# Electrical Impedance Spectroscopy for Quality Assessment of Meat and Fish: A Review on Basic Principles, Measurement Methods, and Recent Advances

Authors: Xin Zhao; Hong Zhuang; Seung-Chul Yoon; Yonggui Dong; Wei Wang; Wei Zhao
Journal: Journal of Food Quality
 (2017)

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## Abstract

Electrical impedance spectroscopy (EIS), as an effective analytical technique for electrochemical system, has shown a wide application for food quality and safety assessment recently. Individual differences of livestock cause high variation in quality of raw meat and fish and their commercialized products. Therefore, in order to obtain the definite quality information and ensure the quality of each product, a fast and on-line detection technology is demanded to be developed to monitor product processing. EIS has advantages of being fast, nondestructive, inexpensive, and easily implemented and shows potential to develop on-line detecting instrument to replace traditional methods to realize time, cost, skilled persons saving and further quality grading. This review outlines the fundamental theories and two common measurement methods of EIS applied to biological tissue, summarizes its application specifically for quality assessment of meat and fish, and discusses challenges and future trends of EIS technology applied for meat and fish quality assessment.


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## Body

## 1. Introduction

Electrical impedance spectroscopy (EIS) is a method to analyze electrical properties of materials and systems by inducing alternating electrical signals at different frequencies into them and measuring the responding signals [1]. A function of impedance according to frequencies is established and further correlated with physical parameters or properties of materials and systems, for the aim of analysis and evaluation. EIS was originally applied in research on electrochemical system, and, from 1920s, it began to be used for biological systems. Until now EIS has had an extensive application in biological research. According to the biological objects, application of EIS can be divided into three aspects, that is, electrical impedance tomography in medical imaging [2–4], quality and safety assessment in food industry, and phytophysiology in agronomy [5–7]. Research objects and targets of EIS applied in food are abundant and extensive, including for fruits, such as study on dry matter content of durian [8] and ripening of banana [9], for vegetables, such as changes in potato and spinach tissues during or after heating [10, 11] and moisture content of carrot slices during drying [12], for meat, such as quality evaluation of pork meat during storage [13] and investigation of beef meat behavior during ageing [14], for chicken, such as discrimination of fresh and frozen-thawed chicken breast muscles [15], for fish, such as salt and moisture content determination of salted rainbow trout [16] and freshness estimation of carp [17], for dairy products, such as real-time detection of bovine milk adulteration [18], and moreover for determination of the additives content in natural juices [19], fermentation process of bread dough [20], and quality assessment of cooking oil [21].Nowadays customers pay more attentions on quality attributes of meat products, such as appearance, flavor, and nutrients. However due to individual differences of livestock, there is a broad-ranging variability in quality of the raw meat, which also causes high variation in the commercialized end products. Customers and manufacturers expect that products can be further graded according to quality, in order to gain more benefit. Thus there is a strong demand for manufacturers to assess the quality of every product on-line to monitor processing, in order to obtain reliable and definite quality information for customers and further processing manufacturers [22]. It also can help to determine the best destination for meat carcasses and to reduce economic losses for industries and customers [23]. There are similar requirements for fish industry as well.Traditional methods for quality assessments of both meat and fish are precise but destructive, time-consuming, complicated for experiments, and requiring skilled operators. Hence quality information for every individual product cannot be easily collected on-line and nondestructively. In addition to EIS, other new alternative technologies for on-line quality detection also have been investigated, such as near infrared spectroscopy [24–26], hyperspectral image [27–29], and electronic nose technology [30–32]. The comparison of the four technologies was showed in Table 1. All of the technologies are fast, nondestructive, and suitable for development of on-line detecting instrument. However, near infrared spectroscopy and hyperspectral image demand expensive equipment, and electronic nose technology requires specific environmental conditions for measurement [33]. Compared with other new technologies, EIS shows outstanding advantages of being inexpensive and low requirement to the operation [14, 17].Table 1
Comparison of four new technologies.
Technologies
Fast
Nondestructive
Easily implemented
Inexpensive
Near infrared spectroscopy
√
√
√
×
Hyperspectral image
√
√
√
×
Electronic nose technology
√
√
×
×
EIS
√
√
√
√The review mainly introduced principles of EIS applied to biological tissue and summarized its application for quality assessment of meat and fish. Specifically, the article consisted of four sections: (i) the fundamental theories and principles, (ii) two common measurement methods and corresponding probes with diverse configurations, (iii) the latest development in application for quality assessments of meat and fish, and (iv) challenges and future trends of using EIS for quality assessment of meat and fish.


## 2. Three Important Fundamental Theories

EIS began to be tested on biological system in the 1920s [34, 35]. Some classic theories about principles of EIS applied to biological system were proposed in later study. They were Fricke model, an equivalent circuit diagram of biological tissue [36–38], Schwan’s dispersion theory, which proposed three main dispersions occurring in biological tissue [14, 39], and Cole-Cole equation, an empirical formula describing the dispersions [40].


### 2.1. Fricke Equivalent Circuit Model

Fricke model [36–38], shown in Figure 1, is an elementary and excellent method to simulate biological system at microscopic level by electronic components [17, 41, 42]. It considers biological tissue as a homogeneous suspension of cells in an ionized liquid medium and simulates biological tissue components, such as membranes, intracellular (ICF) and extracellular fluids (ECF), with passive electrical elements, such as resistor and capacitor, connected in series and in parallel.Figure 1
Fricke model.The model consists of three elements (Re, Ri, Cm), so it is also called three-element circuit model. The three elements Re, Ri, and Cm represent resistance of ECF, resistance of ICF, and capacitance of membrane, respectively. Na+ and Cl- ions exist in ECF. In ICF, major cation is K+, while major anions are phosphate and proteins. Therefore, ICF and ECF can be regarded as electrolytes. Cell membrane performs similar to capacitance. At low frequencies, the current cannot pass through the cells membrane because of its high resistance, while, at higher frequencies, the current passes through ECF, membrane, and ICF. Parameters of the three electrical elements depend on ions concentration and mobility during metabolism of cells, which reflect on physicochemical properties of biological tissue [41]. The model has been widely used in cells, microorganisms’ suspensions in a liquid medium, and homogeneous media [41, 42].


### 2.2. Schwan’s Dispersion Theory

A phenomenon that electrical parameters have dramatical changes in a certain frequency range is called frequency scattering, referred to as dispersion, which is due to corresponding relaxation phenomena [43]. Schwan [39] proposed that there are three main dispersions (α,β,γ) in biological system occurring at three frequency bands (Figure 2). The α-dispersion, from few Hz to few KHz, is caused by the polarization phenomena in the electrical double layer of the tissue and is a relaxation of “nonpermanent” dipoles, which is formed during free counterions laterally moving along the insulating cell membrane or large molecules [14]. The α-dispersion has been extensively studied in biomedical applications on monitoring tissue or organ vitality for transplantation [41].Figure 2
Three dispersions in biological system [σ(S/m) represents electric conductivity and ε′ represents dielectric constant] [23].Theβ-dispersion, at radio frequencies ranging from few KHz to MHz, is mainly due to the Maxwell-Wagner effect, which is related to interface polarization occurring in systems where the electric current passes at the interface between two different materials. The β-dispersion is associated with the dielectric properties of the cell membranes, as well as the interactions between membranes and the extracellular or intracellular electrolytes [14]. The β-dispersion is directly associated with the cell membranes behavior and could serve in study of meat ageing based on membrane integrity, as oxidation of the phospholipid membrane layers and lysis occurring during ageing make the membrane porous, which causes insulating properties of membrane to decrease [14, 44].Theγ-dispersion, at microwave frequencies above 100 MHz, is mainly a result of the permanent dipole relaxation of small molecules, mainly water molecules in biological tissues [14, 23].In general, theα- and β-dispersions are more relevant to the cells states compared with γ-dispersion and commonly used in impedance measurements for study on biological tissues.


### 2.3. The Cole-Cole Theory

The Cole-Cole equation (shown in (1)) derived based on a considerable amount of experimental data can be used to fit the dispersion processes [40, 45]. In (1), Z∗ is complex impedance. R0 and R∞ are the impedances at the “static” and “infinite frequency.” ω=2πf. τ is a characteristic constant which may be called the relaxation time. α is a dimensionless exponent parameter, which is a constant reflecting distribution in dispersion and is used for correcting the nonstrict capacitive behavior of membranes in biological tissues caused by dielectric losses [14, 41]:(1)Z∗=R∞+R0-R∞1+iωτα=zRe+izIm.As shown in Figure3, locus of (1) in complex plane is an arc with its center below the horizontal axis, which is consistent with the measured bioimpedance data plotted in the complex impedance plane. This is called Cole-Cole plot [40].Figure 3
The Cole-Cole plot [45].Moreover, there is another similar equation described by Foster and Schwan [46] and shown as (2) [47], where fc=1/2πτ (shown in Figure 3) is the characteristic frequency, at which the imaginary part of impedance is the largest in absolute value among other frequencies [47].(2)Z∗=R∞+R0-R∞1+if/fcα=zRe+izIm.Furthermore, the Cole-Cole equation can be related to Fricke model by following equations shown in (3) [41, 45]. Electric parameters in the fitting equations reflect physical and chemical properties of biological tissues. Prediction models for quality parameters are established with the electric parameter data as input, which is the principle of EIS applied to quality assessment of food.(3)τ=Ri+ReCm,R0=Re,R∞=ReRiRe+Ri.


