of DG (MW) 0 0 1.00 1.22 1.01 1.24 0.601 1.15
    0 0 0.80 1.10 1.24 0.762 1.22 1.22
    0 0 0.985 0.672 0.54
    Total MW 0 0 2.79 2.32 2.93 2.00 2.58 2.37
    Location bus for SCB 0 0 37 32 8 62 46 61
    0 0 61 34 40 63 37 65
    0 0 28 17 62
    Size of SCB (MVAr) 0 0 1.10 0.184 2.20 0.615 0.470 2.07
    0 0 1.17 1.122 0.150 0.710 0.841 0.370
    0 0 0.448 0.550 1.25
    Total MVAr 0 0 2.71 1.406 2.90 1.33 2.56 2.443
    Max. voltage (p.u) 1.032 1.026 1.01 1.02 1.004 1.01 1.01 1.01
    Min. voltage (p.u) 0.9438 0.9010 0.980 0.9537 0.9758 0.9386 0.9863 0.9689
    VSI (p.u) 1.283 1.581 1.092 1.16 1.102 1.30 1.058 1.15
    SCI (p.u) 0.728 0.976 0.310 0.611 0.327 0.615 0.331 0.449
    THD 9.92 9.23 1.96 6.10 1.82 4.28 2.20 2.95
    Power losses (kW) 232.9 438.48 31.43 155.73 69.19 152.87 30.13 105
    Reduction (%) 0 0 87.54 64.50 70.30 65.14 87.1 76.10
    DG cost ($K/y) 0 0 887.790 738.480 930.970 635.810 820.460 752.520
    SCB cost ($K/y) 0 0 3.522 2.384 3.518 2.414 3.444 2.483
    Power cost ($M/y) 3.100 3.980 1.159 2.715 1.062 2.795 1.266 2.196
    Benefit ($M/y) 0 0 1.045 0.522 1.090 0.544 1.0 1.027
    BCR 0 0 1.17 0.70 1.18 0.85 1.22 1.36

     | Show Table
    DownLoad: CSV

    The impact of ambient temperature on this system was studied using Eq 9. One observes that in the base case, the real power loss was 438.48 kW and reduced to 105 kW after applying the MOGWO method which was less than in the other methods as shown in Table 3. The performance of the MOGWO method was compared to other methods through its elapsed time needed to converge as explained in Table 2 for both scenarios. The indices of VSI, SCI, and THD had permissible values comparing to other methods; these values were 1.15 p.u., 0.449 p.u., and 2.95, respectively. Figure 5 shows the voltage profile of this system. The figure compares the magnitudes of the voltage profile before and after installing DGs and SCBs for both scenarios. In the base case, the voltage profile was poor, which violates the lower limit of the voltage constraint. However, this voltage profile was improved after the installation of DGs and SCBs. Figure 6 shows VSI values on buses of the system, and Figure 7 shows the section current for also both scenarios. Almost all currents in all sections were decreased after installing of DGs and SCBs. The total annual reduction cost of the power losses was $ 2,195,500 for a cost-saving of $ 1,026,800. The calculated BCR was 1.36.

    Table 2.  The elapsed time of different algorithms in Seconds.
    Scenario MONSGA MOPSO Proposed MOGWO
    No Temperature (TNo) 621.38 584.76 565.18
    With Temperature (TWith) 673 648.92 589.78

     | Show Table
    DownLoad: CSV
    Figure 5.  Voltage profile for 69-bus distorted RDS.
    Figure 6.  VSI for 69-bus distorted RDS.
    Figure 7.  SCI for 69-bus distorted RDS.

    A typical 38-bus and 37-branch RDS as shown in Figure 8, is a SEC distribution system. The system voltage and total load are 13.8 kV and 8.77 MVA, respectively.

    Figure 8.  Single line diagram of 38-bus RDS.

    The MONSGA, MOPSO and MOGWO algorithms were also implemented for this system while considering all the objective functions and constraints. Various optimal results such as the optimal location, size, VSI, SCI and THD values, real power losses, reduction cost of power and energy losses and BCR before and after installing of DGs and SCBs in the distorted 38-bus RDS were calculated. It can be observed that for the base case (before the installation of DGs and SCBs), the real power loss was 194.11 kW as shown in Table 5. By applying the MOGWO method, this loss was reduced to 128.12 kW, which was less than that in other methods. The VSI, SCI, and THD had permissible values which were 1.10 p.u., 0.220 p.u., and 2.29, respectively. The total annual reduction cost of the power losses was $ 2,298,500 for a cost-saving of $ 473,030. It can be seen that the calculated BCR was 1.46

