JSSSJournal of Sensors and Sensor SystemsJSSSJ. Sens. Sens. Syst.2194-878XCopernicus PublicationsGöttingen, Germany10.5194/jsss-6-303-2017Electrical impedance spectroscopy (EIS) for biological analysis and food
characterization: a reviewGrossiMarcomarco.grossi8@unibo.ithttps://orcid.org/0000-0003-1316-9035RiccòBrunoDepartment of Electrical Energy and Information Engineering “Guglielmo
Marconi” (DEI), University of Bologna, Bologna, ItalyMarco Grossi (marco.grossi8@unibo.it)28August2017623033259March20175July201714July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://jsss.copernicus.org/articles/6/303/2017/jsss-6-303-2017.htmlThe full text article is available as a PDF file from https://jsss.copernicus.org/articles/6/303/2017/jsss-6-303-2017.pdf
Electrical impedance spectroscopy (EIS), in which a sinusoidal test
voltage or current is applied to the sample under test to measure its
impedance over a suitable frequency range, is a powerful technique to
investigate the electrical properties of a large variety of materials. In
practice, the measured impedance spectra, usually fitted with an equivalent
electrical model, represent an electrical fingerprint of the sample
providing an insight into its properties and behavior. EIS is used in a broad
range of applications as a quick and easily automated technique to
characterize solid, liquid, semiliquid, organic as well as inorganic
materials. This paper presents an updated review of EIS main implementations
and applications.
Introduction
Electrical impedance spectroscopy (EIS) is a powerful technique (Barsoukov
and Macdonald, 2005) that can be used in a broad range of applications, such
as microbiological analysis (Ramirez et al., 2008), food products screening
(Grossi et al., 2011a, 2014a), corrosion monitoring (Zhu et al., 2016),
quality control of coatings (Amirudin and Thieny, 1995) and cement paste
(Christensen et al., 1994), characterization of solid electrolytes
(Rafiuddin, 2016) and human body analysis (Clemente et al., 2013).
EIS dates back to 1894 when W. Nerst measured the dielectric constant of
aqueous electrolytes and other organic fluids (Nernst, 1894). However, it
was only in the mid-1980s that the interest in EIS really grew substantially,
thanks to computer-controlled digital instruments allowing quick and easy
measurements as well as complex data processing and analysis. According to
Orazem and Tribollet (Orazem and Tribollet, 2008), the number of scientific
papers on EIS applications doubled every 4 or 5 years, with over 1200
papers published in 2006. A conference dedicated to EIS started in 1989 in
Bombannes (France), and since then meetings have been held every 3 years.
Measurement setup configurations used in EIS featuring
(a) two electrodes, (b) three electrodes and
(c) four electrodes.
EIS is essentially carried out as follows. In one of its two versions,
the so-called “potentiostat EIS”, a sine-wave voltage,
V(t)=V‾+V^⋅sinωt,
is applied to the sample under test (SUT) and the induced current,
I(t)=I‾+I^⋅sinωt+ϕ,
is measured. Then, the complex impedance is calculated as
Z(jω)=V(jω)I(jω)=V^I^⋅e-jϕ=Z⋅ej⋅Arg(Z)=Re(Z)+j⋅Im(Z).
Here, V^ and I^ are the voltage and the current amplitude,
respectively; V‾ and I‾ are the voltage and the
current direct current (DC) values; f is the test signal frequency; ω=2πf the
angular frequency; φ the phase difference between V(t) and I(t);
V(jω) and I(jω) the Steinmetz transforms of V(t) and
I(t), respectively. The sine-wave parameters can be calculated from the
acquired signals using a fitting algorithm in the time domain (Grossi et al.,
2012a) or by applying a fast Fourier transform algorithm (Yoo and Park,
2000).
In the other version of EIS, known as “galvanostat EIS”, the SUT is
stimulated with a sine-wave current and the voltage drop across the SUT is
measured (of course, the impedance being still given by Eq. 3).
In most cases, the investigation by potentiostat EIS or galvanostat
EIS is equivalent and provides the same results. There are, however,
application-specific conditions making one technique more suitable than the
other. For example, in the case of corrosion analysis with the open circuit
voltage changing with time, galvanostat EIS assures the measurement is
carried out at the true corrosion potential (Guyader et al., 2009).
EIS measurements can be made with a different number of electrodes in
different configurations, among which the most common ones (usually called
two-, three- and four-electrode implementations) are presented in Fig. 1.
In the simplest one, Fig. 1a, the stimulus is applied and the measurement
is done using the same two electrodes, named the working electrode (WE) and
the counter electrode (CE). In this case, of course, Z(jω) includes
contributions due to the sample interface at both electrodes.
To reduce the influence of such interfaces, a third electrode, called
reference electrode (RE), can be added, as shown in Fig. 1b. In this
case, usually used for electrochemical systems, the test signal is applied
between the WE and the RE, while the current is measured at the CE. Since no
current is drawn at the RE, the measured impedance includes only the
contribution of the interface at the WE.
A further electrode can also be added, as shown in Fig. 1c, as it is
often the case in galvanostat EIS. The sine-wave test current is applied
between WE and CE, while the voltage is measured between the (added) working
sensing electrode (WSE) and RE. Since no current is drawn at both WSE and
RE, the measured impedance is independent of the electrode–sample
interfaces.
In practice, the use of a higher number of electrodes makes the measurements
more complex but also more precise; thus, a trade-off is in order with the
optimum solution depending on the constraint of any specific application.
The impedance Z(jω) is defined in the case of linear time invariant
(LTI) systems, exhibiting three conditions: (a) linearity, (b) stability and
(c) causality. However, since electrochemical systems exhibit a typically
non-linear behavior, to avoid excessive perturbations to the SUT, small
values of V^ (usually in the range 10 to 100 mV) are used to
operate in a pseudo-linear region where Z(jω) does not depend on
V^.
The LTI conformity of the measured EIS data can be tested using the
Kramers–Kronig (KK) relations (Boukamp, 1995):
ImZ(ω)=2ωπ∫0+∞ReZ(x)-ReZ(ω)x2-ω2dxReZ(ω)=ReZ+∞+2π∫0+∞x⋅ImZ(x)-ω⋅ImZ(ω)x2-ω2dx.
These equations can be used to calculate the KK residuals and test the data
for linearity for increasing amplitudes of the stimulating signal (Lohmann et
al., 2015; Haußmann and Melbert, 2017) in order to choose the best
LTI-compliant signal-to-noise ratio. However, Eqs. (4) and (5) are not very
useful since the integral must be calculated on the frequency range 0 to
+∞ while EIS data are available only on a limited frequency range. A
practical implementation of the KK test has been proposed by
Urquidi-Macdonald et al. (1990), where polynomial extrapolation was suggested
outside the measured frequency range (Boukamp, 1995; Schönleber et al.,
2014) where a chain of parallel RC elements is used to model impedance data.
While in most EIS applications the stimulating signal amplitude is kept low
to work in the pseudo-linear region, in a limited number of cases the system
non-linear response is investigated for additional information (Grossi et
al., 2012b). A detailed review on such cases is discussed by Fasmin and
Srinivasan (2017).
Since in the case of complex materials different components might exhibit
different mobilities, Z(jω) is generally measured over a wide range
of frequencies. Usually, experimental data are represented with the Nyquist
plot, where for each frequency, Im(Z) is plotted versus
Re(Z). Since Nyquist plots mask the impedance dependance on
frequency, sometimes the data are plotted using the Bode plots, where |Z|
and Arg(Z), or alternatively Re(Z) and Im(Z), are
plotted versus f (on a log scale).
The data are normally interpreted using equivalent circuits, made of
resistors and capacitors, while more complex elements (such as constant
phase elements or Warburg impedances) can also be added. Data fitting using
these models and extraction of the circuit parameters is a computer-
assisted process carried out by specific software packages, such as multiple
electrochemical impedance spectra parameterization (MEISP), EIS spectrum
analyzer and Elchemea Analytical.
Most EIS investigations are carried out stimulating the SUT with a sequence
of single-frequency sinusoidal test signals. This procedure results in high
signal-to-noise ratio but leads to long measurement times that are
sometimes unacceptable. This is, for instance, the case when very low
frequencies (in the mHz or µHz range) are used (such as for corrosion
analysis and battery testing) or when the electrical parameters of the SUT
change quickly (such as in the case of cardiovascular measurements). To
achieve shorter measurement times, broad bandwidth test signals (with a
spectral density covering the whole frequency range) are used coupled with
discrete Fourier transform analysis. Such signals include, for example,
multi-sine signals (Min et al., 2007; Breugelmans et al., 2010), rectangular
pulses and Gaussian functions (Pliquett et al., 2000; Howie et al., 2001),
sinc signals (Land et al., 2007; Lohmann et al., 2015; Haußann and
Melbert, 2017) and chirp signals (Min et al., 2008).