### 2.4. Progress of Equivalent Circuit Model

EIS technology is able to deduce elementary reactions steps and extract the kinetic parameters characterizing the reaction steps of a total electrochemical reaction system, by using equivalent circuits or functions derived from the known mechanism [48]. However, for biological tissues, although Fricke model and Cole-Cole equation have made some contributions to theoretical analysis, there are still a lot of complicated unknown reactions and transformations in biological system needed to be discovered and explored. Therefore, equivalent circuit model plays an important role in EIS analysis, and more effective ones are still expected to be developed for application in food quality detection.In (1), when α is 1, the equation mathematically describes the Fricke model. However, it was observed that the Fricke model was not accurate enough to fit the experimental results [14, 40], as cell membrane performs different with capacitance. According to Cole-Cole equation, a constant phase element (CPE), which is used to describe cell membrane with modified capacitive performance, is defined as ZCPE=1/Ka(j·ω)α. Guermazi et al. [14] used a modified Fricke model (shown in Figure 4) to investigate the ageing state of beef meat during 14 days. Results indicated that the modified Fricke model led to a good fitting performance with the expected behavior of muscle during ageing.Figure 4
Modified Fricke model.Fricke model and modified Fricke model are the most classic equivalent circuit models for biological tissue and are still used in recent research on animal or plant tissue, such as estimating freshness of carp [17], preliminary analysis to predict breast cancer [49], detecting phosphorus nutrition level forSolanum lycopersicum [7], and changes of potato tissues during drying [50].Additionally, there is also an equivalent circuit model used to simulate plant tissue. As shown in Figure5, the model was developed by Zhang et al. (1990) [51] during study on parenchymatous plant tissues. It takes into account cell wall, vacuole, and tonoplast. In Figure 5, the components R1, R2, and R3 represent the cell wall resistance, cytoplasm resistance, and vacuole resistance. C1 and C2 represent plasma membrane capacitance and tonoplast capacitance, respectively.Figure 5
Equivalent circuit model proposed by Zhang et al. [51].Although the model contains more detailed cell structure, the fitting performance of the model to the measured impedance data was worse than that of the modified Fricke model according to research of Wu et al. [52] on eggplant pulp. Harker and Maindonald [53] also used the model to study nectarine during ripening. The results showed that the model was not able to predict all of the changes during ripening, but it helped to improve the understanding of nectarine ripening.Recently, a new electrical model of a biological cell with nucleus was developed and simulated [54, 55]. The simulation results of the model were compared well with published data, which indicated the promising utilization to serve as an aid to further understand behavior of cells over frequency range.


## 2.1. Fricke Equivalent Circuit Model

Fricke model [36–38], shown in Figure 1, is an elementary and excellent method to simulate biological system at microscopic level by electronic components [17, 41, 42]. It considers biological tissue as a homogeneous suspension of cells in an ionized liquid medium and simulates biological tissue components, such as membranes, intracellular (ICF) and extracellular fluids (ECF), with passive electrical elements, such as resistor and capacitor, connected in series and in parallel.Figure 1
Fricke model.The model consists of three elements (Re, Ri, Cm), so it is also called three-element circuit model. The three elements Re, Ri, and Cm represent resistance of ECF, resistance of ICF, and capacitance of membrane, respectively. Na+ and Cl- ions exist in ECF. In ICF, major cation is K+, while major anions are phosphate and proteins. Therefore, ICF and ECF can be regarded as electrolytes. Cell membrane performs similar to capacitance. At low frequencies, the current cannot pass through the cells membrane because of its high resistance, while, at higher frequencies, the current passes through ECF, membrane, and ICF. Parameters of the three electrical elements depend on ions concentration and mobility during metabolism of cells, which reflect on physicochemical properties of biological tissue [41]. The model has been widely used in cells, microorganisms’ suspensions in a liquid medium, and homogeneous media [41, 42].


## 2.2. Schwan’s Dispersion Theory

A phenomenon that electrical parameters have dramatical changes in a certain frequency range is called frequency scattering, referred to as dispersion, which is due to corresponding relaxation phenomena [43]. Schwan [39] proposed that there are three main dispersions (α,β,γ) in biological system occurring at three frequency bands (Figure 2). The α-dispersion, from few Hz to few KHz, is caused by the polarization phenomena in the electrical double layer of the tissue and is a relaxation of “nonpermanent” dipoles, which is formed during free counterions laterally moving along the insulating cell membrane or large molecules [14]. The α-dispersion has been extensively studied in biomedical applications on monitoring tissue or organ vitality for transplantation [41].Figure 2
Three dispersions in biological system [σ(S/m) represents electric conductivity and ε′ represents dielectric constant] [23].Theβ-dispersion, at radio frequencies ranging from few KHz to MHz, is mainly due to the Maxwell-Wagner effect, which is related to interface polarization occurring in systems where the electric current passes at the interface between two different materials. The β-dispersion is associated with the dielectric properties of the cell membranes, as well as the interactions between membranes and the extracellular or intracellular electrolytes [14]. The β-dispersion is directly associated with the cell membranes behavior and could serve in study of meat ageing based on membrane integrity, as oxidation of the phospholipid membrane layers and lysis occurring during ageing make the membrane porous, which causes insulating properties of membrane to decrease [14, 44].Theγ-dispersion, at microwave frequencies above 100 MHz, is mainly a result of the permanent dipole relaxation of small molecules, mainly water molecules in biological tissues [14, 23].In general, theα- and β-dispersions are more relevant to the cells states compared with γ-dispersion and commonly used in impedance measurements for study on biological tissues.


## 2.3. The Cole-Cole Theory

The Cole-Cole equation (shown in (1)) derived based on a considerable amount of experimental data can be used to fit the dispersion processes [40, 45]. In (1), Z∗ is complex impedance. R0 and R∞ are the impedances at the “static” and “infinite frequency.” ω=2πf. τ is a characteristic constant which may be called the relaxation time. α is a dimensionless exponent parameter, which is a constant reflecting distribution in dispersion and is used for correcting the nonstrict capacitive behavior of membranes in biological tissues caused by dielectric losses [14, 41]:(1)Z∗=R∞+R0-R∞1+iωτα=zRe+izIm.As shown in Figure3, locus of (1) in complex plane is an arc with its center below the horizontal axis, which is consistent with the measured bioimpedance data plotted in the complex impedance plane. This is called Cole-Cole plot [40].Figure 3
The Cole-Cole plot [45].Moreover, there is another similar equation described by Foster and Schwan [46] and shown as (2) [47], where fc=1/2πτ (shown in Figure 3) is the characteristic frequency, at which the imaginary part of impedance is the largest in absolute value among other frequencies [47].(2)Z∗=R∞+R0-R∞1+if/fcα=zRe+izIm.Furthermore, the Cole-Cole equation can be related to Fricke model by following equations shown in (3) [41, 45]. Electric parameters in the fitting equations reflect physical and chemical properties of biological tissues. Prediction models for quality parameters are established with the electric parameter data as input, which is the principle of EIS applied to quality assessment of food.(3)τ=Ri+ReCm,R0=Re,R∞=ReRiRe+Ri.


## 2.4. Progress of Equivalent Circuit Model

EIS technology is able to deduce elementary reactions steps and extract the kinetic parameters characterizing the reaction steps of a total electrochemical reaction system, by using equivalent circuits or functions derived from the known mechanism [48]. However, for biological tissues, although Fricke model and Cole-Cole equation have made some contributions to theoretical analysis, there are still a lot of complicated unknown reactions and transformations in biological system needed to be discovered and explored. Therefore, equivalent circuit model plays an important role in EIS analysis, and more effective ones are still expected to be developed for application in food quality detection.In (1), when α is 1, the equation mathematically describes the Fricke model. However, it was observed that the Fricke model was not accurate enough to fit the experimental results [14, 40], as cell membrane performs different with capacitance. According to Cole-Cole equation, a constant phase element (CPE), which is used to describe cell membrane with modified capacitive performance, is defined as ZCPE=1/Ka(j·ω)α. Guermazi et al. [14] used a modified Fricke model (shown in Figure 4) to investigate the ageing state of beef meat during 14 days. Results indicated that the modified Fricke model led to a good fitting performance with the expected behavior of muscle during ageing.Figure 4
Modified Fricke model.Fricke model and modified Fricke model are the most classic equivalent circuit models for biological tissue and are still used in recent research on animal or plant tissue, such as estimating freshness of carp [17], preliminary analysis to predict breast cancer [49], detecting phosphorus nutrition level forSolanum lycopersicum [7], and changes of potato tissues during drying [50].Additionally, there is also an equivalent circuit model used to simulate plant tissue. As shown in Figure5, the model was developed by Zhang et al. (1990) [51] during study on parenchymatous plant tissues. It takes into account cell wall, vacuole, and tonoplast. In Figure 5, the components R1, R2, and R3 represent the cell wall resistance, cytoplasm resistance, and vacuole resistance. C1 and C2 represent plasma membrane capacitance and tonoplast capacitance, respectively.Figure 5
Equivalent circuit model proposed by Zhang et al. [51].Although the model contains more detailed cell structure, the fitting performance of the model to the measured impedance data was worse than that of the modified Fricke model according to research of Wu et al. [52] on eggplant pulp. Harker and Maindonald [53] also used the model to study nectarine during ripening. The results showed that the model was not able to predict all of the changes during ripening, but it helped to improve the understanding of nectarine ripening.Recently, a new electrical model of a biological cell with nucleus was developed and simulated [54, 55]. The simulation results of the model were compared well with published data, which indicated the promising utilization to serve as an aid to further understand behavior of cells over frequency range.