    Table 5.  Effect of DGs and SCBs placement in a 38-bus distorted RDS compared with different algorithms.
    Method Base Case MONSGA MOPSO Proposed MOGWO
    TNo TWith TNo TWith TNo TWith TNo TWith
    Location bus for DG 0 0 27 27 6 6 25 24
    0 0 21 26 27 22 26 26
    Size of DG (MW) 0 0 0.509 0.415 0.505 0.509 0.509 0.509
    0 0 0.4710 0.368 0.509 0.509 0.505 0.509
    Total MW 0 0 0.976 0.783 1.13 1.017 1.13 1.017
    Location bus for SCB 0 0 12 23 4 25 33 33
    0 0 23 17 38 34 21 21
    Size of SCB (MVAr) 0 0 0.962 1.434 1.64 1.63 0.824 0.98
    0 0 1.700 1.310 1.10 0.960 1.54 1.70
    Total MVAr 0 0 2.66 2.744 2.74 2.59 2.36 2.68
    Max. voltage (p.u) 1 1 1 1 1 1 1 1
    Min. voltage (p.u) 0.9603 0.9466 0.9765 0.9666 0.9764 0.9690 0.9764 0.9687
    VSI (p.u) 1.19 1.27 1.10 1.16 1.10 1.14 1.10 1.14
    SCI (p.u) 0.314 0.425 0.218 0.327 0.213 0.30 0.220 0.31
    THD 3.17 3.97 2.41 3.31 2.47 3.43 2.29 3.05
    Power losses (kW) 194.11 403.83 132 318.76 128.36 304.18 128.12 300
    Reduction (%) 0 0 32 21.1 34.24 24.68 33..95 25.6
    DG cost ($K/y) 0 0 310.030 248.980 322.360 323.410 320.087 323.410
    SCB cost ($K/y) 0 0 2.576 2.581 2.522 2.558 2.576 2.576
    Power cost ($M/y) 3.900 5.470 2.416 4.222 2.439 4 2.197 3.943
    Benefit ($M/y) 0 0 0.365 1 0.331 1.155 0.473 1.204
    BCR 0 0 1.17 3.97 1.02 3.54 1.46 3.70

     | Show Table
    DownLoad: CSV

    In the same way, the temperature effects on this system were studied using Eq 10. In the base case, the real power loss was 403.83 kW and reduced to 300 kW after applying the MOGWO method which was less than in the other methods as shown in Table 5. The performance of the MOGWO method was compared to other methods through its elapsed time needed to converge as explained in Table 4 for both scenarios. The indices of VSI, SCI, and THD had permissible values comparing to other methods; these values were 1.11 p.u., 0.310 p.u., and 3.05, respectively. Figure 9 shows the voltage profile of this system. The figure compares the magnitudes of the voltage profile before and after installing DGs and SCBs for both scenarios. In the base case, the voltage profile was poor, which violates the lower limit of the voltage constraint. However, this voltage profile was improved after the installation of DGs and SCBs. Figure 10 shows VSI values on buses of the system, and Figure 11 shows the section current for also both scenarios. Almost all currents in all sections were decreased after installing of DGs and SCBs. The total annual reduction cost of the power losses was $ 3.942,600 for a cost-saving of $ 1,204,100. The calculated BCR was 3.70.

    Table 4.  The elapsed time of different algorithms in Seconds.
    Scenario MONSGA MOPSO Proposed MOGWO
    No Temperature (TNo) 243.65 238.26 221.34
    With Temperature (TWith) 296.52 289.28 269.98

     | Show Table
    DownLoad: CSV
    Figure 9.  Voltage profile for 38-bus distorted RDS.
    Figure 10.  VSI for 38-bus distorted RDS.
    Figure 11.  SCI for 38-bus distorted RDS.

    The MOGWO technique was proposed to identify the best candidate locations and optimum sizing of DGs and SCBs simultaneously under the impact of ambient temperature. The power loss reduction, voltage stability improvement of the system nodes, and load balancing in the sections were simulated as a multi-objective function. The best optimal solution was chosen via a fuzzy-based mechanism. The proposed method was applied to an IEEE 69-bus and 38-bus RDSs (real data from SEC). The obtained results were compared with the MONSGA and MOPSO methods, and showed better results in terms of system power loss reduction, voltage stability and voltage profile improvement, THD level, and load balancing. Additionally, economic benefits such as cost savings and BCR were evaluated. It is recommended that the power utilities adopt this technique to improve their technical capability and financial figures.