In the past, when modern instruments were not available, EIS measurements
were carried out by simultaneously displaying both test and response
signals on a XY oscilloscope. For each tested frequency, the curve on the
screen (Lissajous curve) was analyzed to extract the electrical parameters.
The main drawback of this technique, now obsolete, was the very long
measurement time (often in the range of many hours) incompatible with use on
non-stationary systems.
Today, most EIS investigations are carried out using dedicated lab
instruments (frequency response analyzers and LCR meters) featuring high
accuracy, a wide range of test frequencies, the possibility to make measurements
with a two-, three- or four-electrode configuration in both potentiostat and
galvanostat operation mode. Moreover, these instruments normally feature
proprietary software for data analysis. However, they are expensive (usually
many thousands of US dollars) and must be used in a lab environment. Thus, they are
not suitable for online measurements in the field.
Since the early 1980s, with the diffusion of low-cost microcontrollers, the
possibility to design portable electronic systems aimed at EIS measurements
has become a reality. In these systems, the excitation signal is usually
generated by means of digital-to-analog converters (DACs), while the
waveforms are acquired with analog-to-digital converters (ADCs).
Dzwonczyk et al. (1992) used an electronic system (featuring the Intel 80C196
microcontroller, eight 10-bit ADC channels and an RS-232 port), to measure
the myocardial impedance spectrum (Dzwonczyk et al., 1992). Later, Atmanand et
al. presented a portable LCR meter based on the Intel 8751 microcontroller
(Atmanand et al., 1995; Atmanand and Kumar, 1996). Yang et al. (2006)
designed a portable device for bioimpedance spectroscopy measurements, based
on the microcontroller PIC18F452 by Microchip and using the integrated
circuit AD9833 to generate the test signal, capable of measuring impedances
from 9Ω to 5.7 kΩ in the frequency range 20 kHz to 1 MHz.
In 2007, Bogonez and Riu proposed a bioimpedance system, based on
microcontroller PIC18F2420 by Microchip, to measure the impedance of kidneys
(from 0.07 Ω to 1.4 kΩ) in the frequency range 200 Hz to
2 MHz (Bogonez and Riu, 2007). An ad hoc designed board (featuring an ARM
STM32 microcontroller) that can perform EIS measurements in the frequency
range 1 mHz to 100 kHz and measure impedances from 1 Ω to
10 MΩ was presented by Piasecki et al. (2016).
(a) Equivalent electrical circuit for two electrodes
immersed in liquid electrolyte; (b) measured electrical resistance
versus time during bacterial growth.
Recently, the design of embedded portable systems for EIS measurements has
become even simpler with the introduction on the market of the integrated
circuit AD5933 by Analog Devices, a low-cost impedance analyzer
system on chip. This device, featuring an on-board test voltage generator
(maximum frequency 100 kHz) and a 12-bit 1 MSPS ADC for signals acquisition,
can measure impedances in the range 1 kΩ–10 MΩ (extendable
to 100 Ω–10 MΩ with an additional circuit). The AD5933 can be
used to realize hand-held, battery-operated impedance analyzers, thanks to
its I2C interface allowing quick and easy communication with a
microcontroller. Although much less accurate than expensive benchtop
instruments, the AD5933 is adequate for many EIS applications.
For instance, Simic (2013) presented an impedance analyzer based on the
microcontroller ATMega128 and AD5933 (with impedance and frequency range 1 kΩ
to 5 MΩ and 1 to 100 kHz, respectively), LCD display
and micro SD card for data filing; Breniuc et al. (2014) used a
DS1077 external oscillator to generate frequencies lower than 1 kHz;
Hoja and Lentka (2009) proposed the use of two AD5933 devices for a
portable impedance analyzer with extended range of impedances (100 Ω
to 10 GΩ) and frequencies (0.01 Hz to 100 kHz);
Alonso-Arce et al. (2013) presented a in-body sensor node for bio-monitoring
powered with a 50 mA coin-cell battery and capable of providing a 20-month
lifetime; Margo et al. (2013) extended the use of AD5933
to four-electrode measurements. Other authors proposed
the use of AD5933 to make low-cost biosensor systems for applications in DNA
and protein sensing (van Grinsven et al., 2010), detection of bladder cancer
(Chuang et al., 2016) and avian flu virus (Wang et al., 2015). Kamat et
al. (2014) discussed a sensor for non-invasive blood glucose analysis;
Ferreira et al. proposed an application for total body composition
(Ferreira et al., 2010) and body fluid distribution analysis (Ferreira et
al., 2011); Park et al. (2006) discussed the implementation of a sensor system to
monitor civil infrastructures (particularly for loosening bolts in a
bolt-jointed structure and detection of corrosion in an aluminum beam);
Durante et al. (2016) developed a sensor to be used for real-time
monitoring to detect fraud in milk composition.
To review a vast field of applications, the rest of this paper is organized
as follows: Sect. 2 is dedicated to microbial concentration measurement;
EIS applications aimed at human body analysis are discussed in Sect. 3;
Sect. 4 reviews the methods for electrical characterization of food
products and assessment of food quality. Finally, other non-biological EIS
applications are presented in Sect. 5.
Detection of microorganisms
Microbiological screening is important in different fields, from medical to
environmental monitoring, from food safety to military applications
(Alocilja and Radke, 2003), since excessive bacterial concentrations or the
presence of particular dangerous pathogens (such as Escherichia coli O157:H7 or Salmonella Typhimurium) can
seriously endanger human health. It is estimated that, only in the US,
food pathogens are responsible for 76 million illnesses, 325 000
hospitalizations and 5000 deaths every year (Mead et al., 2000).
The standard reference for bacterial concentration measurement is the plate
count technique (PCT) (Kaspar and Tartera, 1990), which is reliable and accurate but
essentially a laboratory technique (hence not suitable for in situ and
online measurements) with long response time (from 24 to 72 h).
From this point of view, EIS offers interesting and viable alternatives.
Impedance microbiology
The detection of microorganisms by means of electrical measurements dates
back to 1898, when Stewart discovered that the increase of bacterial
populations changed the electrical conductivity in the growth medium
(Stewart, 1899). However, it was only in the late 1970s, thanks to the works
of Fistenberg-Eden and Eden (Fistenberg-Eden and Eden, 1984; Fistenberg-Eden, 1983), Ur
and Brown (1975) and Cady (1978), that the technique,
known as impedance microbiology (IM), showed its full potential.
IM exploits the fact that bacterial metabolism
transforms uncharged or weakly charged compounds in highly charged ones, thus
producing a change in the electrical characteristics of the growth medium
from which the bacterial concentration responsible for the phenomenon can be
worked out.
For this purpose, measurements are usually done at a single frequency using a
couple of electrodes made of inert material (stainless steel or platinum) in
direct contact with the electrolyte where microbial growth takes place. The
equivalent circuit model for such a system is shown in Fig. 2a, where
Cm and Rm are the medium capacitance and resistance,
respectively; Ri is the resistance of the electrode–electrolyte
interface and Cdl is the capacitance of a double layer formed at
such an interface. Since this double layer is only few
Ångströms thick, Cdl
is large (typically in the range of µF), while Cm is
usually in the picofarad (pF) range. Thus, at
frequencies lower than about 1 MHz (as it is normally the case in IM),
Cm can be neglected and the system can be modeled as the series of
a resistance Rs=Rm+Ri and a capacitance
Cdl.
IM works as follows: the SUT, if necessary, diluted in an enriching medium,
is stored at a temperature favoring bacterial growth. Then, the electrical
parameters (Rs and Cdl) are measured at time intervals of a few
minutes. Until the microbial concentration (CB) is lower than a critical
threshold (Cth in the order of 106–107 CFU mL-1), the electrical
parameters are almost constant at their baseline values, while they exhibit
significant variations when CB>Cth. The time
needed for the electrical parameters to deviate from the baseline values,
called detect time (DT), is lower for highly contaminated samples and higher
for samples featuring low contamination. For example, as shown in Fig. 2b,
considering three samples with decreasing values of CB from 1 to 3,
it is DT1<DT2<DT3. Furthermore, since DT is a linear function
of the logarithm of the initial bacterial concentration (Grossi et al.,
2009), CB can be estimated from the measured DT once the system
has been calibrated for the particular combination of sample type and
bacterial strains.