## 3. Bipolar and Tetrapolar Measurement Methods

### 3.1. Bipolar Measurement Method

The mathematical principle of electrical impedance measurement is Ohm’s law,Z=U/I. Thus the most fundamental measuring method is using two electrodes to induce the current (I) and to measure the voltage (V). However, in this bipolar measurement method, because of the existence of electrode polarization, the measured impedance consists of two parts, that is, impedance of measured object and parasitic capacitive impedance on the interface of the electrode-sample ohmic contacts [22, 56]. The parasitic impedance decreases with frequency increasing; thus it can be ignored when frequency is high in bipolar measurement system [17]. Material and configuration of electrodes have an impact on electric field loaded on the tested biological tissue and are important factors in EIS measurements [57, 58].Five common configurations of two-electrode probes used in research of meat are introduced as follows, and the corresponding schematic diagrams are shown in Figure6. Sun et al. [17] estimated freshness of carp based on EIS morphological characteristic with a pair of platinum (Pt) electrodes (Figure 6(a)). Masot et al. [57] used a coaxial needle electrode (Figure 6(b)) in measurement to predict salt content in cured ham or pork loin. The electrode was made of a hollow needle and an isolated wire inside the needle, which were both made of stainless steel. A dielectric material, an epoxy resin, was between them. The needle is used for electromyography in medical applications. Damez et al. [59] used a cylindrical probe (Figure 6(c)) to measure electrical anisotropy of meat to assess ageing state. The probe was made of 20 stainless steel needles electrodes equally arranged on a periphery with 8 cm in diameter. Each pair of electrodes that are diametrically opposite was a bipolar system. The ten electrodes pairs were able to measure impedances at radial directions. Ćurić et al. [16] tested the applicability of needle-type multielectrode array (Figure 6(d)) in evaluating salt and moisture content of structurally heterogeneous fish samples. The needle-type multielectrode array arranged in bipolar configuration was comprised of two rows of 6 parallel, electrically connected, and gold-plated needles and was considered able for more accurate measurements for electrical properties. Guermazi et al. [14] measured the impedance of beef meat during ageing period with circular penetrating multielectrodes (Figure 6(e)). The circular penetrating probe consisted of one central electrode and eight surrounding electrodes. All electrodes were made of gold-plated steel. The excitation signal was delivered from the inner electrode to the meat and the surrounding electrodes were grounded. This provided information about the distribution of the electric field in meat independent of anisotropy effects [14]. It was found that measured data had higher reliability and the instrument was more stable, when using two rows of pairs of needles electrodes than using circular probe with needles electrodes equally arranged on a periphery [60].Figure 6
Schematic diagrams of five configurations of bipolar probe.
(a)
(b)
(c)
(d)
(e)Electrode distance variation technique can be used for electrode polarization correction in bipolar measurement system. Some researches thought that capacitive gap contact between electrode and sample reflected the feature of the samples [22, 56]. Based on electrode distance variation technique, Damez et al. [61] investigated contact impedance as a parameter of interest for assessment of meat ageing. The probe used in measurement composed of eight stainless needles aligned and spaced 15 mm apart (ϕ=0.6mm;L=5mm). Any pair of electrodes was a bipolar system, and 28 data points were obtained from these pairs of electrodes. Some data were used to plot a figure of average impedance against distance between electrodes at several frequencies. It showed linear relation between impedance and distance, and the slope of line corresponded to impedance per unit length of the sample, while the y-intercept represented contact impedance. Contact impedance was found to be related to meat fibers strength and reflected conductive properties of the extracellular spaces [61]. Limitation of the technique is that the polarization impedance becomes large in comparison with the sample impedances at low frequencies. Therefore, the spacing between electrodes should be as large as possible in order to increase the weight of the sample impedance [56].


### 3.2. Tetrapolar Measurement Method

To eliminate the electrode polarization for more accurate measurement of impedance, a tetrapolar measurement method was proposed. The parasitic voltage is caused when a current flows through the interface of electrode sample. In tetrapolar system, current inducing and voltage measurement are via two separate electrodes pairs [22]; thus current is not introduced in the voltage measurement circuit and the parasitic impedance is eliminated. However, in practical terms, electrodes with uniform specific polarization impedance (per unit surface area) over the entire electrode surface are difficult to obtain, which causes the electrode on a potential level different from the one it ought to register [56].The simplest configuration of four-electrode probe is four metal electrodes arrayed in line. Yang et al. [45] used four stainless steel electrodes (Figure 7(a)) to measure impedance of porcine meat for moisture content prediction. Furthermore, Altmann and Pliquett [62] carried on an impedance measurement using Purdue Tetrapolar probe (PTP) (Figure 7(b)) on the M. longissimus dorsi in pork and beef. The configuration of PTP was a steel shaft with four ring electrodes mounted in a line along it. The electrodes were insulated from each other, and the outer pair of electrodes was used for inducing a current into system while the inner pair of electrodes was for voltage measurement. Similar to the cylindrical bipolar probe mentioned above, Damez et al. [59] also used a cylindrical tetrapolar probe (Figure 7(c)). The probe consisted of 24 noninvasive electrodes which were equally spaced on a periphery in two concentric circles. Each four electrodes arrayed along diameter were a tetrapolar system. The advantage of this probe was that the impedances of six directions could be obtained in a single application. There is also a three-point measurement method, which is the simplification of tetrapolar method with one electrode working to induce current and to measure voltage. However, this method is rarely used in measurement for biological system and is not introduced in detail in this paper.Figure 7
Schematic diagrams of three configurations of tetrapolar probe.
(a)
(b)
(c)


## 3.1. Bipolar Measurement Method

The mathematical principle of electrical impedance measurement is Ohm’s law,Z=U/I. Thus the most fundamental measuring method is using two electrodes to induce the current (I) and to measure the voltage (V). However, in this bipolar measurement method, because of the existence of electrode polarization, the measured impedance consists of two parts, that is, impedance of measured object and parasitic capacitive impedance on the interface of the electrode-sample ohmic contacts [22, 56]. The parasitic impedance decreases with frequency increasing; thus it can be ignored when frequency is high in bipolar measurement system [17]. Material and configuration of electrodes have an impact on electric field loaded on the tested biological tissue and are important factors in EIS measurements [57, 58].Five common configurations of two-electrode probes used in research of meat are introduced as follows, and the corresponding schematic diagrams are shown in Figure6. Sun et al. [17] estimated freshness of carp based on EIS morphological characteristic with a pair of platinum (Pt) electrodes (Figure 6(a)). Masot et al. [57] used a coaxial needle electrode (Figure 6(b)) in measurement to predict salt content in cured ham or pork loin. The electrode was made of a hollow needle and an isolated wire inside the needle, which were both made of stainless steel. A dielectric material, an epoxy resin, was between them. The needle is used for electromyography in medical applications. Damez et al. [59] used a cylindrical probe (Figure 6(c)) to measure electrical anisotropy of meat to assess ageing state. The probe was made of 20 stainless steel needles electrodes equally arranged on a periphery with 8 cm in diameter. Each pair of electrodes that are diametrically opposite was a bipolar system. The ten electrodes pairs were able to measure impedances at radial directions. Ćurić et al. [16] tested the applicability of needle-type multielectrode array (Figure 6(d)) in evaluating salt and moisture content of structurally heterogeneous fish samples. The needle-type multielectrode array arranged in bipolar configuration was comprised of two rows of 6 parallel, electrically connected, and gold-plated needles and was considered able for more accurate measurements for electrical properties. Guermazi et al. [14] measured the impedance of beef meat during ageing period with circular penetrating multielectrodes (Figure 6(e)). The circular penetrating probe consisted of one central electrode and eight surrounding electrodes. All electrodes were made of gold-plated steel. The excitation signal was delivered from the inner electrode to the meat and the surrounding electrodes were grounded. This provided information about the distribution of the electric field in meat independent of anisotropy effects [14]. It was found that measured data had higher reliability and the instrument was more stable, when using two rows of pairs of needles electrodes than using circular probe with needles electrodes equally arranged on a periphery [60].Figure 6
Schematic diagrams of five configurations of bipolar probe.
(a)
(b)
(c)
(d)
(e)Electrode distance variation technique can be used for electrode polarization correction in bipolar measurement system. Some researches thought that capacitive gap contact between electrode and sample reflected the feature of the samples [22, 56]. Based on electrode distance variation technique, Damez et al. [61] investigated contact impedance as a parameter of interest for assessment of meat ageing. The probe used in measurement composed of eight stainless needles aligned and spaced 15 mm apart (ϕ=0.6mm;L=5mm). Any pair of electrodes was a bipolar system, and 28 data points were obtained from these pairs of electrodes. Some data were used to plot a figure of average impedance against distance between electrodes at several frequencies. It showed linear relation between impedance and distance, and the slope of line corresponded to impedance per unit length of the sample, while the y-intercept represented contact impedance. Contact impedance was found to be related to meat fibers strength and reflected conductive properties of the extracellular spaces [61]. Limitation of the technique is that the polarization impedance becomes large in comparison with the sample impedances at low frequencies. Therefore, the spacing between electrodes should be as large as possible in order to increase the weight of the sample impedance [56].


## 3.2. Tetrapolar Measurement Method

To eliminate the electrode polarization for more accurate measurement of impedance, a tetrapolar measurement method was proposed. The parasitic voltage is caused when a current flows through the interface of electrode sample. In tetrapolar system, current inducing and voltage measurement are via two separate electrodes pairs [22]; thus current is not introduced in the voltage measurement circuit and the parasitic impedance is eliminated. However, in practical terms, electrodes with uniform specific polarization impedance (per unit surface area) over the entire electrode surface are difficult to obtain, which causes the electrode on a potential level different from the one it ought to register [56].The simplest configuration of four-electrode probe is four metal electrodes arrayed in line. Yang et al. [45] used four stainless steel electrodes (Figure 7(a)) to measure impedance of porcine meat for moisture content prediction. Furthermore, Altmann and Pliquett [62] carried on an impedance measurement using Purdue Tetrapolar probe (PTP) (Figure 7(b)) on the M. longissimus dorsi in pork and beef. The configuration of PTP was a steel shaft with four ring electrodes mounted in a line along it. The electrodes were insulated from each other, and the outer pair of electrodes was used for inducing a current into system while the inner pair of electrodes was for voltage measurement. Similar to the cylindrical bipolar probe mentioned above, Damez et al. [59] also used a cylindrical tetrapolar probe (Figure 7(c)). The probe consisted of 24 noninvasive electrodes which were equally spaced on a periphery in two concentric circles. Each four electrodes arrayed along diameter were a tetrapolar system. The advantage of this probe was that the impedances of six directions could be obtained in a single application. There is also a three-point measurement method, which is the simplification of tetrapolar method with one electrode working to induce current and to measure voltage. However, this method is rarely used in measurement for biological system and is not introduced in detail in this paper.Figure 7
Schematic diagrams of three configurations of tetrapolar probe.
(a)
(b)
(c)