    The authors extend their appreciation to the Deanship of Scientific Research, King Saud University for funding this work through research group no. (RG-1439-028).

    The authors declare no conflict of interest in this paper.



    [1] Marrone R, Salzano A, Di Francia A, et al. (2020) Effects of feeding and maturation system on qualitative characteristics of buffalo meat (Bubalus bubalis). Animals 10: 899. https://doi.org/10.3390/ani10050899 doi: 10.3390/ani10050899
    [2] Kandeepan G, Mendiratta S, Shukla V, et al. (2013) Processing characteristics of buffalo meat-a review. J Meat Sci Technol 1: 01–11.
    [3] Anjaneyulu A, Thomas R, Kondaiah N (2007) Technologies for value added buffalo meat products-a review. Am J Food Technol 2: 104–114. https://doi.org/10.3923/ajft.2007.104.114 doi: 10.3923/ajft.2007.104.114
    [4] Yousefi M, Azizi M, Mohammadifar MA, et al. (2018) Antimicrobial coatings and films on meats: A perspective on the application of antimicrobial edible films or coatings on meats from the past to future. Bali Med J 7: 87–96. https://doi.org/10.15562/bmj.v7i1.759 doi: 10.15562/bmj.v7i1.759
    [5] Pateiro M, Munekata PE, Sant'Ana AS, et al. (2021) Application of essential oils as antimicrobial agents against spoilage and pathogenic microorganisms in meat products. Int J Food Microbiol 337: 108966. https://doi.org/10.1016/j.ijfoodmicro.2020.108966 doi: 10.1016/j.ijfoodmicro.2020.108966
    [6] Shaltout FA, Koura HA (2017) Impact of some essential oils on the quality aspect and shelf life of meat. Benha Vet Med J 33: 351–364. https://doi.org/10.21608/bvmj.2017.30503 doi: 10.21608/bvmj.2017.30503
    [7] Petrou S, Tsiraki M, Giatrakou V, et al. (2012) Chitosan dipping or oregano oil treatments, singly or combined on modified atmosphere packaged chicken breast meat. Int J Food Microbiol 156: 264–271. https://doi.org/10.1016/j.ijfoodmicro.2012.04.002 doi: 10.1016/j.ijfoodmicro.2012.04.002
    [8] Noshad M, Alizadeh Behbahani B, Jooyandeh H, et al. (2021) Utilization of Plantago major seed mucilage containing Citrus limon essential oil as an edible coating to improve shelf‐life of buffalo meat under refrigeration conditions. Food Sci Nutr 9: 1625–1639. https://doi.org/10.1002/fsn3.2137 doi: 10.1002/fsn3.2137
    [9] Sánchez-Ortega I, García-Almendárez BE, Santos-López EM, et al. (2014) Antimicrobial edible films and coatings for meat and meat products preservation. Sci World J 2014: 248935. https://doi.org/10.1155/2014/248935 doi: 10.1155/2014/248935
    [10] Barbosa LN, Rall VLM, Fernandes AAH, et al. (2009) Essential oils against foodborne pathogens and spoilage bacteria in minced meat. Foodborne Pathog Dis 6: 725–728. https://doi.org/10.1089/fpd.2009.0282 doi: 10.1089/fpd.2009.0282
    [11] Bellik Y (2014) Total antioxidant activity and antimicrobial potency of the essential oil and oleoresin of Zingiber officinale Roscoe. Asian Pac J Trop Dis 4: 40–44. https://doi.org/10.1016/S2222-1808(14)60311-X doi: 10.1016/S2222-1808(14)60311-X
    [12] Gedikoğlu A, Sökmen M, Çivit A (2019) Evaluation of Thymus vulgaris and Thymbra spicata essential oils and plant extracts for chemical composition, antioxidant, and antimicrobial properties. Food Sci Nutr 7: 1704–1714. https://doi.org/10.1002/fsn3.1007 doi: 10.1002/fsn3.1007
    [13] Beristain-Bauza SDC, Hernández-Carranza P, Cid-Pérez TS, et al. (2019) Antimicrobial activity of ginger (Zingiber officinale) and its application in food products. Food Rev Int 35: 407–426. https://doi.org/10.1080/87559129.2019.1573829 doi: 10.1080/87559129.2019.1573829
    [14] He J, Hadidi M, Yang S, et al. (2023) Natural food preservation with ginger essential oil: biological properties and delivery systems. Food Res Int 173: 113221. https://doi.org/10.1016/j.foodres.2023.113221 doi: 10.1016/j.foodres.2023.113221