IM has been used to measure bacterial concentration in different kind of
samples, with the correct choice of incubation temperature and enriching
medium playing a key role to achieve a good accuracy. Grossi et al. (2008) showed
that the total bacterial concentration in ice cream samples can be measured
with good accuracy (compared with the PCT) and without any enriching medium
at T= 39 ∘C, provided the system is suitably calibrated for
products of different composition. The same authors obtained good results
with samples of raw milk (Grossi et al., 2011b) (correlation R2=0.728 at T= 18 ∘C);
beer (Pompei et al., 2012), where the lactobacilli concentration was
measured at T= 35 ∘C by diluting the sample in 3 % yeast extract; and brackish
waters (Mancuso et al., 2016).
Hardy et al. (1977) discussed the application of IM to frozen vegetables, showing
how a microbial concentration of 105 CFU mL-1 can be detected with a
response time of 5 h. Settu et al. (2015) showed the
feasibility to detect Escherichia coli in human urine samples without any dilution in
enriching media, using a test frequency of 10 Hz and
achieving a good correlation (R2=0.90) with the PCT. Johnson et al. (2014) tested
different food products and various bacterial strains using indirect IM
(monitoring the concentration of CO2 produced by the bacteria) with a
commercial instrument (RABIT by Don Whitley Scientific) and found a
good correlation (R2>0.84) for 80 % of the tested products (Johnson et al.,
2014).
IM was also used to test the antimicrobial efficiency of different
antibiotics and chemical preservatives (Zhou and King, 1995; Silley and
Forsythe, 1996). In this case, samples inoculated with the same
concentration of the bacterial strain under test but different
concentrations of antibiotic are used, and the measured DT is plotted versus the
antibiotic concentration (more effective antibiotics leading to higher DTs).
Recently, research in IM was mainly aimed at reducing instrument dimensions
(to allow automatic and in situ measurements) and improving measurement
performance (in particular, lowering the response time). Grossi et al. (2010, 2013a, 2017)
designed a portable biosensor to measure bacterial concentration in liquid
and semiliquid media that includes an
incubation chamber (featuring stainless steel electrodes, heating
resistances and a temperature sensor for thermoregulation) and two electronic
boards, connected to a PC via RS-232 for data analysis and filing. A Telit
GT 863-PY module integrated into the system allows wireless data
transmission to remote hosts.
Uria et al. (2016) used a platinum interdigitated electrode array for IM with milk
samples spiked with concentrations of Escherichia coli ranging from 102 to
106 CFU mL-1. In this case, a novel calibration technique was
used where the electrical parameters were monitored only at two time
intervals (270 and 390 min from the start of the assay) at a test
frequency of 10 kHz. The authors claimed slightly better performances than
standard IM systems for lower bacterial concentrations (102 CFU mL-1),
though not in the case of highly contaminated samples.
(a) Modified working electrode with immobilized
bioreceptor; (b) equivalent electrical circuit of the biosensor;
(c) Nyquist plot of the equivalent circuit.
Choi et al. (2009) proposed the use of a solid culture medium for IM. In this
case, two thick acrylic plate electrodes were used with a
15 mm × 15 mm × 5 mm solid medium while the inoculated
sample was dropped on the medium surface and allowed to diffuse. The obtained
results showed that, compared with those based on liquid media, the proposed
biosensor had comparable performance in spite of its simpler fabrication and
portability. Puttaswamy and Sengupta produced a capillary microchannel with
gold-coated electrodes (Puttaswamy and Sengupta, 2010) that, thanks to the
increased electrolyte resistance, was able to monitor the medium capacitance
Cm with a multi-frequency approach (1 kHz to 1 MHz) allowing a
drastic reduction in response time (4 times faster than traditional IM systems
on the market). A microfluidic biochip was presented by Gomez-Sjoberg et
al. (2005) that uses dielectrophoresis (DEP) to separate bacterial cells from
the supporting electrolyte and trap them inside a small chamber (400 pL) by
DEP forces. In this way, bacteria can be concentrated (by a factor of 104
to 105), thus allowing much faster detection.
Impedance biosensors
In IM, the selectivity towards particular bacterial species is achieved by
diluting the SUT in selective enriching media (such as MacConkey Broth for
coliforms and Mannitol Salt Broth for staphylococci). In the case of
specific pathogenic strains, such as Escherichia coli O157:H7
or Salmonella Typhimurium, that can be dangerous even in very
small concentrations, a different approach is used: a particular bioreceptor
that is able to bind with the target bacterial strain is immobilized on the
electrode and the resulting binding modifies the system electrical
parameters. Compared with IM, impedance biosensors are usually characterized
by higher selectivity, higher sensitivity and shorter response times, and the
implementation by microfluidic strategies can result in even better
performances. Nevertheless, some limitations exist, mainly related to the
stability of the immobilized bioreceptor, and its implementation is more
complex than IM.
The typical experimental setup used in impedance biosensors
(electrochemical cells) is a three-electrode configuration, where the test
signal is applied between WE and RE, while the current is measured at CE.
The electrolyte in which the three electrodes are immersed is usually a
phosphate-buffered saline (PBS) solution with the presence of
[Fe(CN)6]3-/4- used as redox probe. The CE is usually realized with a
platinum wire while a Ag/AgCl or saturated calomel electrode is used as RE.
The WE, instead, is modified immobilizing the bioreceptor on its surface,
whose general structure is shown in Fig. 3a. The most common bioreceptor
for bacteria detection is an antibody (in this case, the term
“immunosensor” is often used); however, other options have recently been
presented, such as nucleic acids, bacteriophages and lectins (Wang et al.,
2012). When the target bacteria bind to the bioreceptor, the redox reaction
of [Fe(CN)6]3-/4- with the WE is hindered and the electrochemical
cell impedance changes. As discussed in Barreiros dos Santos et al. (2009),
the immobilization technique has a great impact on the biosensor
performance.
The impedance of the three-electrode electrochemical cell is usually modeled
with the equivalent circuit shown in Fig. 3b, where Rs is the
electrical resistance of the electrolyte; Cdl the double-layer
capacitance at the WE–electrolyte interface; Rct the charge transfer
resistance due to the redox reaction of [Fe(CN)6]3-/4- with the WE
and
ZW the Warburg impedance due to the diffusion process of reactants
(Randviir and Banks, 2013; Chang and Park, 2010).
Sometimes, Cdl is modeled with a constant phase element (CPE) for better
accuracy, and the impedance of CPE can then be expressed as
ZCPEjω=1Q⋅jωα=cosαπ2Q⋅ωα-jsinαπ2Q⋅ωα,
where Q represents the double-layer capacitance, while the parameter
α accounts for the non-ideal electrode–electrolyte interface (the
case α=1 refers to an ideal capacitance).
The Warburg impedance, in the ideal case of a diffusion layer with infinite
thickness, can be expressed as
ZWjω=σω⋅1-j,
where σ is the Warburg coefficient.
The electrical circuit of Fig. 3b is described using the Nyquist plot of
Fig. 3c. For low values of test frequency, the dominant effect is ion
diffusion (Warburg impedance) and the plot is essentially a straight line
with slope 45∘. At high frequencies instead, where diffusion time
constant is much longer than the signal period, the plot is described by a
semicircle with diameter given by the charge transfer resistance Rct.
This parameter is the most used one to estimate bacterial concentration,
since, when bacterial cells bind to the target bioreceptors at the WE
surface, the redox reaction is hindered and Rct increases. Sometimes,
however, the double layer capacitance Cdl is used instead.
Many impedance biosensors have been presented in the last few years to
measure the concentration of different pathogens. Yang et al. (2004) proposed
an interdigitated array (IDA) microelectrode capable of detecting
Escherichia coli O157:H7 in the concentration range 4.36×105–4.36×108 CFU mL-1. In this case, E. coli
antibodies were immobilized on the microelectrode surface by physical
absorption and, when exposed to E. coli concentration from 105
to 108 CFU mL-1, Rct increased from 750 Ω to
about 2 kΩ. An immunosensor for the same bacterial strain was
developed by Li et al. (2011), which used a gold electrode coated on both
sides with a 8 MHz quartz crystal on which E. coli antibodies were
immobilized through a self-assembled monolayer (SAM). Differently from other
biosensors, here the E. coli concentration was estimated from the
Cdl shift, 1 h after the bacteria were placed on the electrode.