## 4. Quality Assessments of Meat and Fish

This section summarized recent development in quality assessments of meat and fish using EIS. The published research was summarized on two aspects of quality assessments: (i) physicochemical properties of raw meat and fish and (ii) chemical compositions of both raw meat and fish and their products. For physicochemical properties evaluation, four common quality issues, ageing state of beef, PSE and DFD of pork, freshness of fish, and defrosting of fish and chicken, were discussed, respectively. For chemical compositions evaluation, salt and water are the main evaluated ingredients, and the discussions are separated into two parts, meat and fish, depending on distinct features between them. Specific framework of the summary is shown in Figure8. The general information of all studies reviewed is given in Table 2.Table 2
Application of EIS for quality assessments of meat and fish.
Object
Quality indicator
Predictor variable
Frequency
Algorithm
Accuracy
Reference
Ageing state: beef
Heifers
WBSF
Electrical impedance(EI), conductivity(EC)
A wide range for EI, 1 KHz for EC
CA
R
=
0.68 (EI), and −0.65 (EC)
[63]
beef
Mechanical resistance
Z
1
k
H
z
/
Z
100
k
H
z
1,100 kHz
LRA
0.73
≤
R
2
≤
0.98
[64]
beef
Mechanical resistance
Lineic impedance index (I); contact impedance (CI)
100 Hz
LRA
R
2
=
0.79 (I); 0.77≤R2≤0.95 (CI)
[61]
Cull cows
Mechanical resistance
8 selected parameters related to complex impedance
100 Hz–1.5 MHz
LRA
R
2
=
0.71
[59]
Beef and veal
Time of postmortem period
R
e, Ri, C derived from the modified Fricke model
40 Hz–110 MHz
TA
—a
[14]
PSE, DFD, and RFN: pork
Porcine muscle
pH45
Changes in phase angle
1 kHz
CA
R
=
0.73
[65]
Pig carcass
Drip loss at 24 h
Electrical impedance
1000 Hz
PLS
R
=
0.50, RMSEP = 2.53%
[66]
Green ham
pH45
R
∞
/
R
0, α, and fc
8 kHz–1 MHz
MRA
R
2
=
0.50
[47]
Lean pork tenderloins
TAC, TVB-N
Z
=
(
Z
0
-
Z
t
)
/
Z
0 (Z0, impedance at day 0; Zt, impedance at a given storage interval)
20, 200 Hz, 2, 20, 200 kHz
NRA
0.758
≤
R
2
≤
0.992, 0.11≤RMSE≤0.57 for TAC; 0.636≤R2≤0.989, 0.29≤RMSE≤1.68 for TVB-N
[13]
Freshness: fish
Carp, herring and sea bass
Hypoxanthine content
Phase angle
2.5 kHz
LRA
R
2
=
0.92 for carp; 0.87 for herring; 0.86 for sea bass
[67]
Grass carps
TAC,K value, TVB-N, sensory assessment (SA)
Q
=
(
Z
1
k
H
z
-
Z
16
k
H
z
)
×
100
/
Z
16
k
H
z
1 kHz, 16 kHz
CA
R
=
0.943 for TAC, 0.996 for k value, 0.951 for TVB-N, 0.968 for SA
[68]
Bighead carp
pH, texture, TVB-N,K value, drop loss, SA and TAC
Q
=
(
Z
1
k
H
z
-
Z
20
k
H
z
)
×
100
/
Z
20
k
H
z
1 kHz, 20 kHz
CA
R
≥
0.919 for pH and texture, ≥0.955 for TVB-N, K value, drop loss, SA and TAC
[69]
Sea bream
TVB-N
Impedance modulus and phase
1 Hz–1 MHz
PLS
R
2
=
0.72
[70]
Carp
Storage time (ST), SA
Morphological characteristic parameter, impedance modulus, phase
1 Hz–1 MHz
LRA
R
2
=
0.69 for ST, 0.66 for SA
[17]
Defrosting: fish and chicken
Sea bass
Slow-frozen in 1 and 2 cycles, Fast-frozen in 1 and 2 cycles
Resistance, reactance, water, fat content, WHC and pH
1 Hz–1 MHz
PCA, DA
78% for total samples, 100% for the unfrozen
[71]
Salmon
Frozen and stored for 15, 30 and 60 days, frozen in 1 and 2 cycles
Impedance modulus and phase
1 Hz–1 MHz
PCA, DA
71.93%
[72]
Sea bream
Frozen and stored for 15, 30 and 60 days, frozen in 1 and 2 cycles
Impedance modulus and phase
1 Hz–1 MHz
PCA, DA
70.24%
[73]
Sea bass
Frozen for 1 month with and without temperature fluctuations (TF), frozen for 4 months with and without TF
Impedance modulus and phase
0.1–1000 kHz
ANOVA, LRA
R
2
=
0.918 between phase angle and protein solubility
[74]
Atlantic chub mackerel
Slow-frozen in 1 and 2 cycles, Fast-frozen in 1 and 2 cycles
Resistance, reactance
1 Hz–1 MHz
ANOVA
—a
[75]
Chicken breast meat
1 and 2 frozen-thawed cycles
Impedance modulus and phase
50–200 kHz
LVQNN
100% for fresh, >90% for one cycle, >88% for two cycles
[60]
Chicken breast meat
1, 2, and 3 frozen-thawed cycles
(
Z
50
H
z
-
Z
200
k
H
z
)
/
Z
200
k
H
z, (Ph50Hz-Ph200kHz)/Ph200kHz, chewiness, hardness, expressible loss (Z is the modulus; Ph is the phase angles)
50 Hz, 200 kHz
LVQNN
100%, 97.5%, 87.5%, and 77.5% for 0, 1, 2, and 3 cycles samples, respectively
[15]
Water, fat, and salt: meat
Potted minced pork products
Water (W), lipid (L)
Impedance modulus and phase
5 kHz–2 MHz
PLS
A standard deviation of the residues of 0.66% for W and 1.08% for L
[76]
Porcine meat
Water
R
∞
1–250 kHz
LRA
R
2
=
0.879, RMSEP = 0.00566, RSD = 0.73%
[45]
Chicken breast meat
Water
Impedance module and phase
1 Hz–1 MHz
PLS
R
2
=
0.9388, predictive residual sum of squares of 1.9242
[58]
Green ham
Visual fatness
R
∞ , α, conformation, ham weight
8 kHz–1 MHz
MRA
R
2
=
0.59
[47]
Beef and pork from different size grinds
Fat
Resistance, temperature, weight
50 kHz
—a
R
2
=
0.36, 0.60,0.86 for beef trim, 0.95-, 0.32 cm ground;R2=0.64, 0.66, 0.92 for pork trim, 0.95,  0.32 cm ground
[77]
Pig of various breeds and cattle
Intramuscular fat
R
u, resistance at high frequencies immediately after the change of current signal;R0, derived for the voltage extrapolated to infinite time
500 Hz–100 kHz
SFR, CA
R=0.34 for total pork,R=0.69 for beef
[62]
Minced pork loin
Salt
Impedance modulus and phase
100 Hz–1 MHz
PLS
R
2
=
0.934
[57]
Minced pork loin
Sodium chloride, sodium nitrite, sodium nitrate content
Impedance modulus and phase
1 kHz–1 MHz
PLS
p1 = 0.985, NR = 0.388 for chloride;p1 = 0.709, NR = 1.436 for nitrite; p1 = 0.648, NR = 1.511 for nitrateb
[78]
Dry-cured hams of three qualities (deep spoilage, spoiled swollen, and unaltered)
Salt
Impedance modulus and phase
100 Hz–1 kHz
PLS
0.72
≤
R
2
≤
0.78
[79]
Salt and water: fish
Fresh salted Atlantic salmon
Salt, water, water phase salt (WPS)
Conductance at 1 MHz, capacitance increment(Cap10MHz-Cap1MHz)
1, 10 MHz
LRA
0.822
≤
R
2
≤
0.926 for salt, 0.488≤R2≤0.534 for water, 0.732≤R2≤0.890 for WPS
[80]
Fresh salted rainbow trout
Salt, water, WPS
Impedance modulus and phase
50 kHz
LRA, MRA
R
2
=
0.864 for WPS, 0.844 for water, 0.853 for salt
[16]
Salmon during salting-smoking process
Salt, water, water activity (aw)
Impedance modulus and phase
1 Hz–1 MHz
PLS
RMSEP = 0.685 for salt, 0.006 foraw, 3.579 for water
[81]
Smoked salmon and cod products (of different brands and batches)
Salt, water, lipid,aw
Impedance modulus and phase
1 Hz–1 MHz
PLS
0.701
≤
R
2
≤
0.823 for aw, 0.351≤R2≤0.640 for lipid, 0.564≤R2≤0.851 for water, 0.600≤R2≤0.761 for salt
[82]
Correlation analysis: CA; Linear regression analysis: LRA; Trend analysis: TA; Multiple regression analysis: MRA; Nonlinear regression analysis: NRA; Analysis of variance: ANOVA; Learning vector quantization neural network: LVQNN; Stepwise forward regression: SFR;Accuracya was not available; Theb parameters of p1 (slope of the fitting line) and norm of residuals (NR) were obtained from the fitting line of predicted and measured salt content, and were used as indicators of performance of the PLS model.Figure 8
Framework of overview.


### 4.1. Physicochemical Properties

Physicochemical properties of raw meat and fish include color, pH, water-holding capacity (WHC), Warner-Bratzler shear force (WBSF), and total volatile basic nitrogen (TVB-N). These properties are the indicators for diverse quality issues, which draw attention of customers and manufacturers. EIS has been studied to realize prediction of physicochemical properties in order to solve the appropriate quality issues.


#### 4.1.1. Ageing State: Beef

One of the most important factors of beef palatability or quality is tenderness that is closely related to meat ageing state [59]. Meat after slaughter undergoes two periods, rigor mortis and ageing. Then it is either further processed or sold in the retail markets. During ageing, connective proteins break down, causing structural changes of fragmentation of myofibrils and degradation of cytoskeletons. Well-aged meat is tender with improved flavor. Assessment of meat tenderness optimizes refrigerated storage time and the ageing state for sale [61]. Ageing results in changes in membrane and intracellular and extracellular electrolytes. These can impact electrical properties and can be reflected by impedances with increasing frequencies [83]. During post-rigor-mortis ageing, impedance of meat decreases more slowly than prerigor period [84] and there is no convincing explanation of the mechanisms.It was found that ratioZ1kHz/Z100kHz decreased free from the impact of fat level during ageing [85, 86]. Therefore, the ratio was suggested as an indicator for meat ageing state. However, Lepetit et al. [64] found that the ratio Z1kHz/Z100kHz and impedance (Z) tempted to decrease with ageing, but the slope of curves for the same muscle varied with different animals of Charolaise bulls. Thus these two parameters were not suitable for prediction of the ageing state. Meanwhile, they put forward electrical anisotropy as a new prediction parameter, since it decreased with ageing period and became almost isotropic after meat was fully aged. Based on this, Damez et al. [59] developed a circular probe, which was able to measure impedance at different directions of bovine meat. Ageing state evaluated by impedance measurement using the probe showed strong correlation with mechanical measurement for all muscles from three types (R2=0.71). However the correlations showed great difference between the three muscle types (rectus abdominis: R2=0.83; semimembranosus: R2=0.58; semitendinosus: R2=0.18). The results also showed that impedance measured parallel to muscle fibers is the minimum, while impedance measured across the muscle fibers is the maximum. Furthermore, Damez et al. [61] carried out impedance measurement only on the two directions for early assessment of beef meat ageing. The lineic impedance index was defined as the difference between the director coefficients of the regression lines of transversal and longitudinal impedance respectively against distance between electrodes. Higher correlation (R2=0.79) based on the lineic index were obtained. Another parameter, contact impedance, was also tested. It was calculated as the y-intercept of impedance regression line against the distance, and high correlations with meat fibers strength were obtained for all tested muscle types (0.77≤R2≤0.95). Recently, Guermazi et al. [14] applied the modified Fricke model to characterize the ageing state of different muscles of beef and veal. The extracellular resistance Re showed the highest sensitivity to ageing, with decreasing about 7% to 25% from day 6 to day 14 depending on the muscle type. These indicated that prediction ability of same model varied among different types of muscle.