    [15] Xi B, Gao Y, Guo T, et al. (2020) Study on preservation of chilled beef with natural essential oil nanocapsules. J Chem 2020: 8123254. https://doi.org/10.1155/2020/8123254 doi: 10.1155/2020/8123254
    [16] Nieto G (2020) A review on applications and uses of thymus in the food industry. Plants 9: 961. https://doi.org/10.3390/plants9080961 doi: 10.3390/plants9080961
    [17] Posgay M, Greff B, Kapcsándi V, et al. (2022) Effect of Thymus vulgaris L. essential oil and thymol on the microbiological properties of meat and meat products: A review. Heliyon 8: e10812. https://doi.org/10.1016/j.heliyon.2022.e10812 doi: 10.1016/j.heliyon.2022.e10812
    [18] Vital ACP, Guerrero A, Monteschio JdO, et al. (2016) Effect of edible and active coating (with rosemary and oregano essential oils) on beef characteristics and consumer acceptability. PloS one 11: e0160535. https://doi.org/10.1371/journal.pone.0160535 doi: 10.1371/journal.pone.0160535
    [19] Higueras L, López-Carballo G, Hernández-Muñoz P, et al. (2013) Development of a novel antimicrobial film based on chitosan with LAE (ethyl-Nα-dodecanoyl-L-arginate) and its application to fresh chicken. Int J Food Microbiol 165: 339–345. https://doi.org/10.1016/j.ijfoodmicro.2013.06.003 doi: 10.1016/j.ijfoodmicro.2013.06.003
    [20] Ng ZJ, Zarin MA, Lee CK, Tan JS (2020) Application of bacteriocins in food preservation and infectious disease treatment for humans and livestock: a review. RSC Adv 10: 38937–38964. https://doi.org/10.1039/D0RA06161A doi: 10.1039/D0RA06161A
    [21] Behbahani BA, Shahidi F, Yazdi FT, et al. (2017) Use of Plantago major seed mucilage as a novel edible coating incorporated with Anethum graveolens essential oil on shelf life extension of beef in refrigerated storage. Int J Biol Macromol 94: 515–526. https://doi.org/10.1016/j.ijbiomac.2016.10.055 doi: 10.1016/j.ijbiomac.2016.10.055
    [22] Nunes C, Silva M, Farinha D, et al. (2023) Edible coatings and future trends in active food packaging–fruits' and traditional sausages' shelf life increasing. Foods 12: 3308. https://doi.org/10.3390/foods12173308 doi: 10.3390/foods12173308
    [23] Musalem S, Hamdy MM, Mashaly MM, et al. (2024) Extending the shelf-life of refrigerated chicken fillets by using polymeric coating layer composed of ginger Zingiber officinale essential oil loaded-chitosan nanoparticles. J Umm Al-Qura Univ Appl Sci 2024: 1–14. https://doi.org/10.1007/s43994-024-00172-8 doi: 10.1007/s43994-024-00172-8
    [24] Khaledian Y, Pajohi-Alamoti M, Bazaegani-Gilani B (2019) Development of cellulose nanofibers coating incorporated with ginger essential oil and citric acid to extend the shelf life of ready‐to‐cook barbecue chicken. J Food Process Preserv 43: e14114. https://doi.org/10.1111/jfpp.14114 doi: 10.1111/jfpp.14114
    [25] Zhang B, Liu Y, Wang H, et al. (2021) Effect of sodium alginate-agar coating containing ginger essential oil on the shelf life and quality of beef. Food Control 130: 108216. https://doi.org/10.1016/j.foodcont.2021.108216 doi: 10.1016/j.foodcont.2021.108216
    [26] Ricardo-Rodrigues S, Rouxinol MI, Agulheiro-Santos AC, et al. (2024) The antioxidant and antibacterial potential of thyme and clove essential oils for meat preservation—An overview. Appl Biosci 3: 87–101. https://doi.org/10.3390/applbiosci3010006 doi: 10.3390/applbiosci3010006
    [27] Al-Moghazy M, El-sayed HS, Salama HH, et al. (2021) Edible packaging coating of encapsulated thyme essential oil in liposomal chitosan emulsions to improve the shelf life of Karish cheese. Food Biosci 43: 101230. https://doi.org/10.1016/j.fbio.2021.101230 doi: 10.1016/j.fbio.2021.101230