The detection limit in E. coli pure culture was
102 CFU mL-1 and a linear response was found from 102 to
105 CFU mL-1. The immunosensor was successfully tested also on
real food samples (milk, spinach and ground beef) but the detection limit was
found to be higher than in the case of E. coli culture
(103 CFU mL-1 for milk and spinach and 104 CFU mL-1
for ground beef).
Radke and Alocilja proposed a microfabricated biosensor (featuring
interdigitated gold microelectrodes with immobilized bacteria antibodies)
capable of measuring E. coli K12 concentrations in the range 105
to 107 CFU mL-1 with response time of 5 min (Radke and Alocilja,
2004). Dong et al. (2013) discussed a label-free impedance immunosensor for
the detection of Salmonella Typhimurium developed by immobilizing
Salmonella antibodies on gold nanoparticles and poly
(amidoamine)-multi-walled carbon nanotubes – chitosan nanocomposite film
modified glassy carbon electrode. This biosensor, able to measure
concentrations from 103 to 107 CFU mL-1 in pure culture, was
successfully tested also with real milk samples. An impedance immunosensor
for detection of Salmonella Typhimurium was proposed by Dastider et
al. (2015) which is based on a microfluidic, highly interdigitated electrode
array with immobilized Salmonella antibodies. The lower detection limit of
this biosensor was 3×103 CFU mL-1 and it was demonstrated
that the microfluidic approach allows lower detection limit and faster
response time.
Recently, other bioreceptors have been tested such as nucleic acids,
bacteriophages and lectins (Wang et al., 2012). Bacteriophages, in
particular, are viruses that recognize and bind to specific receptors on the
target bacteria. When they come in contact with the target bacteria, they infect
the cell by viral DNA injection that, in about 30–60 min, leads to
bacterial cell lyses. Thus, Rct variation due to bacteriophages
binding to the host bacteria is somewhat different from other bioreceptors in
that at the beginning Rct increases due to bacterial binding
hindering the redox reaction at the WE surface, while later it decreases due
to cell lyses allowing the release of cell ionic material. Bacteriophages
have been used as immobilizing bioreceptors in biosensors for different
bacterial species (Shabani et al., 2008; Gervais et al., 2007; Tolba et al.,
2012), as well as for an unconventional approach to IM where bacteriophages
are integrated into the culture broth, and the monitored variation of
electrolyte resistance is mainly due to conductivity increase induced by cell
lyses (and not only due to bacterial metabolism) (Mortari et al., 2015).
According to the authors, this approach results in much better performances
with a detection limit of 1 CFU mL-1 or lower and response time less
than 1 h.
Analysis of human body composition
EIS is widely used as a quick, non-invasive and low-cost technique to
estimate human body composition and, in this particular application, it is
often referred to as bioelectrical impedance analysis (BIA).
The human body can be divided into different compartments, as shown in Fig. 4a
(Kyle et al., 2004; Mialich et al., 2014). Fat-free mass (FFM) (sometimes
referred as lean body mass) includes all body parts that are not fat mass
(FM). FFM, in turn, can be divided into various components: bone mineral (about
7 %); extracellular water (ECW, about 29 %); intracellular water
(ICW, about 44 %); and visceral protein. Total body water (TBW) represents
the sum of ECW and ICW.
Fat tissues are characterized by low electrical conductivity (i.e., high
impedance values) while lean tissues present high electrical conductivity
(i.e., low impedance values) due to the high content of electrolytes (Kanti
Bera, 2014). TBW is the major compound of FFM that helps the flow of
electrical current due to the conductivity of electrolytes dissolved in body
water (Khalil et al., 2014).
(a) The different compartments of the human body;
(b) a typical four-electrode configuration for BIA measurements;
(c) equivalent electrical circuit used to interpret measured data in
BIA.
BIA is often used to estimated TBW and also FFM, since for healthy people in
a normal hydration state TBW is about 73.2 % of FFM.
BIA normally features a four-electrode configuration in galvanostat mode: a
sinusoidal test current is applied between WE and CE and the corresponding
voltage drop is measured between WSE and RE. The most common configuration
used to this purpose (referred to as hand-to-foot configuration) is shown in Fig. 4b: one
driving electrode and one sensing electrode are placed on the right hand of
the subject under test, while the other two electrodes are applied to the
right foot. However, other electrode configurations have also been used, such
as hand-to-hand (Deurenberg and Deurenberg-Yap, 2002; Ghosh et al., 1997) and
foot-to-foot (Xie et al., 1999; Utter et al., 1999) configurations.
In BIA, the impedance is also influenced by the test frequency. ICW is
surrounded by cell membrane that is essentially an insulator; thus, it does
not contribute to the impedance at low frequency, where the electrical
current flows essentially within the ECW. However, at high frequency, the
current can penetrate the cell membrane; hence, the overall impedance is due
to both ICW and ECW. Figure 4c presents an equivalent electrical circuit of
the whole human body. The current path through ICW can be modeled as the
series of a capacitance due to cell membrane (Ccell) and a
resistance due to ICW electrolytes (RICW), all in parallel with a
resistance due to ECW electrolytes (RECW). At low frequency, the
body impedance is essentially resistive due to ECW, while at higher
frequencies the reactive component due to Ccell is not negligible.
Different BIA techniques have been used that use different
frequencies.
The simplest, and the first to be introduced, is single-frequency BIA
(SF-BIA), where the body impedance, as well as its resistive and reactive
components, is measured at a single frequency, usually 50 kHz, with the
measured impedance being due to a mix of contributions of ICW and ECW. SF-BIA
has been widely used to estimate TBW and FFM. Because of the use of a single
frequency, however, it is not able to reliably estimate the ECW / ICW ratio. In
SF-BIA, the body parameter (usually TBW or FFM) is estimated using empirical
linear equations, depending on the characteristics of the subject under test
(height, weight, etc.). Many commercial systems exist that estimate TBW and
FFM using SF-BIA where the body parameters are automatically estimated by
means of empirical equations stored in the system memory together with
personal physical data.
In multi-frequency BIA (MF-BIA), the ECW / ICW ratio can also be
estimated, in addition to TBW and FFM, using empirical linear equations.
According to Hannan et al. (1994), however, MF-BIA is characterized by poor
reproducibility for frequencies lower than 5 kHz and higher than 200 kHz.
In bioelectrical impedance spectroscopy (BIS), the impedance is measured over
a wide range of frequencies and the data are used to best fit the equivalent
circuit of Fig. 4c to calculate the resistance at zero and infinite frequency
(R0 and R∞, respectively). These parameters are then used in
the equations proposed by Hanai (1968) to predict different body compartments
parameters.
The strongest point of BIA is the possibility to replace invasive and lengthy
laboratory analysis with a quick, non-invasive test that can be carried out
in a medical office. However, BIA is also characterized by restrictions and
limitations, such as lack of accuracy and reproducibility for particular
groups of subjects (pregnant women, people wearing a pacemaker, subjects with
skin lesions and altered fluid balance); need for the tested subject to
follow strict procedures before the test (no alcohol for at least 8 h, no
food and no drinking water for at least 4 h); and the need for periodic maintenance
of both instrumentation and electrodes (Mialich et al., 2014).
One of the first investigations on the prediction of TBW content using SF-BIA
was carried out by Hoffer (1969). In this case, the whole human body was
modeled from an electrical point of view as a cylinder of height H and base
area A. Denoting with ρ the constant electrical
resistivity (accounting for the body conducting
electrolytes) and considering the impedance to be purely resistive, it is
R=ρ⋅HA=ρ⋅H⋅HA⋅H.
Since A⋅H is the volume of the conductor (V), it is
V=ρ⋅H2R.
Thus, the volume of conducting electrolytes is proportional to the ratio
between the squared height and the measured impedance (in practice
coinciding with its resistive component).
The investigation on 34 patients showed a good correlation coefficient (0.92)
between TBW and H2/|Z|, where the impedance modulus was measured at
100 kHz.
Since then, a large number of equations to predict FFM and TBW using SF-BIA
have been proposed. In addition to the ratio of Eq. (9), many other
parameters have been added to the equation for better accuracy, such as
weight, age, gender and ethnicity of the subject. For example, Kyle et
al. (2011) proposed a single equation to predict FFM in subjects from 22 to
94 years old as function of resistance and reactance measured at 50 kHz, height,
weight and gender. Such equation was tested in 343 healthy subjects and
achieved a correlation coefficient of 0.986.