#### 4.1.2. PSE, DFD, and RFN: Pork

Pork meat can be divided into DFD (dark, firm, and dry), PSE (pale color, soft texture, and high exudation), and RFN (red, firm, and nonexudative) based on its color, pH, and drip loss. PSE meats are unsuitable for processing and DFD meats are perishable [61]. Both of them present unfavorable appearance for consumers and thus cause economic losses for meat industry [87]. In contrast, RFN meat is desirable meat and ideal for both producers and consumers. The benefits of identification of low quality meat for industry include reducing economic losses and distributing the best destination of meat carcasses [23].Forrest et al. [66] used complex impedance (at 1000 Hz) to predict drip loss at 24 h of pig carcass at the slaughter line. The cross-validation showed a correlation of 0.50. The moderate results were thought to be due to a lack of temperature correction. Besides, the rate of changes in impedance and phase angle had a better performance of prediction for meat qualities than the absolute magnitude of impedance, which was concluded by Whitman et al. [65]. A better result was obtained by Oliver et al. [47] studying on green ham with Cole-Cole theory. Multiple regression model based on variables of ratio (R∞/R0), α, and fc showed a result of R2=0.50 to predict pH45 in thesemimembranosus (SM) region. It was also found that ratio in SM region classified the technologically normal meat (pH45 > 6.10) from the PSE meat with 88.46% accuracy.For DFD meat, the results were found not promising for the early detection based on electrical measurements [23, 61]. Besides, the detection for PSE meat was also difficult during the rigor mortis period, because pH, temperature, and metabolic modifications, which have influence on electrical properties of meat, are rapidly evolving at this period [88]. It was also concluded that discrimination of PSE, DFD, and RFN pork meat was more valid when the final pH has been reached, based on dielectric properties [23]. These indicated that detection time is also a significant factor in meat quality prediction.H. B. Nguyen and L. T. Nguyen [13] used a new parameter of relative changes impedance [Z=(Z0-Zt)/Z0, Z0, impedance at day 0; Zt, impedance at a given storage interval] to evaluate TVB-N and total aerobic count (TAC) of lean pork tenderloins during storage. The results were promising, with 0.636≤R2≤0.989 for TVB-N and 0.758≤R2≤0.992 for TAC. This study can be used as a reference for other quality parameters prediction, such as pH and drop loss.


#### 4.1.3. Freshness: Fish

Because of fish being highly perishable and significant impact of freshness on flavor, freshness is the primary quality attribute of fish for consumers and manufacture. EIS applied for freshness estimation of fish shows the promising results. Based on the phase angle and admittance changing with time, four stages of freshness (fresh, semifresh, semideteriorated, and deteriorated) of carp, herring, and sea bass can be easily defined. Furthermore, phase angle at 2.5 kHz had good linear relationship with hypoxanthine concentration which is used to estimate fish freshness (R2≥0.86 for carp, herring, and sea bass) [67]. Impedance change ratio (Q value), defined as Q=(Zf1-Zf2)×100/Zf2, is also found to have high correlation (R≥0.943) with TAC, TVB-N, and sensory assessment (SA) of grass carps and bighead carp during storage [68, 69].Although these impedance parameters demonstrated the ability as freshness indicators, the studies for practical application are in the preliminary stage and need to be confirmed in more situations [68]. Furthermore, it was found that impedance parameters to predict freshness of fish samples of different batches showed worse results than for the samples of same batches [70]. The reason was thought to be the difference in composition terms among the distinct batches. It was proved that growth environments and dietaries have an effect on body composition of fish [89, 90]. To eliminate the effect of different origins of fish, Sun et al. [17] defined a morphological characteristic parameter extracted from Bode plots to predict freshness. Compared with impedance module and phase angle, morphologic characteristic parameter showed highest correlations (R2=0.69). The results were not outstanding due to 20 samples obtained from 20 different retailers.


#### 4.1.4. Defrosting: Fish and Chicken

Due to perishability of fish and chicken, freezing is the common means to slow down meat quality deterioration during postharvest handling and storage. But in freezing, frozen storage, and thawing, the processes of protein denaturation and lipid oxidation take place, which not only cause tissue injury but also affect the sensory as well as nutritional quality of the products [72, 91]. Because of this, a lot of consumers prefer fresh products regardless of price. However in many cases, the difference between frozen-thawed products and fresh ones are too small to tell based upon their visual appearance [92]. Processes of freezing/thawing and frozen storage provoke protein denaturation and the destruction of cell membranes, which cause modification produced on the mobility of water and ion concentration and affect electrical impedance measured on fish and chicken [71, 93].Fish samples of fresh and suffered with different freezing styles, frozen storage periods, and freezing cycles were investigated to be distinguished based on EIS at a frequency range of 1 Hz to 1 MHz [71–73]. Impedance data were analyzed with principal components analysis (PCA) and discriminant analysis (DA). Classification accuracy was above 70% (71.93% and 70.24%) for total samples [72, 73], while impedance data combined with physical and chemical parameters (water, fat content, WHC, and pH) as input had a higher accuracy of 78% [71]. The accuracies for fresh samples were up to 100%, while the results for the separation between freezing styles, storage periods, and freezing cycles were not good. This was considered caused by no significant difference between samples of different storage periods and freezing cycles in electrical impedance, which was the same as in other physicochemical parameters such as moisture content, TVB-N, K1 value (an indicator of ATP related compounds), pH, and microbial counts [72]. Moreover, it was found that reactance can be used to differentiate between different freezing styles and freezing cycles for sea bass at frequencies higher than 500 kHz [71]. And it seems that phase or reactance may be better indicator of the freezing history compared to impedance modulus or resistance [74, 75].For chicken breast meat, the fresh samples and frozen samples with different freezing cycles were successfully distinguished (100% for fresh, >90% for one cycle, and >88% for two cycles), based on impedance magnitude and phase at frequencies from 50 to 200 kHz and a modeling method of learning vector quantization neural network (LVQNN) [60]. Furthermore, impedance modulus and phase angles measured at two frequencies (50 Hz and 200 kHz), combined with three physical properties (chewiness, hardness, and expressible loss), also showed the discrimination ability (100%, 97.5%, 87.5%, and 77.5% for the fresh samples, the one frozen-thawed cycles, two and three cycles, respectively) [15].


### 4.2. Chemical Compositions

There are many kinds of meat and fish products, such as meat floss, ham, bacon meat, and fish, which are deeply liked by customers in the market. The flavors of these products are affected not only by their physicochemical properties of the raw materials but also by chemical compositions in raw materials and end products as well as additives added in processes. Some of these compositions are moisture, lipid, and salt. Some research [94–97] has demonstrated that electrical properties of food are closely associated with their chemical compositions, which shows the possibility of EIS to assess component contents of food.


#### 4.2.1. Water, Fat, and Salt Content: Meat

Prediction of water content of meat showed promising results, with EIS being applied to raw porcine meat [45] and potted minced pork products [76]. De Jesús et al. [79] studied dry-cured hams of three qualities (deep spoilage, spoiled swollen, and unaltered), and no good performance for water content prediction was obtained. This was due to no significant differences for water content among the all dry-cured ham samples. It was discovered that the moisture content differs from part to part and, even in one single piece of meat, moisture values vary from different detecting directions. Furthermore, meat muscles with fat uniformly distributed and low fat levels may obtain better accuracy for prediction [45]. Schmidt et al. [58] predicted moisture content of skinless deboned chicken breast meat, during cooking process with different heating time. Good prediction was obtained (R2=0.9388), which indicated EIS can also be applied to chicken for moisture prediction.The prediction of fat content of muscle meat based on EIS is not good [47, 62]. It was considered due to the fat concentrated in clustered adipose cells and inhomogeneously distributed within muscles [62]. This can be verified by the study of Marchello et al. [77], which assessed fat content of beef and pork from different size grinds based on impedance measurement. For the beef samples with fat percentage range of 4–35%, R2 for the trim, 0.95, and 0.32 cm ground meat were 0.36, 0.60, and 0.86, respectively. For the pork samples with fat percentage range of 7.5–35%, R2 for the pork samples of the trim, 0.95, and 0.32 cm ground were 0.64, 0.66, and 0.92, respectively. The results showed that the smaller the grind, the higher the accuracies of the prediction. These also demonstrated that fat content assessment for minced samples showed a better performance than for block samples.Good results were also obtained for salt content prediction of salted meat based on EIS [57, 78, 79]. It was also found that minced samples had better prediction accuracies than block samples, which indicated that salt homogeneously distributed within meat favorably impacts prediction accuracy. Furthermore, Labrador et al. [78] applied EIS to predict concentration levels of different salt types in minced meat. Precise prediction of chloride was obtained, whereas the predictions for nitrite and nitrate were moderate.


#### 4.2.2. Salt and Water Content: Fish

Salt content and water phase salt (WPS) of fresh salted fish have good correlations with impedance parameters, even at two or single frequencies [16, 80]. These parameters include impedance modulus at 50 kHz, conductance at 1 MHz, and capacitance increment between 10 MHz and 1 MHz. Impedance modulus generally reflects changes of conductance. Conductance is thought to be related to ions capability of movement and capacitance which includes the information of the muscle state. For smoked product and during salting-smoking process, salt content prediction also showed good results based on impedance data at a frequency range of 1 Hz to 1 MHz [81, 82]. For both situations, prediction for water activity (aw), a parameter closely related to microbial spoilage, obtained better results than for salt content. These indicated the feasibility to determine shelf life of smoked product based on EIS [82].Studies showed different prediction performance for moisture content of fish [16, 80, 81]. In addition, it was found that prediction results for smoked products differed among brands and species, and the difference between species was greater [82]. Species with lower lipid content had better accuracies, which is consistent with prediction for meat. Moreover, configuration of electrode is also considered as an influence factor on moisture prediction accuracy [16]. This was indirectly verified by Fuentes et al. [73], who tested two types of electrode to separate the fresh fish from the frozen. One type of electrode was able to separate, while the other one failed. However, the failed electrode was found to be efficient to separate for another species of fish [72]. Effect caused by species is considered due to the different structures and compositions of fish muscle between species, while effect of electrode is the various electrical fields loaded on muscle tissue. Guermazi et al. [14] investigated electrode configuration by using Finite Element Methods to simulate the distribution of the electric field in meat sample.