    [28] Abd Aziz NA, Hassan J, Osman NH, et al. (2017) Extraction of essential oils from Zingiberaceace Famili by using Solvent-free Microwave Extraction (SFME), Microwave-assisted Extraction (MAE) and Hydrodistillation (HD). Asian J Appl Sci 5: 97–100.
    [29] Lucchesi ME, Smadja J, Bradshaw S, et al. (2007) Solvent free microwave extraction of Elletaria cardamomum L.: A multivariate study of a new technique for the extraction of essential oil. J Food Eng 79: 1079–1086. https://doi.org/10.1016/j.jfoodeng.2006.03.029 doi: 10.1016/j.jfoodeng.2006.03.029
    [30] Collado L, Mabesa L, Oates C, et al. (2001) Bihon‐type noodles from heat‐moisture‐treated sweet potato starch. J Food Sci 66: 604–609. https://doi.org/10.1111/j.1365-2621.2001.tb04608.x doi: 10.1111/j.1365-2621.2001.tb04608.x
    [31] Müller CM, Yamashita F, Laurindo JB (2008) Evaluation of the effects of glycerol and sorbitol concentration and water activity on the water barrier properties of cassava starch films through a solubility approach. Carbohydr Polym 72: 82–87. https://doi.org/10.1016/j.carbpol.2007.07.026 doi: 10.1016/j.carbpol.2007.07.026
    [32] Ballesteros-Mártinez L, Pérez-Cervera C, Andrade-Pizarro R (2020) Effect of glycerol and sorbitol concentrations on mechanical, optical, and barrier properties of sweet potato starch film. NFS journal 20: 1–9. https://doi.org/10.1016/j.nfs.2020.06.002 doi: 10.1016/j.nfs.2020.06.002
    [33] Fernández-Pan I, Carrión-Granda X, Maté JI (2014) Antimicrobial efficiency of edible coatings on the preservation of chicken breast fillets. Food Control 36: 69–75. https://doi.org/10.1016/j.foodcont.2013.07.032 doi: 10.1016/j.foodcont.2013.07.032
    [34] Yoon D-k, Kim J-H, Cho W-Y, et al. (2018) Effect of allium hookeri root on physicochemical, lipid, and protein oxidation of longissimus dorsi muscle meatball. Korean J Food Sci Anim Resour 38: 1203. https://doi.org/10.5851/kosfa.2018.e53 doi: 10.5851/kosfa.2018.e53
    [35] Zhang H, Liang Y, Li X, et al. (2020) Effect of chitosan-gelatin coating containing nano-encapsulated tarragon essential oil on the preservation of pork slices. Meat Sci 166: 108137. https://doi.org/10.1016/j.meatsci.2020.108137 doi: 10.1016/j.meatsci.2020.108137
    [36] Anandh MA, Lakshmanan V (2014) Storage stability of smoked buffalo rumen meat product treated with ginger extract. J Food Sci Technol 51: 1191–1196. https://doi.org/10.1007/s13197-012-0622-2 doi: 10.1007/s13197-012-0622-2
    [37] Shadman S, Hosseini SE, Langroudi HE, et al. (2017) Evaluation of the effect of a sunflower oil-based nanoemulsion with Zataria multiflora Boiss. essential oil on the physicochemical properties of rainbow trout (Oncorhynchus mykiss) fillets during cold storage. LWT-Food Sci Technol 79: 511–517. https://doi.org/10.1016/j.lwt.2016.01.073 doi: 10.1016/j.lwt.2016.01.073
    [38] Marcinkowska-Lesiak M, Onopiuk A, Wojtasik-Kalinowska I, et al. (2020) The influence of sage and hemp oils addition to gelatin-based edible coating on the quality features of pork. CyTA-J Food 18: 719–727. https://doi.org/10.1080/19476337.2020.1836027 doi: 10.1080/19476337.2020.1836027
    [39] Aykın-Dinçer E, Erbaş M (2020) Effect of packaging method and storage temperature on quality properties of cold-dried beef slices. LWT-Food Sci Technol 124: 109171. https://doi.org/10.1016/j.lwt.2020.109171 doi: 10.1016/j.lwt.2020.109171
    [40] Fernández-López J, Sayas-Barberá E, Muñoz T, et al. (2008) Effect of packaging conditions on shelf-life of ostrich steaks. Meat Sci 78: 143–152. https://doi.org/10.1016/j.meatsci.2007.09.003 doi: 10.1016/j.meatsci.2007.09.003