The posture of the subject during measurement is also important. Rush et
al. (2006) compared the measured impedance at 50 kHz in 205 subjects in two
different positions: standing and lying. The results showed that the measured
impedance was slightly higher in the lying position. The same experiment was
carried out by Kagawa et al. (2014) that measured bioelectrical parameters in
boys and adult males using both SF-BIA and MF-BIA: in this case, the impedance
at 50 kHz was found higher in the lying position in boys but no significant
differences were found in adult males. The conclusion was that the posture is
influential in the estimation of the ECW / ICW ratio but not as far as TBW is
concerned.
In general, SF-BIA is capable of estimating FFM and TBW with good accuracy in
healthy subjects. However, it becomes inaccurate when applied to people with
diseases or alterations in body fluid compartments. For example, Haverkort et
al. (2015) estimated FFM and TBW in surgical and oncological patients using
equations developed for healthy subjects and the accuracy was significantly
worse than in the case of healthy subjects. Gudivaka et al. (1999) compared
the results from SF-BIA at 50 kHz and MF-BIA (5 to 500 kHz) in subjects
with altered body water compartmentalization due to infusion with lactated
Ringer solution and/or a diuretic agent concluding that MF-BIA performs
better under conditions of altered hydration. Jaffrin and Morel (2008) compared the
results obtained with SF-BIA and BIS in the estimation of TBW and ECW
finding that BIS is equivalent to SF-BIA in
the estimation of TBW in healthy subjects, but it performs better in patients
with abnormal fluid distribution or with a large amount of adipose tissue.
Ibrahim et al. (2005) analyzed 184 patients affected by Dengue hemorrhagic
fever using SF-BIA at 50 kHz and found that the reactance measured at
50 kHz can be used in classifying the patient risk category.
A more recent approach is segmental BIA (SEG-BIA), where the human body is
modeled composed of five different segments (the two arms, the trunk and the
two legs). By means of multi-electrode configurations placed in different
parts of the body, impedance measurements of the different segments can be
made and local fluid distribution can be estimated. Using an eight-electrode
SEG-BIA with three different frequencies (5, 50 and 250 kHz), Shafer et
al. (2009) analyzed 132 adults exhibiting a large range of body mass indexes
(normal, overweight and obese) and found that body fat percent (BF%) can
be accurately estimated in subjects classified as normal and overweight,
while a significant overestimation was found in subjects classified as obese.
Eight electrodes around the abdominal surface were used with SF-BIA at
50 kHz by Yoneda et al. (2007) to estimate the visceral fat volume. Chinen
et al. (2015) presented a new measurement method featuring six electrodes
(two hands, two armpits and two feet) and working on a wide range of
frequencies (from 20 Hz to 1 MHz) as well as a new equivalent electrical
circuit to interpret the measured data. According to these authors the system
can accurately measure the impedance value of the human trunk (characterized
by lower impedance values than arms and legs).
Local BIA measurements have been also used to characterize the muscular
tissue (Clemente et al., 2014). In 2014, Clemente et al. realized a
measurement system featuring BIS (from 1 to 60 kHz) with four electrodes
placed on the subject arms. The electrical model used to describe the system
was the same as that of Fig. 4c, except that the capacitance membrane was
modeled with a CPE. The investigations showed that, when the muscle is
contracted, the extracellular resistance (Rec) presents a very
significant increment (probably due to the impediment to blood flow caused by
the contraction).
Assessment of food quality parameters
A large number of EIS applications have been proposed to characterize and
screen different food products for quality assessment.
(a) Model of the anatomical structure of a fruit;
(b) Hayden equivalent electrical circuit; (c) double-shell
equivalent electrical circuit.
Fruits
In the case of fruits, EIS has been widely used to estimate the ripening
state as well as to detect defects (such as fruit bruising). As a first
approximation, the (complex) structure of fruits and vegetables can be
modeled with the simplified structure of Fig. 5a (proposed by Labavitch et
al., 1998) where the plant cells are all surrounded by the extracellular
cell wall and delimited by a phospholipid bilayer membrane (plasmalemma).
Inside the plasmalemma, the nucleus is immersed in a viscous fluid
(cytoplasm). The inner boundary of the cytoplasm is marked by a membrane
(tonoplast) that stores the vacuole (Hall et al., 1974). From an electrical
point of view, different electrical models have been proposed to describe the
plant response to the passage of electric current. One of the simplest is the
Cole model, described by the same circuit of Fig. 4c, where Rec
represents the resistance of the extracellular cell wall, Ric the
cytoplasm resistance and Ccell the membrane capacitance. A slightly
more complex model, proposed by Hayden et al. (1969), is shown in Fig. 5b,
where R1 represents the cell wall resistance, R2 the resistance of
the cell membrane, R3 the cytoplasm resistance and C the capacitance
of all membranes. A more detailed model (double-shell model), proposed by
Zhang and Willison (1991), is represented in Fig. 5c, where R1
represents the cell wall resistance, R2 the cytoplasm resistance,
R3 the vacuole resistance, C1 the plasmalemma capacitance and
C2 the tonoplast capacitance. An even more complex model was proposed by
Zhang et al. (1990) but it is rarely used. Moreover, some authors simply
measure the product impedance modulus at a single or multiple frequencies,
while others use empirical models using networks of resistances and
distributed elements.
Many papers on the use of EIS to characterize the physiological state of
fruits were published in the 1990s by Harker and co-workers. Harker and Maindonald (1994)
presented the results of the study on the correlation
between change of impedance and ripening of nectarines. The fruits (cultivar Fantasia) were peeled and the
(removed) tissue was implanted with a linear array of five silver electrodes in
radial position (inter-electrode distances of 10, 20 and 30 mm). The samples
were characterized at frequencies ranging from 50 Hz to 1 MHz. The data
were represented in the Nyquist plot as a semicircle and fitted with the
double-shell model of Fig. 5c. The measured resistance at low frequency
(50 Hz) significantly decreased with the ripening process at 20 ∘C
while small variations were observed in the membrane capacitance and
negligible ones in the high-frequency resistance (300 kHz), thus indicating
that the electrical changes were mainly due to the fruit cell wall
(extracellular). To test how the chilling injury induced by cool storage
affects the fruit electrical parameters during ripening, a set of fruits was
stored at 0 ∘C for several weeks and then monitored during the
ripening process at 20 ∘C: this resulted in a much more limited
variation of the resistance measured at 50 Hz during the ripening process.
Harker and Forbes (1997) published the results of a similar experiment
carried out on persimmons (cultivar Fuyu) using the same experimental setup.
In this case too, only the resistance at low
frequency (50 Hz) was influenced by the ripening process at 20 ∘C,
but the resistance at 50 Hz increased in the first 21 days and then
decreased until day 35 (at the end of the ripening reaching a value lower
than at day 0). When stored at 7 ∘C for several weeks to induce
chilling injuries, only negligible variations were monitored during the
successive ripening at 20 ∘C.
In 2000, Bauchot, Harker and Arnold investigated the impedance changes of
kiwis (cultivar Hayward) during the ripening process at 20 ∘C under
the same frequency range (50 Hz to 1 MHz) (Bauchot et al., 2000). In this
case, different electrode configurations were tested but no significant
variation in the measured impedance was observed (both at low and high
frequency). Jackson and Harker (2000) presented a study on apple bruise
detection by impedance measurements. Apples
(cultivars Granny Smith and Splendour) were measured by a couple of Ag/AgCl
electrodes implanted into the fruit at distance of 35 mm from one another
and to a depth of 3 mm. Signal frequencies ranging from 50 Hz to 1 MHz
were used before and after falls from different heights to induce injury.
The results showed that the measured resistance at 50 Hz decreased
after bruising and the changes were correlated (R2 from 0.36 to 0.72) to
the induced bruise weight.
The results of Harker and co-workers are important since they provide
information on the relationship between the physiological state of different
fruits and changes in their electrical parameters. This technique, however,
cannot be used for online quality control in industrial or commercial
environments since it uses electrodes implanted into the fruit tissue.
For this reason, more recently, several authors monitored electrical
parameters in fruits using non-destructive techniques. Rehman et al. (2011)
designed a system with two spring-loaded cylindrical electrodes (with
diameter of 5 mm) capable of impedance measurements on intact fruits with
skin. A balance was installed in the bottom of the measuring system and a
temperature sensor based on the AD590 IC was used for temperature
compensation. The system was tested by monitoring the impedance changes of
mangoes during the ripening process in the frequency range 1 to 200 kHz
using an electrical model composed of a resistance and a capacitance in
parallel. The effective resistance showed higher variations at low frequency
(1 kHz) with an increase in the first phase of the ripening process,
reaching a maximum after 5 days and then decreasing with further ripening.