### 4.3. Challenges and Future Trends

Although the above review shows a promising application of EIS in quality assessment/prediction of raw meat, fish, and/or its products, several challenges remain to be addressed before it can be applied in practical manufacturing process. The challenges are mainly from two aspects, that is, electrical impedance measurement and the tested meat or fish.Electrode polarization presents one main challenge of EIS in impedance measurement and data exploration. Forced by the electric field, dissolved free ions existing in conductive systems tend to move towards the electrode/sample interface, leading to the formation of ionic double layers. The ionic double layers exhibit capacitive impedance character, producing voltage drops. This phenomenon is electrode polarization. The resultant spectra of electrode polarization overlay the relaxation process of the interested samples, impeding the interpretation of the data. Various approaches on both description of electric double layer using equivalent circuits and measurement setup configuration compensations are studied to correct the electrode polarization. However, effect of electrode polarization depends on impedance of the samples, the measuring temperature, the structure and materials of the electrode, and even the roughness of the electrode surface. These factors make it complicated and no correction technique can satisfy ideally in practical application [56].Some challenges from the tested meat and/or fish are caused by variety of biological tissue. Diverse species, muscle types, and origins cause different muscle structure and/or composition, which produce various measurement results. Furthermore, in one muscle, anisotropic properties of tissues adversely impact prediction accuracy. These imply that predict model and/or electrode need be specialized for different application objects, and measurements should be performed on exact location of samples as well as control electrode orientation. Complexity of biological tissue also makes the difficulty in data interpretation and exploration. Although prediction of quality indicators performed promising results, the interpretations of relationships between EIS data and status of tested meat or fish samples are ambiguities.Further research should focus on enhancing measurement system, which includes developing optimal electrode in suitable material and configuration, controlling or correcting temperature during measurement, and optimizing measurement setup configuration, in order to reduce effect of electrode polarization and muscle anisotropy to improve precision and stability. More effective equivalent circuit models and data processing methods need to be further studied to explore interpretation of the data with status and/or components of diverse tested meat or fish samples, in order to help extract the most important feature parameter for quality estimation accordingly and to help investigate the most suitable prediction model strategy with high precision, efficiency, and robustness for practical application.


## 4.1. Physicochemical Properties

Physicochemical properties of raw meat and fish include color, pH, water-holding capacity (WHC), Warner-Bratzler shear force (WBSF), and total volatile basic nitrogen (TVB-N). These properties are the indicators for diverse quality issues, which draw attention of customers and manufacturers. EIS has been studied to realize prediction of physicochemical properties in order to solve the appropriate quality issues.


### 4.1.1. Ageing State: Beef

One of the most important factors of beef palatability or quality is tenderness that is closely related to meat ageing state [59]. Meat after slaughter undergoes two periods, rigor mortis and ageing. Then it is either further processed or sold in the retail markets. During ageing, connective proteins break down, causing structural changes of fragmentation of myofibrils and degradation of cytoskeletons. Well-aged meat is tender with improved flavor. Assessment of meat tenderness optimizes refrigerated storage time and the ageing state for sale [61]. Ageing results in changes in membrane and intracellular and extracellular electrolytes. These can impact electrical properties and can be reflected by impedances with increasing frequencies [83]. During post-rigor-mortis ageing, impedance of meat decreases more slowly than prerigor period [84] and there is no convincing explanation of the mechanisms.It was found that ratioZ1kHz/Z100kHz decreased free from the impact of fat level during ageing [85, 86]. Therefore, the ratio was suggested as an indicator for meat ageing state. However, Lepetit et al. [64] found that the ratio Z1kHz/Z100kHz and impedance (Z) tempted to decrease with ageing, but the slope of curves for the same muscle varied with different animals of Charolaise bulls. Thus these two parameters were not suitable for prediction of the ageing state. Meanwhile, they put forward electrical anisotropy as a new prediction parameter, since it decreased with ageing period and became almost isotropic after meat was fully aged. Based on this, Damez et al. [59] developed a circular probe, which was able to measure impedance at different directions of bovine meat. Ageing state evaluated by impedance measurement using the probe showed strong correlation with mechanical measurement for all muscles from three types (R2=0.71). However the correlations showed great difference between the three muscle types (rectus abdominis: R2=0.83; semimembranosus: R2=0.58; semitendinosus: R2=0.18). The results also showed that impedance measured parallel to muscle fibers is the minimum, while impedance measured across the muscle fibers is the maximum. Furthermore, Damez et al. [61] carried out impedance measurement only on the two directions for early assessment of beef meat ageing. The lineic impedance index was defined as the difference between the director coefficients of the regression lines of transversal and longitudinal impedance respectively against distance between electrodes. Higher correlation (R2=0.79) based on the lineic index were obtained. Another parameter, contact impedance, was also tested. It was calculated as the y-intercept of impedance regression line against the distance, and high correlations with meat fibers strength were obtained for all tested muscle types (0.77≤R2≤0.95). Recently, Guermazi et al. [14] applied the modified Fricke model to characterize the ageing state of different muscles of beef and veal. The extracellular resistance Re showed the highest sensitivity to ageing, with decreasing about 7% to 25% from day 6 to day 14 depending on the muscle type. These indicated that prediction ability of same model varied among different types of muscle.


### 4.1.2. PSE, DFD, and RFN: Pork

Pork meat can be divided into DFD (dark, firm, and dry), PSE (pale color, soft texture, and high exudation), and RFN (red, firm, and nonexudative) based on its color, pH, and drip loss. PSE meats are unsuitable for processing and DFD meats are perishable [61]. Both of them present unfavorable appearance for consumers and thus cause economic losses for meat industry [87]. In contrast, RFN meat is desirable meat and ideal for both producers and consumers. The benefits of identification of low quality meat for industry include reducing economic losses and distributing the best destination of meat carcasses [23].Forrest et al. [66] used complex impedance (at 1000 Hz) to predict drip loss at 24 h of pig carcass at the slaughter line. The cross-validation showed a correlation of 0.50. The moderate results were thought to be due to a lack of temperature correction. Besides, the rate of changes in impedance and phase angle had a better performance of prediction for meat qualities than the absolute magnitude of impedance, which was concluded by Whitman et al. [65]. A better result was obtained by Oliver et al. [47] studying on green ham with Cole-Cole theory. Multiple regression model based on variables of ratio (R∞/R0), α, and fc showed a result of R2=0.50 to predict pH45 in thesemimembranosus (SM) region. It was also found that ratio in SM region classified the technologically normal meat (pH45 > 6.10) from the PSE meat with 88.46% accuracy.For DFD meat, the results were found not promising for the early detection based on electrical measurements [23, 61]. Besides, the detection for PSE meat was also difficult during the rigor mortis period, because pH, temperature, and metabolic modifications, which have influence on electrical properties of meat, are rapidly evolving at this period [88]. It was also concluded that discrimination of PSE, DFD, and RFN pork meat was more valid when the final pH has been reached, based on dielectric properties [23]. These indicated that detection time is also a significant factor in meat quality prediction.H. B. Nguyen and L. T. Nguyen [13] used a new parameter of relative changes impedance [Z=(Z0-Zt)/Z0, Z0, impedance at day 0; Zt, impedance at a given storage interval] to evaluate TVB-N and total aerobic count (TAC) of lean pork tenderloins during storage. The results were promising, with 0.636≤R2≤0.989 for TVB-N and 0.758≤R2≤0.992 for TAC. This study can be used as a reference for other quality parameters prediction, such as pH and drop loss.


### 4.1.3. Freshness: Fish

Because of fish being highly perishable and significant impact of freshness on flavor, freshness is the primary quality attribute of fish for consumers and manufacture. EIS applied for freshness estimation of fish shows the promising results. Based on the phase angle and admittance changing with time, four stages of freshness (fresh, semifresh, semideteriorated, and deteriorated) of carp, herring, and sea bass can be easily defined. Furthermore, phase angle at 2.5 kHz had good linear relationship with hypoxanthine concentration which is used to estimate fish freshness (R2≥0.86 for carp, herring, and sea bass) [67]. Impedance change ratio (Q value), defined as Q=(Zf1-Zf2)×100/Zf2, is also found to have high correlation (R≥0.943) with TAC, TVB-N, and sensory assessment (SA) of grass carps and bighead carp during storage [68, 69].Although these impedance parameters demonstrated the ability as freshness indicators, the studies for practical application are in the preliminary stage and need to be confirmed in more situations [68]. Furthermore, it was found that impedance parameters to predict freshness of fish samples of different batches showed worse results than for the samples of same batches [70]. The reason was thought to be the difference in composition terms among the distinct batches. It was proved that growth environments and dietaries have an effect on body composition of fish [89, 90]. To eliminate the effect of different origins of fish, Sun et al. [17] defined a morphological characteristic parameter extracted from Bode plots to predict freshness. Compared with impedance module and phase angle, morphologic characteristic parameter showed highest correlations (R2=0.69). The results were not outstanding due to 20 samples obtained from 20 different retailers.