    [41] Alizadeh Behbahani B, Falah F, Vasiee A, et al. (2021) Control of microbial growth and lipid oxidation in beef using a Lepidium perfoliatum seed mucilage edible coating incorporated with chicory essential oil. Food Sci Nutr 9: 2458–2467. https://doi.org/10.1002/fsn3.2186 doi: 10.1002/fsn3.2186
    [42] Koirala B, Bhattarai R, Maharjan R, et al. (2020) Bacterial Assessment of Buffalo Meat in Kathmandu Valley. Nepal J Sci Technol 19: 90–96. https://doi.org/10.3126/njst.v20i1.39438 doi: 10.3126/njst.v20i1.39438
    [43] Nemati Z, Barzegar R, Khosravinezhad M, et al. (2018) Chemical composition and antioxidant activity of Shirazi Thymus vulgaris essential oil. Adv Herb Med 4: 26–32.
    [44] Aljabeili HS, Barakat H, Abdel-Rahman HA (2018) Chemical composition, antibacterial and antioxidant activities of thyme essential oil (Thymus vulgaris). Food Nutr Sci 9: 433–446. https://doi.org/10.4236/fns.2018.95034 doi: 10.4236/fns.2018.95034
    [45] Galovičová L, Borotová P, Valková V, et al. (2021) Thymus vulgaris essential oil and its biological activity. Plants 10: 1959. https://doi.org/10.3390/plants10091959 doi: 10.3390/plants10091959
    [46] Oforma C, Udourioh G, Ojinnaka C (2019) Characterization of essential oils and fatty acids composition of stored ginger (Zingiber officinale Roscoe). J Appl Sci Environ Manag 23: 2231–2238. https://doi.org/10.4314/jasem.v23i12.22 doi: 10.4314/jasem.v23i12.22
    [47] Dhanik J, Verma A, Arya N, et al. (2017) Chemical profiling and antioxidant activity of essential oil of Zingiber officinale Roscoe from two different altitudes of Uttarakhand. J Essent Oil Bear Plants 20: 1547–1556. https://doi.org/10.1080/0972060X.2017.1417747 doi: 10.1080/0972060X.2017.1417747
    [48] Shirooye P, Mokaberinejad R, Ara L, et al. (2016) Volatile constituents of ginger oil prepared according to Iranian traditional medicine and conventional method: A comparative study. Afr J Tradit Complement Altern Med 13: 68–73. https://doi.org/10.21010/ajtcam.v13i6.11 doi: 10.21010/ajtcam.v13i6.11
    [49] Wang X, Shen Y, Thakur K, et al. (2020) Antibacterial activity and mechanism of ginger essential oil against Escherichia coli and Staphylococcus aureus. Molecules 25: 3955. https://doi.org/10.3390/molecules25173955 doi: 10.3390/molecules25173955
    [50] Palotás P, Palotás P, Jónás G, et al. (2020) Preservative effect of novel combined treatment with electrolyzed active water and lysozyme enzyme to increase the storage life of vacuum-packaged carp. J Food Qual 2020: 1–7. https://doi.org/10.1155/2020/4861471 doi: 10.1155/2020/4861471
    [51] Harpaz S, Glatman L, Drabkin V, et al. (2003) Effects of herbal essential oils used to extend the shelf life of freshwater-reared Asian sea bass fish (Lates calcarifer). J Food Prot 66: 410–417. https://doi.org/10.4315/0362-028X-66.3.410 doi: 10.4315/0362-028X-66.3.410
    [52] Khare AK, Abraham RJ, Rao VA, et al. (2017) Effect of Chitosan and Cinnamon oil edible coating on shelf life of chicken fillets under refrigeration conditions. Indian J Anim Res 51: 603–610. https://doi.org/10.18805/ijar.v0iOF.7834 doi: 10.18805/ijar.v0iOF.7834
    [53] Gutierrez J, Barry-Ryan C, Bourke P (2008) The antimicrobial efficacy of plant essential oil combinations and interactions with food ingredients. Int J Food Microbiol 124: 91–97. https://doi.org/10.1016/j.ijfoodmicro.2008.02.028 doi: 10.1016/j.ijfoodmicro.2008.02.028
    [54] Gholami‐Ahangaran M, Ahmadi‐Dastgerdi A, Azizi S, et al. (2022) Thymol and carvacrol supplementation in poultry health and performance. Vet Med Sci 8: 267–288. https://doi.org/10.1002/vms3.663 doi: 10.1002/vms3.663
    [55] Kachur K, Suntres Z (2020) The antibacterial properties of phenolic isomers, carvacrol and thymol. Crit Rev Food Sci Nutr 60: 3042–3053. https://doi.org/10.1080/10408398.2019.1675585 doi: 10.1080/10408398.2019.1675585