Juansah et al. (2012) presented a system for non-destructive
impedance measurements on Garut citrus fruits where the sample was placed
between two conducting plates and monitored in the frequency range 50 Hz to
1 MHz. The measured data were interpreted using a new electrical model and
reasonable correlation with fruit firmness and acidity was found.
Finally, non-destructive impedance measurements using surface electrodes were
used by Vozary and Benko (2010) on apples and by Chowdhury et al. on bananas
(2017a) and mandarin oranges (2017b).
Vegetable oils
In the case of vegetable oils, for frequencies lower than 1 MHz, the system
composed of a couple of electrodes immersed in the sample can be modeled as a
simple capacitance, since edible oils are good insulators (their electrical
conductivity at low frequency can be as low as 0.5 nS m-1) (Prevc et
al., 2013).
Parallel plate capacitive sensor used to investigate the properties
of vegetable oils.
Lizhi et al. (2008) characterized 10 different vegetable oils in the
frequency range 100 Hz–1 MHz. The most useful parameter was found to be
the relative dielectric constant εr (that can be
estimated by measuring the electrical capacitance). εr
depends on the oil's fatty acid composition and moisture content and
decreases for increasing temperature. As for frequency, it is relatively
constant in the range 100 Hz–500 kHz while it decreases with frequency
from 500 kHz to 1 MHz. Capacitive measurements on edible oil are usually
carried out using sensors such as the one shown in Fig. 6, where multiple
parallel plate capacitors are connected in parallel so as to maximize the
capacitance and increase the signal-to-noise ratio. Denoting with A the
electrode area, h the inter-electrode distance, ε0 the
vacuum dielectric constant (8.85×10-12 C2N-1 m-2), N the number of capacitor in
parallel and neglecting fringing field effects, the sensor capacitance
Csensor can be expressed as
Csensor=N⋅ε0⋅εr⋅Ah.
A capacitive sensor has been used by Stevan Jr. et al. (2015) to monitor the
degradation of vegetable oils subjected to thermal stress. When used for
frying with repeated thermal cycles at high temperatures, oils are subject to
alterations in their structure with degradation of organoleptic properties
and potential danger for consumers' health. For this purpose, the possibility
to monitor oil degradation by capacitive measurements has been successfully
proven. In particular, soybean oil samples have been submitted to two
different tests: (1) a constant thermal stress at 180 ∘C for 8 h;
(2) 18 heating cycles between 20 and 250 ∘C. In both cases, an
increase of εr with the number of cycles and progressive
oil degradation evaluated by reference methods has been found. The use of
capacitive measurements to assess the degradation of frying oil has also been
investigated by Yang et al. (2016). In this case, soybean oil has been
subjected to heath stress tests (6 h for five consecutive days) at
180–190 ∘C in the presence and absence of fried dough with
different moisture levels (0, 20, 40 and 60 %). The dielectric parameters
have been measured in the frequency range 40 Hz–100 MHz and the results
indicated that εr, its variation between low and high
frequency as well as the loss factor, all increase with frying time and are
useful parameters to estimate oil degradation with frying time and conditions
(higher moisture of the dough accelerates oil degradation).
Capacitive measurements to estimate εr have been used by
Ragni et al. (2013) to determine the water content in extra virgin olive
oils. Because of the large difference in relative dielectric constant between
water and edible oils (77 versus 3) capacitive measurements are able to detect
water even in very small quantity (178 to 1321 mg kg-1 of oil). For
this purpose, six different frequencies have been tested (500 Hz, 2, 8, 32,
128 and 512 kHz) and the best results have been obtained at 8 kHz, with
R2=0.962 for samples artificially created from a single oil sample by
adding different concentrations of water, while R2=0.818 for real olive
oil samples featuring different composition in terms of fatty acids.
Procedure to estimate the free acidity in olive oil using the
technique discussed in Grossi et al. (2014a).
The possibility to detect the adulteration of extra virgin olive oil with
low-quality vegetable oils by measuring εr has been
investigated by Lizhi et al. (2010). Olive oil samples were artificially
adulterated by spiking the SUT with vegetable oils belonging to the linoleic
and low-content linolenic acid type (sesame, canola, soybean and corn) as
well as to oleic acid type (safflower). The samples were investigated in the
frequency range 100 Hz–1 MHz using techniques of multivariate data
analysis, such as principal component analysis (PCA) and partial least
squares (PLS) regression. The results showed that the adulteration with
vegetable oils belonging to the linoleic and low-content linolenic acid type
can be detected, while in the case of oleic acid adulterants the differences
are probably too small for a reliable discrimination.
A different approach was used by Grossi et al. (2014a) to measure the acidity
of olive oil samples (Valli et al., 2016). In this case, the SUT was mixed
with an hydroalcoholic solution (60 % ethanol, 40 % distilled water)
to create an emulsion. The sensor used for conductivity measurement, with
frequency ranging from 20 Hz to 2 MHz, was a 50 mL vial featuring a couple
of stainless steel electrodes. An electrical model consisting of a
conductance Gm (accounting for the emulsion electrical
conductivity) and a capacitance Cm (taking into account the
dielectric properties) was used. As shown in Fig. 7, when the free fatty
acids of the oil come in contact with the hydroalcoholic reagent, a
dissociation occurs, releasing ions that increase Gm. In the
experiments, 55 olive oil samples with free acidity in the range
0.2–2.3 % were tested and a good correlation (R2=0.9308) was
found with the acidity values obtained with the reference titration method.
This technique was also implemented in the form of a portable electronic
system suitable for in situ measurements in oil mills and packaging centers
(Grossi et al., 2013b, 2014b).
The electrical characterization of an emulsion created with the oil sample
and a suitable chemical reagent was also used by Yang et al. (2014) to
measure the peroxide value in olive oil samples.
Dairy products
EIS has been widely used to characterize different dairy products. Mabrook
and Petty (2003) investigated the relationship between milk composition and
electrical conductivity. Different cow milk samples (full fat, skimmed,
semi-skimmed and lactose reduced) were investigated with a couple of gold
electrodes (15 mm × 6 mm, 1 mm apart) in the frequency range
5 Hz–1 MHz at 8 ∘C, stimulating the sample with a sine-wave
voltage of VRMS=700 mV. The results indicated that the milk
electrical conductivity provides interesting information at high frequency
(100 kHz), where the electrode polarization is negligible and the milk
conductance is affected by both salt and fat content (increasing and
decreasing the conductivity, respectively), while the lactose does not have
significant effects.
In 2006, the same authors used the same experimental setup to detect cow
milk adulteration with added water (in the concentration 0 to 8 %)
(Mabrook et al., 2006). The results have shown that milk conductance varies
with added water, but the variation is highly dependent on the type of milk:
with skimmed milk the conductivity decreases monotonically with increasing
water content, while in the case of full fat milk it presents a local minimum
and with creamy milk it has two local maxima.
A deeper investigation on the use of EIS to detect bovine milk adulteration
was carried out by Durante et al. (2016) considering different types of
adulterants: drinking water, de-ionized water, hydrogen peroxide, sodium
hydroxide and formaldehyde 37 % as well as mixes of these adulterants.
Measurements were carried out using an impedance probe built with two
stainless steel rods spaced 10 mm and immersed for 20 mm in the sample. The
obtained data were analyzed with a k-nearest neighbors algorithm and
discrimination between adulterated and non-adulterated samples was achieved
with 94.9 % correct answers.
Ferrero et al. (2014) built an electronic system for early detection of
mastitis in cows. The system, based on the microcontroller PIC16F876
(Microchip Technology), performed measurements of electrical conductivity at
10 kHz using two stainless steel electrodes 10 mm apart and with effective
area of about 100 mm2. A temperature sensor (LM35) was integrated to
compensate for conductivity variations due to temperature. The detection of
mastitis was realized analyzing the milk electrical conductivity as well as
its variations during the milking time.