### 4.1.4. Defrosting: Fish and Chicken

Due to perishability of fish and chicken, freezing is the common means to slow down meat quality deterioration during postharvest handling and storage. But in freezing, frozen storage, and thawing, the processes of protein denaturation and lipid oxidation take place, which not only cause tissue injury but also affect the sensory as well as nutritional quality of the products [72, 91]. Because of this, a lot of consumers prefer fresh products regardless of price. However in many cases, the difference between frozen-thawed products and fresh ones are too small to tell based upon their visual appearance [92]. Processes of freezing/thawing and frozen storage provoke protein denaturation and the destruction of cell membranes, which cause modification produced on the mobility of water and ion concentration and affect electrical impedance measured on fish and chicken [71, 93].Fish samples of fresh and suffered with different freezing styles, frozen storage periods, and freezing cycles were investigated to be distinguished based on EIS at a frequency range of 1 Hz to 1 MHz [71–73]. Impedance data were analyzed with principal components analysis (PCA) and discriminant analysis (DA). Classification accuracy was above 70% (71.93% and 70.24%) for total samples [72, 73], while impedance data combined with physical and chemical parameters (water, fat content, WHC, and pH) as input had a higher accuracy of 78% [71]. The accuracies for fresh samples were up to 100%, while the results for the separation between freezing styles, storage periods, and freezing cycles were not good. This was considered caused by no significant difference between samples of different storage periods and freezing cycles in electrical impedance, which was the same as in other physicochemical parameters such as moisture content, TVB-N, K1 value (an indicator of ATP related compounds), pH, and microbial counts [72]. Moreover, it was found that reactance can be used to differentiate between different freezing styles and freezing cycles for sea bass at frequencies higher than 500 kHz [71]. And it seems that phase or reactance may be better indicator of the freezing history compared to impedance modulus or resistance [74, 75].For chicken breast meat, the fresh samples and frozen samples with different freezing cycles were successfully distinguished (100% for fresh, >90% for one cycle, and >88% for two cycles), based on impedance magnitude and phase at frequencies from 50 to 200 kHz and a modeling method of learning vector quantization neural network (LVQNN) [60]. Furthermore, impedance modulus and phase angles measured at two frequencies (50 Hz and 200 kHz), combined with three physical properties (chewiness, hardness, and expressible loss), also showed the discrimination ability (100%, 97.5%, 87.5%, and 77.5% for the fresh samples, the one frozen-thawed cycles, two and three cycles, respectively) [15].


## 4.1.1. Ageing State: Beef

One of the most important factors of beef palatability or quality is tenderness that is closely related to meat ageing state [59]. Meat after slaughter undergoes two periods, rigor mortis and ageing. Then it is either further processed or sold in the retail markets. During ageing, connective proteins break down, causing structural changes of fragmentation of myofibrils and degradation of cytoskeletons. Well-aged meat is tender with improved flavor. Assessment of meat tenderness optimizes refrigerated storage time and the ageing state for sale [61]. Ageing results in changes in membrane and intracellular and extracellular electrolytes. These can impact electrical properties and can be reflected by impedances with increasing frequencies [83]. During post-rigor-mortis ageing, impedance of meat decreases more slowly than prerigor period [84] and there is no convincing explanation of the mechanisms.It was found that ratioZ1kHz/Z100kHz decreased free from the impact of fat level during ageing [85, 86]. Therefore, the ratio was suggested as an indicator for meat ageing state. However, Lepetit et al. [64] found that the ratio Z1kHz/Z100kHz and impedance (Z) tempted to decrease with ageing, but the slope of curves for the same muscle varied with different animals of Charolaise bulls. Thus these two parameters were not suitable for prediction of the ageing state. Meanwhile, they put forward electrical anisotropy as a new prediction parameter, since it decreased with ageing period and became almost isotropic after meat was fully aged. Based on this, Damez et al. [59] developed a circular probe, which was able to measure impedance at different directions of bovine meat. Ageing state evaluated by impedance measurement using the probe showed strong correlation with mechanical measurement for all muscles from three types (R2=0.71). However the correlations showed great difference between the three muscle types (rectus abdominis: R2=0.83; semimembranosus: R2=0.58; semitendinosus: R2=0.18). The results also showed that impedance measured parallel to muscle fibers is the minimum, while impedance measured across the muscle fibers is the maximum. Furthermore, Damez et al. [61] carried out impedance measurement only on the two directions for early assessment of beef meat ageing. The lineic impedance index was defined as the difference between the director coefficients of the regression lines of transversal and longitudinal impedance respectively against distance between electrodes. Higher correlation (R2=0.79) based on the lineic index were obtained. Another parameter, contact impedance, was also tested. It was calculated as the y-intercept of impedance regression line against the distance, and high correlations with meat fibers strength were obtained for all tested muscle types (0.77≤R2≤0.95). Recently, Guermazi et al. [14] applied the modified Fricke model to characterize the ageing state of different muscles of beef and veal. The extracellular resistance Re showed the highest sensitivity to ageing, with decreasing about 7% to 25% from day 6 to day 14 depending on the muscle type. These indicated that prediction ability of same model varied among different types of muscle.


## 4.1.2. PSE, DFD, and RFN: Pork

Pork meat can be divided into DFD (dark, firm, and dry), PSE (pale color, soft texture, and high exudation), and RFN (red, firm, and nonexudative) based on its color, pH, and drip loss. PSE meats are unsuitable for processing and DFD meats are perishable [61]. Both of them present unfavorable appearance for consumers and thus cause economic losses for meat industry [87]. In contrast, RFN meat is desirable meat and ideal for both producers and consumers. The benefits of identification of low quality meat for industry include reducing economic losses and distributing the best destination of meat carcasses [23].Forrest et al. [66] used complex impedance (at 1000 Hz) to predict drip loss at 24 h of pig carcass at the slaughter line. The cross-validation showed a correlation of 0.50. The moderate results were thought to be due to a lack of temperature correction. Besides, the rate of changes in impedance and phase angle had a better performance of prediction for meat qualities than the absolute magnitude of impedance, which was concluded by Whitman et al. [65]. A better result was obtained by Oliver et al. [47] studying on green ham with Cole-Cole theory. Multiple regression model based on variables of ratio (R∞/R0), α, and fc showed a result of R2=0.50 to predict pH45 in thesemimembranosus (SM) region. It was also found that ratio in SM region classified the technologically normal meat (pH45 > 6.10) from the PSE meat with 88.46% accuracy.For DFD meat, the results were found not promising for the early detection based on electrical measurements [23, 61]. Besides, the detection for PSE meat was also difficult during the rigor mortis period, because pH, temperature, and metabolic modifications, which have influence on electrical properties of meat, are rapidly evolving at this period [88]. It was also concluded that discrimination of PSE, DFD, and RFN pork meat was more valid when the final pH has been reached, based on dielectric properties [23]. These indicated that detection time is also a significant factor in meat quality prediction.H. B. Nguyen and L. T. Nguyen [13] used a new parameter of relative changes impedance [Z=(Z0-Zt)/Z0, Z0, impedance at day 0; Zt, impedance at a given storage interval] to evaluate TVB-N and total aerobic count (TAC) of lean pork tenderloins during storage. The results were promising, with 0.636≤R2≤0.989 for TVB-N and 0.758≤R2≤0.992 for TAC. This study can be used as a reference for other quality parameters prediction, such as pH and drop loss.


## 4.1.3. Freshness: Fish

Because of fish being highly perishable and significant impact of freshness on flavor, freshness is the primary quality attribute of fish for consumers and manufacture. EIS applied for freshness estimation of fish shows the promising results. Based on the phase angle and admittance changing with time, four stages of freshness (fresh, semifresh, semideteriorated, and deteriorated) of carp, herring, and sea bass can be easily defined. Furthermore, phase angle at 2.5 kHz had good linear relationship with hypoxanthine concentration which is used to estimate fish freshness (R2≥0.86 for carp, herring, and sea bass) [67]. Impedance change ratio (Q value), defined as Q=(Zf1-Zf2)×100/Zf2, is also found to have high correlation (R≥0.943) with TAC, TVB-N, and sensory assessment (SA) of grass carps and bighead carp during storage [68, 69].Although these impedance parameters demonstrated the ability as freshness indicators, the studies for practical application are in the preliminary stage and need to be confirmed in more situations [68]. Furthermore, it was found that impedance parameters to predict freshness of fish samples of different batches showed worse results than for the samples of same batches [70]. The reason was thought to be the difference in composition terms among the distinct batches. It was proved that growth environments and dietaries have an effect on body composition of fish [89, 90]. To eliminate the effect of different origins of fish, Sun et al. [17] defined a morphological characteristic parameter extracted from Bode plots to predict freshness. Compared with impedance module and phase angle, morphologic characteristic parameter showed highest correlations (R2=0.69). The results were not outstanding due to 20 samples obtained from 20 different retailers.


## 4.1.4. Defrosting: Fish and Chicken

Due to perishability of fish and chicken, freezing is the common means to slow down meat quality deterioration during postharvest handling and storage. But in freezing, frozen storage, and thawing, the processes of protein denaturation and lipid oxidation take place, which not only cause tissue injury but also affect the sensory as well as nutritional quality of the products [72, 91]. Because of this, a lot of consumers prefer fresh products regardless of price. However in many cases, the difference between frozen-thawed products and fresh ones are too small to tell based upon their visual appearance [92]. Processes of freezing/thawing and frozen storage provoke protein denaturation and the destruction of cell membranes, which cause modification produced on the mobility of water and ion concentration and affect electrical impedance measured on fish and chicken [71, 93].Fish samples of fresh and suffered with different freezing styles, frozen storage periods, and freezing cycles were investigated to be distinguished based on EIS at a frequency range of 1 Hz to 1 MHz [71–73]. Impedance data were analyzed with principal components analysis (PCA) and discriminant analysis (DA). Classification accuracy was above 70% (71.93% and 70.24%) for total samples [72, 73], while impedance data combined with physical and chemical parameters (water, fat content, WHC, and pH) as input had a higher accuracy of 78% [71]. The accuracies for fresh samples were up to 100%, while the results for the separation between freezing styles, storage periods, and freezing cycles were not good. This was considered caused by no significant difference between samples of different storage periods and freezing cycles in electrical impedance, which was the same as in other physicochemical parameters such as moisture content, TVB-N, K1 value (an indicator of ATP related compounds), pH, and microbial counts [72]. Moreover, it was found that reactance can be used to differentiate between different freezing styles and freezing cycles for sea bass at frequencies higher than 500 kHz [71]. And it seems that phase or reactance may be better indicator of the freezing history compared to impedance modulus or resistance [74, 75].For chicken breast meat, the fresh samples and frozen samples with different freezing cycles were successfully distinguished (100% for fresh, >90% for one cycle, and >88% for two cycles), based on impedance magnitude and phase at frequencies from 50 to 200 kHz and a modeling method of learning vector quantization neural network (LVQNN) [60]. Furthermore, impedance modulus and phase angles measured at two frequencies (50 Hz and 200 kHz), combined with three physical properties (chewiness, hardness, and expressible loss), also showed the discrimination ability (100%, 97.5%, 87.5%, and 77.5% for the fresh samples, the one frozen-thawed cycles, two and three cycles, respectively) [15].