    [56] Majzoobi M, Pesaran Y, Mesbahi G, et al. (2015) Physical properties of biodegradable films from heat‐moisture‐treated rice flour and rice starch. Starch‐Stärke 67: 1053–1060. https://doi.org/10.1002/star.201500102 doi: 10.1002/star.201500102
    [57] Rompothi O, Pradipasena P, Tananuwong K, et al. (2017) Development of non-water soluble, ductile mung bean starch based edible film with oxygen barrier and heat sealability. Carbohydr Polym 157: 748–756. https://doi.org/10.1016/j.carbpol.2016.09.007 doi: 10.1016/j.carbpol.2016.09.007
    [58] Ahmed LI, Ibrahim N, Abdel-Salam AB, et al. (2021) Potential application of ginger, clove and thyme essential oils to improve soft cheese microbial safety and sensory characteristics. Food Biosci 42: 101177. https://doi.org/10.1016/j.fbio.2021.101177 doi: 10.1016/j.fbio.2021.101177
    [59] Salem AM, Amin RA, Afifi GS (2010) Studies on antimicrobial and antioxidant efficiency of some essential oils in minced beef. J Am Sci 6: 691–700.
    [60] Ghimire A, Paudel N, Poudel R (2022) Effect of pomegranate peel extract on the storage stability of ground buffalo (Bubalus bubalis) meat. LWT 154: 112690. https://doi.org/10.1016/j.lwt.2021.112690 doi: 10.1016/j.lwt.2021.112690
    [61] Bekhit AE-DA, Holman BW, Giteru SG, et al. (2021) Total volatile basic nitrogen (TVB-N) and its role in meat spoilage: A review. Trends Food Sci Technol 109: 280–302. https://doi.org/10.1016/j.tifs.2021.01.006 doi: 10.1016/j.tifs.2021.01.006
    [62] Bermúdez-Oria A, Rodríguez-Gutiérrez G, Rubio-Senent F, et al. (2019) Effect of edible pectin-fish gelatin films containing the olive antioxidants hydroxytyrosol and 3, 4-dihydroxyphenylglycol on beef meat during refrigerated storage. Meat Sci 148: 213–218. https://doi.org/10.1016/j.meatsci.2018.07.003 doi: 10.1016/j.meatsci.2018.07.003
    [63] Alsaraf S, Hadi Z, Al-Lawati WM, et al. (2020) Chemical composition, in vitro antibacterial and antioxidant potential of Omani Thyme essential oil along with in silico studies of its major constituent. J King Saud Univ-Sci 32: 1021–1028. https://doi.org/10.1016/j.jksus.2019.09.006 doi: 10.1016/j.jksus.2019.09.006
    [64] Mutlu‐Ingok A, Catalkaya G, Capanoglu E, et al. (2021) Antioxidant and antimicrobial activities of fennel, ginger, oregano and thyme essential oils. Food Frontiers 2: 508–518. https://doi.org/10.1002/fft2.77 doi: 10.1002/fft2.77
    [65] Makanjuola SA, Enujiugha VN, Omoba OS, et al. (2015) Optimization and prediction of antioxidant properties of a tea‐ginger extract. Food Sci Nutr 3: 443–452. https://doi.org/10.1002/fsn3.237 doi: 10.1002/fsn3.237
    [66] Thirathumthavorn D, Charoenrein S, Krochta J (2010) Influence of glass transition on oxygen permeability of starch-based edible films. Water Properties in Food, Health, Pharmaceutical and Biological Systems: ISOPOW 10: 641–646. https://doi.org/10.1002/9780470958193.ch61 doi: 10.1002/9780470958193.ch61
    [67] Akacha BB, Švarc-Gajić J, Elhadef K, et al. (2022) The essential oil of tunisian halophyte Lobularia maritima: A natural food preservative agent of ground beef meat. Life 12: 1571. https://doi.org/10.3390/life12101571 doi: 10.3390/life12101571
    [68] Seideman, Cross HR, Smith GC, et al. (1984) Factors associated with fresh meat color: A review. J Food Qual 6: 211–237. https://doi.org/10.1111/j.1745-4557.1984.tb00826.x doi: 10.1111/j.1745-4557.1984.tb00826.x
    [69] Motoyama M, Kobayashi M, Sasaki K, et al. (2010) Pseudomonas spp. convert metmyoglobin into deoxymyoglobin. Meat Sci 84: 202–207. https://doi.org/10.1016/j.meatsci.2009.08.050 doi: 10.1016/j.meatsci.2009.08.050
    [70] Dobbelstein S (2005) Relationship between beef colour and myoglobin. Agrotechnol Food Innovations 474: 3–54.