Grossi et al. (2012b) characterized ice cream using EIS to cluster the
samples according to their composition. A total of 21 ice cream samples belonging to
three different groups (creamy milk based, frozen yogurt and fruit based)
were characterized in the frequency range 20 Hz–10 kHz using stainless
steel electrodes with two different configurations: the samples were
electrically characterized both in the linear and non-linear region. The
investigation of the non-linear response provided further information
allowing reliable discrimination between milk-based and fruit-based products,
while further discrimination between creamy mixes and frozen yogurt required
measuring the sample pH. The same authors proposed a technique, based on EIS,
to control ice cream freezing and determine the freezing endpoint (Grossi et
al., 2011a). The front grid of an industrial batch freezer (Coldelite
Compacta Top 3002 RTX) was modified to feature two stainless steel electrodes
for the electrical characterization during the freezing process: measurements
of |Z| and Arg(Z) were carried out at time intervals of 1 min in
the frequency range 20 Hz–10 kHz with a test signal of 100 mV. The best
results were achieved at low frequency (20 Hz) with good correlation between
measured impedance and current drawn by the dasher motor (the latter being
the reference technique to assess the freezing status of ice cream mixes).
EIS was used by Kitamura et al. (2000) for online monitoring of yogurt
processing. Yogurt was produced at different fermentation temperatures
between 30 and 42 ∘C, and electrical measurements were carried out at
time intervals of 10 min using two platinum plates (20 × 20 mm,
10 mm apart) in the frequency range 50 Hz–100 kHz. The parameter best
suited to monitor the fermentation process was found to be the ratio
(ZRATIO) between the impedances measured at 100 kHz and at
100 Hz. Curves of ZRATIO presented a bending point corresponding
to the start of coagulation and a good correlation (R2 higher than 0.9)
between ZRATIO and sample acidity as well as hardness were found.
Fringing electric field (FEF) sensor used for spatial imaging of the
moisture present in the sample under test.
Other food products
Moisture is an important parameter in a variety of food products, and EIS can
be used to make quick and non-destructive measurements of such a parameter.
Particularly interesting is the use of fringing electric field (FEF) sensors
that can measure the average moisture and make spatial imaging of the
investigated sample (Li et al., 2006). An example of the FEF sensor is shown in
Fig. 8: a set of concentric electrodes is realized on one side of an
insulating substrate, while on the other side guard rings are used to shield
the sensing electrodes from noise. A current test signal is injected between
a couple of electrodes and the resulting voltage is measured to calculate
the impedance. Since the penetration depth of the electric field is
proportional to the distance between coplanar electrodes, electrodes at
different distances make it possible to measure in different points of the
sample. The main drawback of such a technique is that the measured
impedance increases with electrode distance and this can lead to
inaccuracies when highly spaced electrodes are involved. FEF sensors are
also used in parallel plate configuration, with one in front of the other
and the SUT between them.
Li et al. (2003) used FEF sensors in this configuration to monitor the
moisture content in cookies. In this case, sensors with three concentric
electrodes were used with frequency ranging between 10 Hz and 10 kHz.
Samples with moisture between 0.2 and 1 g were tested and the best results
were achieved at high frequency (10 kHz): the agreement between the moisture
estimated with the proposed method and that obtained with the reference
technique was very good at high moisture level (2 % error) but it was
much worse in the lower moisture range (35 % error).
Bhatt and Nagaraju (2008, 2009)
used a FEF sensor featuring five ring electrodes to measure moisture in bread: measurements were carried out every 24 h
for 5 days using a driving current of 1 mA in the frequency range
50 Hz–100 kHz. The best correlation with moisture was found looking at the
impedance modulus calculated at the frequency where
Re(Z)= Im(Z). The spatial imaging capability of FEF
sensors allowed to estimate moisture both in the bread crust and crumb,
showing that the former features higher values for both moisture and measured
impedance than the latter.
Yang et al. (2013) measured moisture in pork meat with an experimental setup
(very similar to that of BIA) featuring four stainless steel needle electrodes
(1 cm apart) inserted in the meat sample and measurements carried out at
20 ∘C in the frequency range 1–250 kHz. A total of 44 samples were tested (30
for modeling and 14 for validation) and a good correlation was found between
the measured impedance at high frequency and moisture (R2=0.802 for
the modeling set and R2=0.879 for the validation set). Both moisture
and lipid content were determined in pork meat samples by Chanet et
al. (1999). In this case, 72 samples were investigated using two stainless
steel electrodes in the frequency range 5 kHz–2 MHz and two different
approaches were tested: (1) moisture and lipid content were estimated using
the whole spectrum and a PLS algorithm to build a model; (2) the parameters
were estimated with single-frequency measurements at 275 kHz. In both cases,
a good correlation between the estimated parameters and their value obtained
with a reference techniques was found, with the PLS approach resulting only
in slightly better accuracy than single-frequency measurements.
Soltani et al. (2014) determined the moisture content of different
oilseeds (sesame, soybean, canola) using EIS with a capacitive cylindrical
sensor achieving a good correlation using hyperbolic regression lines with a
coefficient of determination higher than 0.9.
EIS has also been used for fish quality assessment. Niu and Lee (2000) used a
three-electrode system (WE and CE made of platinum, while RE was a Ag/AgCl
electrode) with frequency between 0.1 Hz and 100 kHz to determine the
freshness of different types of fish (carp, sea bass and herring). The phase
angle and the imaginary component of the measured admittance have been found
to be the best parameters suited for the purpose. The frequency providing the
best sensitivity varied with the fish type, but in all cases four different
states corresponding to different fish aging could be reliably discriminated.
Fuentes et al. (2013) investigated the use of EIS to discriminate
fresh and frozen-thawed fish. A total of 30 fish samples (sea breams) were investigated
at 20 ∘C in the frequency range 1 Hz–1 MHz using two different
electrode types: (1) screen-printed electrode on an insulator substrate
(arrowhead, AH); (2) two stainless steel needles, 1.5 cm long, featuring a
1 mm diameter and placed 1 cm apart (double electrode, DE). The AH electrode
failed in the discrimination of fresh and frozen-thawed samples, while the DE
electrode measurements with PCA and discriminant analysis (DA) were able to
reliably discriminate between the two products, since frozen-thawed samples featured
lower values of impedance modulus.
Equivalent circuit of a metal coated with an organic polymer in
contact with an electrolyte.
Other EIS applications
In the field of non-biological measurements, one of the most important EIS
applications is corrosion monitoring of metal surfaces. To prevent corrosion
in hostile environment (in particular acid electrolytes), metallic surfaces
are often coated with organic polymers acting as protection barriers. With
continued exposure, however, the acid electrolyte begins to penetrate,
producing metal corrosion, loss of coating adhesion and coating delamination.
In this context, EIS can be used to assess such a degradation as well as to
estimate the quality of coating even before corrosion begins. For this
purpose, a wide frequency range (from mHz to MHz) is used with a three-electrode
system where the WE is the coated metal, while the supporting
electrolyte is the corroding liquid in contact with the coating. The AC
stimulus is often superimposed on a DC voltage corresponding to the open
circuit potential. The electrical circuit most frequently used to model such
a system is shown in Fig. 9 (Loveday et al., 2004), where the electrolyte
resistance Rm is usually very low (1–50 Ω); the coating
capacitance Ccoating tends to be rather low (in the nF range),
since coating is usually thick in order to provide better protection; and the
coating resistance Rcoating is very high when the coating is new
(in the GΩ range). Finally, the parallel of charge transfer
resistance (Rct) and double-layer capacitance (Cdl)
describes the electrochemical process (corrosion) at the metal–coating
interface.
The coating degradation process can be divided into three different phases. In
phase I, the coating is new and no corrosion is present at the metal–coating
interface. Under these conditions, the system can be modeled as the series of
Rm and the parallel of Rcoating and Ccoating.
The contact with the coating surface allows the electrolyte to slowly
penetrate; thus, Ccoating increases (due to absorbed water) while
Rcoating decreases (due to the formation of conductive paths in the
coating structure). When the electrolyte has deeply penetrated the coating
and a portion of the metal substrate is contacted, corrosion reaction starts
and phase II begins, with decreasing Rct (because of increased
reaction rate at the metal interface) and increasing Cdl. As the
corrosion process continues, the system enters phase III where an ever-increasing
portion of the metal surface is corroded and loss of coating
adhesion as well as delamination take place. During phase III, the electrical
parameter variation is highly dependent on type of coating, metal and
electrolyte involved in the process.
In literature, many papers are available about the use of EIS to investigate
underpaint corrosion (Sekine, 1997; Bonora et al., 1995) and to detect the
degree of coating delamination (McIntyre and Pham, 1996).
To monitor underpaint corrosion, all the parameters of the electrical model
must be taken into account. Thus, they must be extracted by means of
non-linear iterative least squares algorithms used to fit the measured data
with the equivalent circuit model. This process, however, is time consuming
and needs dedicated software packages; hence, it not suitable to be
implemented in portable devices for in situ corrosion monitoring. For this
reason, many authors have investigated the possibility to use only raw
impedance data. Mahdavian and Attar (2006) found that the phase angle at 10
kHz can be used to estimate coating resistance and capacitance; Zuo et
al. (2008) extended the investigations of Mahadavian and Attar and discussed
how the phase angle measured at 10 Hz is correlated with the coating
resistance, while the value measured at 15 kHz can be used to detect the
loss of coating protection; Akbarinezhad et al. (2009) suggested that the
area under the Bode plot for |Z| and Arg(Z) provides a parameter
for ranking the performance of organic coating; Zhang et al. (2005) discussed
how the breakpoint frequency (i.e., that at which the phase angle equals
45∘) is proportional to the delaminated area and inversely
proportional to coating resistivity and permittivity; Zhao et al. (2007) used
the changing rate of impedance in conjunction with the self-organizing feature
map network for the analysis of the coating deterioration process. An
handheld system for in situ corrosion monitoring, based on the
microcontroller MSP430F149 and capable of measuring impedances in the range
1 kΩ–10 GΩ was built by Angelini et al. (2006).
A different, but related, EIS application is the investigation of corrosion
inhibition efficiency by different compounds. In this case, the metal surface
is exposed to the electrolyte without any coating and different
concentrations of the investigated compound are added. The system can be
modeled with the electrical circuit of Fig. 3b and the most useful parameter
is Rct (increasing with corrosion inhibition). The compound
inhibition efficiency can be defined as
η=Rct∗-RctRct∗×100,
where Rct∗ and Rct are the charge transfer
resistance in the presence and in the absence of the inhibition compound,
respectively.
Equivalent circuit used to investigate the hardening process of
cement paste.
In particular, this technique has been used in the case of mild steel in
contact with acid electrolyte (such as HCl and H2SO4) to test
inhibition efficiency for different compounds: caffeic acid (de Souza and
Spinelli, 2009); plant leaf extract, such as henna (Lawsonia inermis) (Ostovari et al., 2009), Justicia gendarussa (Satapathy et
al., 2009) and Aquilaria crassna (Helen et al., 2014);
ultrafiltrated oil palm frond lignins (Hussin et al., 2016); pyridine
(Ansari et al., 2015) and pyrazine (Bouklah et al., 2005) derivatives as well
as different synthesized Schiff bases (Dasami et al., 2015; Singh and
Quraishi, 2016).
Another EIS application is characterization of the hardening process of
cement paste (Andrade et al., 1999). This investigation is usually carried
out by means of a two-electrode configuration with a cylinder of cement paste
between two graphite electrodes: between the cement paste and the electrodes
a narrow separation exists (air gap or insulating polyester sheets) to
eliminate any contribution of the cement–electrode interface. Under these
conditions, the electrical model for the system is shown in Fig. 10, where
C1 accounts for the dielectric capacitance associated with the solid
phase, while R2 and C2 are the resistive and capacitive components
of the electrolyte filling cement pores.
This electrical model is valid at frequencies lower than 15 MHz, since for
higher frequencies the system response features multiple time constants
(Keddam et al., 1999). According to Cabeza et al. (2002, 2006), during the
cement drying process, the capacitance C1 (depending on sample thickness
and porosity) presents no significant variation, while C2 features an
irregular and noisy behavior and R2 increases with hardening. Zhang et
al. (2015) showed how EIS can be used to detect cracks in concrete
structures.
EIS is also widely used in battery management systems, where the state of
charge (SOC) and state of health (SOH) can be estimated by looking at the
cell electrical parameters (Huet, 1998; Rodrigues et al., 2000). In the
literature, no single established electrical circuit is present to model the
investigated cell, since different battery technologies (i.e., lead–acid,
nickel–cadmium, lithium–ion) are based on different chemical reactions.
However, the electrical model of Fig. 11 can be used for lithium–ion cells
(Andre et al., 2011).
Equivalent circuit and Nyquist plot of a lithium–ion battery cell.
EIS investigations on batteries are usually carried out in the frequency
range from a few mHz (or even tens of µHz) to several tens of kHz. In
the high-frequency range, the response is dominated by the cell inductance L
(phase I in Fig. 11), while at low frequency the ion diffusion inside the
battery is the main process, to be modeled by the Warburg impedance
ZW (phase IV). At middle frequencies, the Nyquist plot is described by
two semicircles (each modeled by the parallel of a charge transfer
resistance and a CPE) representing the reactions at the two electrodes.
The high-frequency resistance RHF, modeling the ohmic resistance of
the electrolyte, has been widely used to estimate the SOC of different
batteries such as lead–acid (Barton and Mitchell, 1989) and nickel–cadmium
(Diard et al., 1998) since such a parameter can be measured at relatively
high frequency (about 1 kHz) in a short time; in both cases, however, the
RHF versus SOC curve is strongly non-linear and the RHF
sensitivity to SOC is non-negligible only for low values of SOC.
Better results have been obtained by Ran et al. (2010), where SOC of
lithium–ion cells has been estimated using different parameters, such as the
frequency of maximum of the Nyquist plot semicircle, the phase angle and the
equivalent series capacitance.
Cuadras et al. proposed a technique, based on the phase variation of the
measured impedance, to estimate SOC (Cuadras and Kanoun, 2009) and SOH
(Cuadras et al., 2008) in lithium–ion cells.
Furthermore, studies have also been done for the characterization and
diagnosis of proton exchange membrane fuel cells (Yuan et al., 2007), solid
oxide fuel cells (Huang et al., 2007), biofuel cells (Kashyap et al.,
2014), solar cells (Fabregat-Santiago et al., 2005; Glatthaar et al., 2005;
Rock et al., 2014) and microbial fuel cells (He and Mansfeld, 2009).
Finally, EIS is also used to characterize different electronic devices such
as organic semiconductors (Chandra et al., 2007; Garcia-Belmonte et al.,
2008) and supercapacitors (Barsali et al., 2010) to assess the stability of
emulsions (Roldan-Cruz et al., 2016) and to estimate the oil concentration
in metalworking fluids (Grossi and Riccò, 2017).
Conclusions
A review of EIS particularly focused on
its many applications in both biologic and non-biologic fields has been
presented. The response to an electrical stimulus applied to the sample
under test in a wide range of frequencies provides an electrical fingerprint
of the investigated material and can be used to estimate useful parameters.
A number of applications has been reviewed. In the field of bacterial
contamination analysis, the microbial concentration is estimated either by
the changes in the medium electrical parameters due to bacterial metabolism
(impedance microbiology) or by measuring the charge
transfer resistance of an electrode with an immobilized bioreceptor
(impedance biosensor): while the first case is suitable for easy
implementation in the form of an embedded electronic system, the second is
more complex but results in shorter time response and improved selectivity
towards specific bacterial strains. The application of EIS to the analysis of
human body composition provides quick, non-invasive tools for health
monitoring and early detection of potential pathologies. In the field of food
screening for quality assessment, EIS offers an alternative to laboratory
analysis for a wide range of products, avoiding sample shipments with
significant advantages in terms of shorter time response and cost savings.
Finally, in the case of corrosion analysis and battery management, EIS offers
the possibility of low-cost portable electronic system for online monitoring.
While the presented applications are those most discussed in literature, a
large number of others also exist, making EIS a very useful and
versatile tool for material investigation.
No data sets were used in this article.
Abbreviations
ADCAnalog-to-digital converterBIABioelectrical impedance analysisBISBioelectrical impedance spectroscopyCECounter electrodeCPEConstant phase elementDADiscriminant analysisDACDigital-to-analog converterDEPDielectrophoresysDTDetect timeECWExtracellular waterEISElectrical impedance spectroscopyFEFFringing electric fieldFFMFat-free massFMFat massICWIntracellular waterIDAInterdigitated arrayIMImpedance microbiologyKKKramers–Kronig relationLTILinear time invariantMF-BIAMulti-frequency BIAPBSPhosphate-buffered salinePCAPrincipal component analysisPCTPlate count techniquePLSPartial least squaresREReference electrodeSAMSelf-assembled monolayerSEG-BIASegmental BIASF-BIASingle-frequency BIASOCState of chargeSOHState of healthSUTSample under testTBWTotal body waterWEWorking electrodeWSEWorking sensing electrode
The authors declare that they have no conflict of
interest.
Edited by: Marco Jose da Silva
Reviewed by: three anonymous
referees
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