## 4.2. Chemical Compositions

There are many kinds of meat and fish products, such as meat floss, ham, bacon meat, and fish, which are deeply liked by customers in the market. The flavors of these products are affected not only by their physicochemical properties of the raw materials but also by chemical compositions in raw materials and end products as well as additives added in processes. Some of these compositions are moisture, lipid, and salt. Some research [94–97] has demonstrated that electrical properties of food are closely associated with their chemical compositions, which shows the possibility of EIS to assess component contents of food.


### 4.2.1. Water, Fat, and Salt Content: Meat

Prediction of water content of meat showed promising results, with EIS being applied to raw porcine meat [45] and potted minced pork products [76]. De Jesús et al. [79] studied dry-cured hams of three qualities (deep spoilage, spoiled swollen, and unaltered), and no good performance for water content prediction was obtained. This was due to no significant differences for water content among the all dry-cured ham samples. It was discovered that the moisture content differs from part to part and, even in one single piece of meat, moisture values vary from different detecting directions. Furthermore, meat muscles with fat uniformly distributed and low fat levels may obtain better accuracy for prediction [45]. Schmidt et al. [58] predicted moisture content of skinless deboned chicken breast meat, during cooking process with different heating time. Good prediction was obtained (R2=0.9388), which indicated EIS can also be applied to chicken for moisture prediction.The prediction of fat content of muscle meat based on EIS is not good [47, 62]. It was considered due to the fat concentrated in clustered adipose cells and inhomogeneously distributed within muscles [62]. This can be verified by the study of Marchello et al. [77], which assessed fat content of beef and pork from different size grinds based on impedance measurement. For the beef samples with fat percentage range of 4–35%, R2 for the trim, 0.95, and 0.32 cm ground meat were 0.36, 0.60, and 0.86, respectively. For the pork samples with fat percentage range of 7.5–35%, R2 for the pork samples of the trim, 0.95, and 0.32 cm ground were 0.64, 0.66, and 0.92, respectively. The results showed that the smaller the grind, the higher the accuracies of the prediction. These also demonstrated that fat content assessment for minced samples showed a better performance than for block samples.Good results were also obtained for salt content prediction of salted meat based on EIS [57, 78, 79]. It was also found that minced samples had better prediction accuracies than block samples, which indicated that salt homogeneously distributed within meat favorably impacts prediction accuracy. Furthermore, Labrador et al. [78] applied EIS to predict concentration levels of different salt types in minced meat. Precise prediction of chloride was obtained, whereas the predictions for nitrite and nitrate were moderate.


### 4.2.2. Salt and Water Content: Fish

Salt content and water phase salt (WPS) of fresh salted fish have good correlations with impedance parameters, even at two or single frequencies [16, 80]. These parameters include impedance modulus at 50 kHz, conductance at 1 MHz, and capacitance increment between 10 MHz and 1 MHz. Impedance modulus generally reflects changes of conductance. Conductance is thought to be related to ions capability of movement and capacitance which includes the information of the muscle state. For smoked product and during salting-smoking process, salt content prediction also showed good results based on impedance data at a frequency range of 1 Hz to 1 MHz [81, 82]. For both situations, prediction for water activity (aw), a parameter closely related to microbial spoilage, obtained better results than for salt content. These indicated the feasibility to determine shelf life of smoked product based on EIS [82].Studies showed different prediction performance for moisture content of fish [16, 80, 81]. In addition, it was found that prediction results for smoked products differed among brands and species, and the difference between species was greater [82]. Species with lower lipid content had better accuracies, which is consistent with prediction for meat. Moreover, configuration of electrode is also considered as an influence factor on moisture prediction accuracy [16]. This was indirectly verified by Fuentes et al. [73], who tested two types of electrode to separate the fresh fish from the frozen. One type of electrode was able to separate, while the other one failed. However, the failed electrode was found to be efficient to separate for another species of fish [72]. Effect caused by species is considered due to the different structures and compositions of fish muscle between species, while effect of electrode is the various electrical fields loaded on muscle tissue. Guermazi et al. [14] investigated electrode configuration by using Finite Element Methods to simulate the distribution of the electric field in meat sample.


## 4.2.1. Water, Fat, and Salt Content: Meat

Prediction of water content of meat showed promising results, with EIS being applied to raw porcine meat [45] and potted minced pork products [76]. De Jesús et al. [79] studied dry-cured hams of three qualities (deep spoilage, spoiled swollen, and unaltered), and no good performance for water content prediction was obtained. This was due to no significant differences for water content among the all dry-cured ham samples. It was discovered that the moisture content differs from part to part and, even in one single piece of meat, moisture values vary from different detecting directions. Furthermore, meat muscles with fat uniformly distributed and low fat levels may obtain better accuracy for prediction [45]. Schmidt et al. [58] predicted moisture content of skinless deboned chicken breast meat, during cooking process with different heating time. Good prediction was obtained (R2=0.9388), which indicated EIS can also be applied to chicken for moisture prediction.The prediction of fat content of muscle meat based on EIS is not good [47, 62]. It was considered due to the fat concentrated in clustered adipose cells and inhomogeneously distributed within muscles [62]. This can be verified by the study of Marchello et al. [77], which assessed fat content of beef and pork from different size grinds based on impedance measurement. For the beef samples with fat percentage range of 4–35%, R2 for the trim, 0.95, and 0.32 cm ground meat were 0.36, 0.60, and 0.86, respectively. For the pork samples with fat percentage range of 7.5–35%, R2 for the pork samples of the trim, 0.95, and 0.32 cm ground were 0.64, 0.66, and 0.92, respectively. The results showed that the smaller the grind, the higher the accuracies of the prediction. These also demonstrated that fat content assessment for minced samples showed a better performance than for block samples.Good results were also obtained for salt content prediction of salted meat based on EIS [57, 78, 79]. It was also found that minced samples had better prediction accuracies than block samples, which indicated that salt homogeneously distributed within meat favorably impacts prediction accuracy. Furthermore, Labrador et al. [78] applied EIS to predict concentration levels of different salt types in minced meat. Precise prediction of chloride was obtained, whereas the predictions for nitrite and nitrate were moderate.


## 4.2.2. Salt and Water Content: Fish

Salt content and water phase salt (WPS) of fresh salted fish have good correlations with impedance parameters, even at two or single frequencies [16, 80]. These parameters include impedance modulus at 50 kHz, conductance at 1 MHz, and capacitance increment between 10 MHz and 1 MHz. Impedance modulus generally reflects changes of conductance. Conductance is thought to be related to ions capability of movement and capacitance which includes the information of the muscle state. For smoked product and during salting-smoking process, salt content prediction also showed good results based on impedance data at a frequency range of 1 Hz to 1 MHz [81, 82]. For both situations, prediction for water activity (aw), a parameter closely related to microbial spoilage, obtained better results than for salt content. These indicated the feasibility to determine shelf life of smoked product based on EIS [82].Studies showed different prediction performance for moisture content of fish [16, 80, 81]. In addition, it was found that prediction results for smoked products differed among brands and species, and the difference between species was greater [82]. Species with lower lipid content had better accuracies, which is consistent with prediction for meat. Moreover, configuration of electrode is also considered as an influence factor on moisture prediction accuracy [16]. This was indirectly verified by Fuentes et al. [73], who tested two types of electrode to separate the fresh fish from the frozen. One type of electrode was able to separate, while the other one failed. However, the failed electrode was found to be efficient to separate for another species of fish [72]. Effect caused by species is considered due to the different structures and compositions of fish muscle between species, while effect of electrode is the various electrical fields loaded on muscle tissue. Guermazi et al. [14] investigated electrode configuration by using Finite Element Methods to simulate the distribution of the electric field in meat sample.


## 4.3. Challenges and Future Trends

Although the above review shows a promising application of EIS in quality assessment/prediction of raw meat, fish, and/or its products, several challenges remain to be addressed before it can be applied in practical manufacturing process. The challenges are mainly from two aspects, that is, electrical impedance measurement and the tested meat or fish.Electrode polarization presents one main challenge of EIS in impedance measurement and data exploration. Forced by the electric field, dissolved free ions existing in conductive systems tend to move towards the electrode/sample interface, leading to the formation of ionic double layers. The ionic double layers exhibit capacitive impedance character, producing voltage drops. This phenomenon is electrode polarization. The resultant spectra of electrode polarization overlay the relaxation process of the interested samples, impeding the interpretation of the data. Various approaches on both description of electric double layer using equivalent circuits and measurement setup configuration compensations are studied to correct the electrode polarization. However, effect of electrode polarization depends on impedance of the samples, the measuring temperature, the structure and materials of the electrode, and even the roughness of the electrode surface. These factors make it complicated and no correction technique can satisfy ideally in practical application [56].Some challenges from the tested meat and/or fish are caused by variety of biological tissue. Diverse species, muscle types, and origins cause different muscle structure and/or composition, which produce various measurement results. Furthermore, in one muscle, anisotropic properties of tissues adversely impact prediction accuracy. These imply that predict model and/or electrode need be specialized for different application objects, and measurements should be performed on exact location of samples as well as control electrode orientation. Complexity of biological tissue also makes the difficulty in data interpretation and exploration. Although prediction of quality indicators performed promising results, the interpretations of relationships between EIS data and status of tested meat or fish samples are ambiguities.Further research should focus on enhancing measurement system, which includes developing optimal electrode in suitable material and configuration, controlling or correcting temperature during measurement, and optimizing measurement setup configuration, in order to reduce effect of electrode polarization and muscle anisotropy to improve precision and stability. More effective equivalent circuit models and data processing methods need to be further studied to explore interpretation of the data with status and/or components of diverse tested meat or fish samples, in order to help extract the most important feature parameter for quality estimation accordingly and to help investigate the most suitable prediction model strategy with high precision, efficiency, and robustness for practical application.


## 5. Conclusions

EIS has been proved as a promising detection technology with advantages of being fast, nondestructive, inexpensive, and easily implemented and shows potential to replace traditional methods in order to save time, cost, and skilled persons. The reviewed studies above, including predictions of physicochemical properties and chemical compositions for raw meat and fish and their commercial products, illustrate that EIS has potential for application in quality assessments of meat and fish. Challenges of EIS lie on impact factors of impedance measurement, including electrode polarization, materials and structure of electrodes, and the measurement setup configuration, and lie on difficulty in data processing and interpretation for diverse and complex tested meat or fish tissue. These challenges still need to be carefully considered before moving this technology from the laboratories to the industrial real-time detection.



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*Source: 6370739-2017-07-16.xml*