    [71] Guyon C, Meynier A, de Lamballerie M (2016) Protein and lipid oxidation in meat: A review with emphasis on high-pressure treatments. Trends Food Sci Technol 50: 131–143. https://doi.org/10.1016/j.tifs.2016.01.026 doi: 10.1016/j.tifs.2016.01.026
    [72] Jaspal MH, Badar IH, Usman Ghani M, et al. (2022) Effect of Packaging Type and Aging on the Meat Quality Characteristics of Water Buffalo Bulls. Animals 12: 130. https://doi.org/10.3390/ani12020130 doi: 10.3390/ani12020130
    [73] Jouki M, Yazdi FT, Mortazavi SA, et al. (2014) Effect of quince seed mucilage edible films incorporated with oregano or thyme essential oil on shelf life extension of refrigerated rainbow trout fillets. Int J Food Microbiol 174: 88–97. https://doi.org/10.1016/j.ijfoodmicro.2014.01.001 doi: 10.1016/j.ijfoodmicro.2014.01.001
    [74] Mahboob S, Al-Ghanim K, Al-Balawi H, et al. (2018) Study on assessment of proximate composition and meat quality of fresh and stored Clarias gariepinus and Cyprinus carpio. Braz J Biol 79: 651–658. https://doi.org/10.1590/1519-6984.187647 doi: 10.1590/1519-6984.187647
    [75] Shukla V, Kandeepan G, Vishnuraj M (2015) Evaluation of shelf life of buffalo meat in aerobic cold storage using physicochemical parameters. Buff Bull 34: 453–457.
    [76] Alam J, Murshed HM, Rahman SME, et al. (2017) Effect of chitosan on quality and shelf life of beef at refrigerated storage. Bang J Anim Sci 46: 230–238. https://doi.org/10.3329/bjas.v46i4.36963 doi: 10.3329/bjas.v46i4.36963
    [77] Ghollasi-Mood F, Mohsenzadeh M, Housaindokht MR, et al. (2016) Microbial and chemical spoilage of chicken meat during storage at isothermal and fluctuation temperature under aerobic conditions. Iran J Vet Sci Technol 8: 38–46.
    [78] Kandeepan G, Biswas S (2007) Effect of low temperature preservation on quality and shelf life of buffalo meat. Am J Food Technol 2: 126–135. https://doi.org/10.3923/ajft.2007.126.135 doi: 10.3923/ajft.2007.126.135
    [79] Amariei S, Poroch-Seriţan M, Gutt G, et al. (2016) Rosemary, thyme and oregano essential oils influence on physicochemical properties and microbiological stability of minced meat. J Microbiol Biotechnol Food Sci 6: 670–676. https://doi.org/10.15414/jmbfs.2016.6.1.670-676 doi: 10.15414/jmbfs.2016.6.1.670-676
    [80] Khare AK, Abraham RJ, Rao VA, et al. (2016) Utilization of carrageenan, citric acid and cinnamon oil as an edible coating of chicken fillets to prolong its shelf life under refrigeration conditions. Veterinary World 9: 166. https://doi.org/10.14202/vetworld.2016.166-175 doi: 10.14202/vetworld.2016.166-175
    [81] Strange E, Benedict R, Smith J, et al. (1977) Evaluation of rapid tests for monitoring alterations in meat quality during storage. J Food Prot 40: 843–847. https://doi.org/10.4315/0362-028X-40.12.843 doi: 10.4315/0362-028X-40.12.843
    [82] Shelef L (1974) Hydration and pH of microbially spoiling beef. J Appl Bacteriol 37: 531–536. https://doi.org/10.1111/j.1365-2672.1974.tb00478.x doi: 10.1111/j.1365-2672.1974.tb00478.x
    [83] Maqsood S, Manheem K, Gani A, et al. (2018) Degradation of myofibrillar, sarcoplasmic and connective tissue proteins by plant proteolytic enzymes and their impact on camel meat tenderness. J Food Sci Technol 55: 3427–3438. https://doi.org/10.1007/s13197-018-3251-6 doi: 10.1007/s13197-018-3251-6
  • This article has been cited by:

    1. Waleed Fadel, A Review of Distributed Generation Optimization with Shunt Capacitors in Reconfigured Distribution Networks, 2024, 136, 0929-6212, 1, 10.1007/s11277-024-11062-x
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(974) PDF downloads(85) Cited by(0)

Figures and Tables

Figures(1)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog