Food Engineering Reviews
https://doi.org/10.1007/s12393-020-09256-7
Microbial Inactivation by Non‑equilibrium Short‑Pulsed Atmospheric
Pressure Dielectric Barrier Discharge (Cold Plasma): Numerical
and Experimental Studies
Ender H. Arserim1 · Deepti Salvi1 · Gregory Fridman2 · Donald W. Schaffner1 · Mukund V. Karwe1
Received: 14 January 2020 / Accepted: 14 September 2020
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Microbial inactivation efficacy of plasma generated by a custom-made floating electrode dielectric barrier discharge (FEDBD) or cold plasma at three different frequencies (1 kHz, 2 kHz, and 3.5 kHz) was experimentally evaluated for its inactivation of the pathogen surrogate Enterobacter aerogenes on a glass surface to obtain inactivation kinetics. COMSOL Multiphysics® was used to numerically simulate the amount and the distribution of reactive species within an FE-DBD system.
Microbial inactivation kinetics was predicted using species concentrations and microbial inactivation rates from the literature
and compared with experimental data. The results showed that the FE-DBD plasma treatment achieved a microbial reduction
of 4.3 ± 0.5 log CFU/surface at 3.5 kHz, 5.1 ± 0.09 log CFU/surface at 2 kHz, and 5.1 ± 0.05 log CFU/surface at 1 kHz
in 2 min, 3 min, and 6 min, respectively. The predicted values were 4.02 log CFU/surface, 4.10 log CFU/surface, and 4.56
log CFU/surface at 1 kHz, 2 kHz, and 3.5 kHz, respectively. A maximum 1 log difference was observed between numerical
predictions and the experimental results. The difference might be due to synergistic interactions between plasma species, UV
component of FE-DBD plasma, and/or the electrical field effects, which could not be included in the numerical simulation.
Keywords Dielectric · Barrier discharge plasma · Microbial inactivation · Mathematical modeling · Inactivation kinetics
Introduction
Significant effort and resources have been directed towards
improving the efficiency and sustainability of the food supply chain. Besides developing new approaches for reducing food waste and reducing energy consumption, there is
a growing demand for high-quality food products that are
minimally processed, safe, and affordable. The development
of novel nonthermal food preservation processes is of great
interest for minimally processed foods. Researchers seek to
increase food safety and enhance shelf life while maintaining
important food quality attributes. One emerging nonthermal technology that may help is cold atmospheric pressure
* Mukund V. Karwe
mkarwe@sebs.rutgers.edu
1
Department of Food Science, Rutgers, The State University
of New Jersey, 65 Dudley Road, New Brunswick,
New Jersey 08901, USA
2
C. & J. Nyheim Plasma Institute, Drexel University, 200
Federal St. Suite 500, Camden, New Jersey 08103, USA
plasma (CAPP). The antimicrobial efficacy at or near room
temperature of cold plasma makes it desirable to process
temperature-sensitive foods [49].
Plasma, the fourth state of matter, can be described as
a partially or fully ionized gas [32]. If enough energy is
applied to a gas, this breaks apart interactions between molecules and atoms, which results in the generation of charged
and neutral species, including ions, electrons, and free radicals, while releasing radiation at different wavelengths. The
mixture of species and radiation is called plasma. Plasma
can be classified as a hot plasma or cold plasma, depending upon its temperature. The Sun and the interior of stars
are examples of hot plasma, and the Aurora Borealis is an
example of cold plasma in our universe [7]. This research is
focused on non-equilibrium short-pulsed discharge, or “cold
plasma” in which the temperatures of neutral and reactive
species are at or near room temperature while electrons are
at ~ 1 eV (~ 104 K).
Different theories have been developed to explain the
antimicrobial effects of CAPP. The most common proposed mechanism of action is the oxidation of cell constituents which is attributed to reactive oxygen such as
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Food Engineering Reviews
superoxide (O 2−), hydrogen peroxide (H 2O 2), hydroxyl
radicals (OH), ozone (O3), and reactive nitrogen species
such as nitric oxide (NO) and others. The interactions
between these reactive species and cellular macromolecules, such as lipids, proteins, and DNA, can result in the
death of the cell [4, 11, 42].
Researchers have shown that CAPP can effectively
inactivate different microorganisms with high food quality
retention [2, 12, 13, 50]. Some research has suggested that
plasma has adverse effects on some food quality parameters changing color and contributing to oxidation [23, 25,
34]. While some microorganisms (e.g., Escherichia coli)
are more susceptible to plasma, spore-forming bacteria
such as Bacillus subtilis are less so [20, 30, 33]. Different
plasma generation methods can also affect the degree of
microbial inactivation observed [5, 39, 45].
While the application of plasma technologies to inactivate microorganisms has been studied, the prediction
of microbial inactivation kinetics would allow the comparison of different plasma process technologies on the
reduction of microbial populations. Very few studies
have focused on the mathematical modeling of microbial
inactivation by cold plasma treatment. Some studies have
focused on microbial inactivation by plasma as functions
of time, process gas type, gas flow rate, frequency, or voltage [48, 49]. While many of the operating parameters for
the plasma generating equipment can affect the amount
and type of molecular species generated, microbial inactivation by cold plasma has been ascribed mainly to the
reactive species [8, 15]. A generalized reactive speciesbased microbial inactivation model would allow comparison between studies and might point a way toward more
effective treatment conditions.
The overarching goal of this study was to investigate the
microbial inactivation efficacy of CAPP on Enterobacter
aerogenes in a model system and develop a mathematical model to predict microbial inactivation kinetics. A key
objective was to predict the plasma species distribution
and concentration at a microbially contaminated surface,
and couple it with the microbial inactivation kinetics from
the literature to predict the microbial inactivation level and
compare with experimental data. The transport of reactive
species was numerically solved to obtain species distribution
between plasma electrodes in a custom made floating electrode dielectric barrier discharge (FE-DBD) device (Drexel
University, PA). The same equipment was used to conduct
microbial inactivation experiments [10]. Numerical simulation of reactive species distribution was carried out by using
COMSOL Multiphysics®. Numerically predicted reactive
species concentrations and their distributions were coupled
with microbial inactivation rate from the literature to estimate microbial inactivation, and the results were compared
with experimental data.
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Material and methods
Interacting surface
Since surface roughness can affect the microbial inactivation
efficacy of cold plasma [3, 21]. Fisherbrand™ glass microscope slide (0.001 m × 0.0254 m × 0.0762 m) was used as a
model surface. The surface roughness of these glass slides
was 0.28 ± 0.02 μm [21], which is smaller than a typical
microbe, and therefore, it could be considered a “smooth”
surface. Enterobacter aerogenes (0.6–1.0 μm in diameter
and 1.2–3.0 μm long) were spot inoculated on the glass slide
surface in an area with dimensions of 0.0254 m × 0.0254 m.
Microbial inactivation results were reported in terms of log
CFU per area (1 in2 or 0.000645 m2).
Bacterial culture
Nalidixic acid-resistant Enterobacter aerogenes B 199A
(Vivolac Cultures, Indianapolis, IN) is a non-pathogenic,
gram-negative bacterium [31], with similar attachment characteristics to Salmonella spp. [53]. Prior research has shown
that Enterobacter, Salmonella, and four other Enterobacteriaceae (Escherichia, Citrobacter, Arizona, and Shigella)
are part of a single, large cluster within the Enterobacteriaceae, with little or no evidence of subdivisions into tribes
or genera when analyzed according to 105 different features
including biochemical and physiological characteristics [28].
Nalidixic acid inhibits the growth of other microorganisms
and allows the growth of only E. aerogenes B 199A strain.
The frozen bacterial culture was stored at − 80 °C in
80% sterile glycerol. Bacteria from frozen stock were transferred, and the quadrant was streaked onto Tryptic Soy Agar
(BD Difco, Sparks, MD) containing 50 µg/ml nalidixic acid
(Sigma Chemical Co., St. Louis, MO) (TSA-na) and incubated at 37 °C for 24 h. One colony of the bacterium was
transferred to 30 ml of Bacto Tryptose Soy Broth (Bacto,
BD, Sparks, MD) containing nalidixic acid (50 µg/ml) and
incubated at 37 °C for 24 h. The final bacterial concentration
of suspension was 8.27 ± 0.11 log CFU/ml. This suspension
was used for all microbial inactivation experiments.
Floating electrode dielectric barrier
discharge Plasma (FE‑DBD)
The FE-DBD (at Drexel University, Camden, PA) system
used in our experiments is shown in Fig. 1. The system uses
a microsecond-pulsed power supply in which sinusoidal discharges were generated between two electrodes, as shown in
Fig. 1 a and c. The rounded powered copper electrode was
Food Engineering Reviews
Table 1 Experimental conditions for FE-DBD plasma
Frequency (kHz)
1
2
3.5
Exposure time (s)
0
90
180
270
360
0
45
90
135
180
0
30
60
90
120
Experimental design for FE‑DBD Plasma
treatment
Fig. 1 a FE-DBD or cold plasma unit (electrodes and power supply). b Powered copper electrode (0.035 m diameter) and clear fused
quartz (0.05 m diameter and 0.001 m thick). c The purple glow is the
plasma generated between the electrodes
0.035 m in diameter (Fig. 1b). Its open end was covered
with 0.05 m diameter and a 1-mm-thick clear fused quartz
(Technical Glass Products, Painesville, OH), and the copper
electrode was enclosed in polyetherimide (Ultem) (Fig. 1b).
The ground electrode was a standard stainless-steel plate
(0.15 m × 0.25 m) placed at 2 mm from the quartz surface
(Fig. 1c).
Electrical Characterization of Plasma
The power supply was connected to a power analyzer, and
the power required measured by a wattmeter (Electronic
Educational Device, Denver, CO, USA). Power was measured five times when the plasma system was operated at each
of three different frequencies (1 kHz, 2 kHz, and 3.5 kHz)
with a 0.001 m air gap between a high-voltage electrode and
a glass slide (1 mm thick). The distance between electrodes
was increased (~ 50 mm) to measure power when the same
voltage was applied, but no plasma was generated. The difference between the two measurements was reported as the
power consumed in the generation of plasma. The average
values of power of this FE-DBD system were 4.80 ± 0.5 W,
11.18 ± 0.2 W, and 15.13 ± 0.3 W at 1 kHz, 2 kHz, and
3.5 kHz, respectively. The power of the plasma was divided
into the number of pulses for each frequency to obtain power
consumed per cycle or pulse. The average power of the FEDBD plasma system per cycle was 5 × 10–3 W/cycle. Average power per cycle was used in the numerical simulation
for the energy input of FE-DBD.
The bacterial suspension of E. aerogenes (50 µl) was spot
inoculated directly from the broth to the glass surface (area
of 0.0254 m × 0.0254 m). The glass slide was held in a
laminar flow biosafety cabinet for 2 h at room temperature
for drying before treatment. The glass slide was exposed to
plasma for different exposure times and at different plasma
power cycles using the FE-DBD plasma system at conditions
shown in Table 1.
After plasma treatment, the glass slide was transferred
to a sterile bag with 20 ml 0.1% peptone broth (BD Difco,
Sparks, MD), gently massaged to dislodge any viable cells
on the slide. The 0.1% peptone containing any viable cells
was serially diluted further in 0.1% peptone as needed
(mostly 3 times, tenfold at a time) and 0.1 ml of peptone
water was plated on tryptic soy (enriched with 50 µg/ml
nalidixic acid) agar on duplicate plates. Plates were incubated at 37 °C for 24 h. The number of colonies between
30 and 300 were reported. Each treatment condition was
repeated 5 times.
The detection limit was calculated based on a
single viable cell being recovered in 0.1 ml from
the original 20 ml massaged with the glass slide
(1 CFU/0.2 ml × 20 ml = 100 CFU/slide or 2 log CFU/
slide).
Optical emission spectroscopy
An optical emission spectrometer (OES) by AvaSpecULS2048L-EVO (Avantes, Broomfield, CO, USA) was used
to relatively quantify the presence of reactive (and potentially antibacterial) species in the CAPP. The OES had a
2-m-long 600-µm fiber cable with an F400 COL-UV/VIS
collimating lens in the range from 200 to 1100 nm. The
Avasoft8 software recorded the relative absorbance value
of species. Each measurement was performed three times
with an integration time of 10,000 ms and was recorded as
emission spectra. The spectral peaks were then identified
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Food Engineering Reviews
and processed by using the Avasoft8 software and spectrum
analyzer for the presence of reactive species (v1.7, Brno,
Czech Republic).
Statistical analyses
Statistical analyses were performed using SPSS software
(version 24.0 IBM Corp, NY). All experimental data were
subjected to one-way analysis of variance (ANOVA), and
means were compared using the Tukey method with a 95%
confidence interval.
Mathematical modeling
The mathematical model numerically solved for the diffusion
of generated reactive species and their secondary reactions
to predict the concentrations and distributions of the reactive species in the FE-DBD plasma zone and on the glass
surface. COMSOL Multiphysics® (version 5.4) transport of
diluted species sub-module under the chemical engineering
module was used to predict distributions and concentrations
of reactive species. The calculated surface concentration of
reactive species was used to predict microbial inactivation
kinetics of E. aerogenes, and the details are described in the
following sections.
Geometry
The model addressed the cylindrical domain between the
two electrodes with a glass slide on the bottom surface. The
problem was assumed to be 2D axisymmetric without swirl
to reduce computational time, so the computational domain
consisted of a radial slice (center to edge) between the two
electrodes. When revolved around the vertical (y) axis, this
covers the entire domain under consideration. The radial
slice implies that the glass slide area was circular, so the
square inoculated region on the glass slide in the simulation was approximated as a circle. The domain included
the region between the high-voltage electrode, which had
a radius of 0.0175 m and the ground electrode. There was a
0.002-m gap between the electrodes, and the glass slide was
0.001 m thick, but it did not cover the entire region between
the electrodes (see Fig. 2).
FE‑DBD Plasma chemistry
Plasma has complex chemistry due to the numerous potential
reactions and generation of the many possible species, especially when the air is used as the gas supply. The FE-DBD
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Fig. 2 Radial cross section of computational domain showing the
region between the electrodes and the glass slide. The computational
domain is shown in gray for FE-DBD (the radius of high voltage electrode was 0.0175 m, the gap between electrode and glass slide was
0.001 m, and the length of glass slide was 0.0254 m)
system generated a thermodynamically non-equilibrium cold
plasma [7], where the plasma temperature was near or at
room temperature. The primary reactive species generated
in the FE-DBD air plasma that has high antimicrobial inactivation activity are O•, OH, O3, and H2O2 [15]; [49], so
only these species were included in the mathematical model
describing the transport of the species.
The rate of reactive species generation in cold plasma can
be obtained by using the species G-value from the literature,
which represents the number of active species molecules
generated by specific input energy (molecules per 100 eV).
It can be expressed as a function of the SIE (specific input
power) and the removed/produced amount of gas (∆C), in a
general form [24].
G − value(molecules per 100eV ) =
ΔC
x 1.6 x10−19
SIE
(1)
where ∆C is the number of plasma species produced
(molecules/m3/s), and SIE is the specific input power (W/
m3).
The G-value of O• and OH, and reactions (2) and (3),
were used to obtain the generation of the O•, OH, O3, and
H2O2 species for the FE-DBD plasma system. Reactions (2),
(3), (4) and (5) were used for the decay of O•, OH, O3, and
H2O2, species in the model, respectively.
k2
OH + OH → H2 O2
)
(
GOH = 9 molecule
[9]
100eV
)
(
m3
[35]
k2 = 2.81x10−18 molecule⋅s
k3
O∙ + O2 → O3
(2)
(3)
Food Engineering Reviews
GO∙ = 3.2
(
)
molecule
100eV
−17 3
k3 = 3.01x10
[9]
m ∕(molecule ⋅ s) [35]
k4
(4)
H2 O2 → H2 O + O2
k4 = 8.58x10−2 1s [35]
k4
(5)
O3 → O∙ + O2
k5 = 5.22x10−6 1s [35]
to define decay or generation of reactive species O•, OH,
O3, and H2O2 in the numerical simulation. The generation
and the decay of the species were defined everywhere in
the simulation domain (Fig. 3).
Boundary conditions for predicting
the distribution of charged species
The boundary conditions imposed in the mathematical
model are described in Fig. 3. No flux boundary (Ji = 0)
condition was employed at electrode surfaces and the glass
slide. The flux vector in the presence of the diffusion and
reaction becomes,
Governing equations for predicting
the distribution of charged species
Ji = −Di ∇Ci
It was assumed that the air/plasma column between two
electrodes was stagnant, and mass diffusion was the only
mass transfer mechanism. The governing mass transfer
equation of diffusion of species is
(
)
𝜕Ci
+ ∇ ⋅ −Di ∇Ci = ±Ri
𝜕t
(6)
where Ci is the concentration of the species (mol/m3),
Di is the mass diffusion coefficient (m2/s) of species i in
air. The values for Di were obtained from the literature
and are given in Table 2 [6, 16, 36]. In Eq. (6), Ri is the
rate of generation of species i (mol/(m3·s) and was used
Table 2 Mass diffusivity values of various reactive species in air
Species (i)
Di,air (m2/s) × 105
Reference
O2
O3
O
OH
H2O2
1.9
3
6.1
7.1
0.2
[6]
[16]
[36]
(7)
Open boundary conditions were imposed on the side,
which describes boundaries in contact with a large volume. The species can both enter and leave the domain
from open boundaries, which can be described mathematically as follows.
{
−n ⋅ Di ∇Ci = 0ifn ⋅ u ≥ 0
(8)
Ci = Co,i ifn ⋅ u ≤ 0
Meshing for FE‑DBD Plasma simulation
The computational domain was discretized using a triangular mesh. The mesh was generated by commercial software
COMSOL Multiphysics® mesh generator module (Version
5.4). The initial mesh was finer and consisted of 10,119
triangles, 537 edge elements, and 7 vertex elements. During the numerical simulation runs, the computational mesh
was adapted repeatedly based on the species concentration
gradient until the mesh independent solution was obtained.
The final grid had 19,187 triangles, 835 edge elements,
and 7 vertex elements (Fig. 4).
Solver and computational time
Fig. 3 Boundary conditions for the transport of reactive species (no
flux boundary condition at electrodes surfaces and glass surface and
open boundary on the side) and generation and the decay between
surfaces in the domain
COMSOL Multiphysics® solver was used in the numerical
simulation [8] and is based on an implicit time-dependent
backward differentiation formula (BDF) solver algorithm.
If the residual values for continuity and species concentration were all less than the user-defined relative tolerance
value of 10–3, then the solution was considered to be converged. The computational time for a 2D axisymmetric
model was 1 h and 47 min on a DELL Inc. workstation
with Intel® Xeon® processor E5640 and 24 GB RAM.
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Food Engineering Reviews
Fig. 4 Final computational
mesh used for numerical
simulation (19,187 triangles,
835 edge elements, and 7 vertex
elements)
Empirical microbial inactivation kinetics
models
))
( (∑i
Ci ki t
S(t) = exp −
1
(11)
Microbial inactivation was assumed to follow a first-order
reaction and therefore could be described as
Results and discussion
S(t) = e−kt
(9)
where S(t) is the survival ratio N/N0 at a given time, and
t is the time (s).
The exponential model parameters k (1/s) was modified
as a function of the numerically predicted reactive species
concentration to predict the microbial inactivation kinetics
of E. aerogenes.
k=
∑i
1
Ci ki
(10)
where C i (mol/m3) is the concentration of individual
species, and ki is the microbial inactivation rate of individual species (m3/(mol s)). The ki values for reactive species were obtained from previous studies (Table 3) [1, 43,
49]. Microbial inactivation rate values for Salmonella were
used in the calculations as Salmonella is closely related to
E. aerogenes. The inactivation rate of H2O2 was calculated
based on the oxidation-reduction potential (ORP) of H2O2
due to a lack of published inactivation data.
If we combine Eq. (9) and Eq. (10), we can rewrite
the exponential model as a function of time and species
concentration.
Identification of FE‑DBD Plasma reactive species
Figures 5, 6, and 7 represent the typical emission spectrum from FE-DBD air plasma at three different frequencies (1 kHz, 2 kHz, and 3.5 kHz). The observed emission
spectrums illustrate the relative concentrations of nitrogen,
oxygen, and hydrogen ions and their presence.
Atmospheric pressure cold air FE-DBD spectra showed
emission peaks in all UV range (200–400 nm) and few in
the visible light range. Metastable singlet state of oxygen
(O I), oxygen (O II), nitrogen (N2R I), nitric oxide (NO
I), hydroxyl radical (OHR I), and hydrogen ions (H2 I) at
Table 3 Microbial inactivation rate of Salmonella for individual reactive species
Species (i)
Ki( m3/(mol s)
Reference
H2O2
O3
OH
3.6
3.9
5.2
[49]
[43]
[1]
13
Fig. 5 Emission spectra of FE-DBD plasma at 1 kHz in vis–NIR
region (the gap between electrodes was 1 mm)
Food Engineering Reviews
Fig. 6 Emission spectra of FE-DBD plasma at 2 kHz in vis–NIR
region (the gap between electrodes was 1 mm)
Fig. 8 The effect of FE-DBD plasma treatment on microbial inactivation of E. aerogenes at three different frequencies (triangles, 1 kHz;
circles, 2 kHz; squares, 3 kHz) at room temperature (the gap between
electrodes was 1 mm, vertical bars indicate a standard error, n = 5)
Fig. 7 Emission spectra of FE-DBD plasma at 3.5 kHz in vis–NIR
region (the gap between electrodes was 1 mm)
different energy states were detected, which would give way
to the formation of antimicrobial reactive species via electron impact excitation and dissociation [46].
Microbial inactivation efficacy of FE‑DBD
Figure 8 represents the vegetative cell population (log
CFU/surface) of E. aerogenes as a function of different
FE-DBD plasma exposure times for three different frequencies. The average initial attached populations of E.
aerogenes were 7.12 ± 0.05, 7.08 ± 0.09, and 7.11 ± 0.09
log CFU/surface at 1, 2, and 3.5 kHz, respectively. The
results showed that the FE-DBD plasma was able to reduce
the population of E. aerogenes by 4.6 ± 0.5 log CFU/surface area at 3.5 kHz, 5.1 ± 0.09 log CFU/surface area at
2 kHz, and 5.1 ± 0.05 log CFU/surface area at 1 kHz on
a glass surface in 120 s, 180 s, and 360 s, respectively (±
indicates the standard error).
It is known that reactive species concentration increases
with increasing frequency due to increased power input
[52]. This results in a lower process time to achieve a similar level of inactivation at a higher frequency level (Fig. 8).
The microbial inactivation efficacy (rate of inactivation) of
FE-DBD decreased slightly with increasing process time.
Such a slowing of the inactivation rate for multi-layered/
stacked microorganisms has been reported by other researchers [22, 26, 51].
The FE-DBD plasma from the air contains reactive oxygen species, reactive nitrogen species, and UV radiation.
It is possible that the achieved inactivation was due to the
combined effect of all these components [44]. UV radiation
might damage the cell membrane and promote production of
peroxidases [27]. The reactive species can damage the cell
membrane because of their high oxidation potential [18, 29].
Besides, the presence of nitrogen species inside the cell can
alter the pH and the cell functionality [19].
Figure 9 evaluates the effect of the number of pulses on
microbial inactivation efficacy of FE-DBD plasma, by plotting E. aerogenes concentration as a function of the number
of pulses instead of time. There was no statistically significant difference observed in the rate of microbial inactivation
per pulse at 3 different frequencies (p < 0.05). It can be
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Fig. 9 The effect of the number of pulses on microbial inactivation
of E. aerogenes on a glass surface (triangles, 1 kHz; circles, 2 kHz;
squares, 3 kHz) at room temperature (the gap between electrodes was
1 mm, vertical bars indicate a standard error, n = 5)
concluded that the rate of microbial inactivation per pulse
was independent of frequency.
Numerically predicted species distribution
of FE‑DBD Plasma system
Figures 10, 11, and 12 show the concentrations and the distributions of the predicted O•, OH, O3, and H2O2 species at
1 kHz, 2 kHz, and 3.5 kHz, respectively. The amounts of
reactive species increased with increased frequency. This
increase can be explained by the increased power input.
Higher frequency values increase the energy input, which
results in a higher number of species generation in the FEDBD plasma system.
Since diffusion was the only transport mechanism that
was considered for calculating the species distributions in
the FE-DBD plasma system, the concentrations of species
were higher in the middle of the electrode, and the concentrations of species were lower along the edges of the electrodes. O3 and H2O2 had higher predicted concentrations in
the FE-DBD plasma system due to their greater stability and
longer half-life which played an important role in microbial
inactivation. The reactive oxygen species have been shown
to have a larger contribution to lethality compared to other
plasma components such as ions, UV light, or reactive nitrogen species [40, 41]. The numerical model assumed isothermal conditions and the effects of gravity (buoyancy) were
not included. In an FE-DBD plasma, electrons are at a much
higher temperature compared to the other particles such as
ions and neutral species which are near room temperature.
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Fig. 10 Concentration distribution of a O•, b O3, c OH, and d H2O2
in the FE-DBD plasma after steady state at 1 kHz
Since FE-DBD plasma is dominated by ions and neutral particles, the effect of temperature on the movement of plasma
species due to buoyancy can be considered negligible [9].
Comparison between numerical predictions
and experimental data for microbial inactivation
in an FE‑DBD plasma system
Comparisons between numerical predictions using a modified exponential model (Eq. 10 and Eq. 11) with an average
concentration of numerically predicted OH, H2O2, and O3
species at the interacting surface and experimental results
at the three frequencies are shown in Figs. 13, 14, and 15.
Microbial inactivation model based on reactive species
predicted 4.1, 4.6, and 4.5 log CFU/surface reduction in
the population of E. aerogenes for 360, 180, and 120 s of
FE-DBD plasma processing, at 1 kHz, 2 kHz, and 3.5 kHz,
respectively. A maximum difference of 1 log was observed
between the numerical predictions and the experimental
results. There are several possible explanations for this difference. The numerical simulation domain was taken to be
two-dimensional axisymmetric without swirl to save on
computational time. For the simulation purposes, the square
Food Engineering Reviews
Fig. 11 Concentration distribution of a O•, b O3, c OH, and d H2O2
in the FE-DBD plasma after steady state at 2 kHz
inoculated region on the glass slide was approximated by
a circumscribed circle. The numerical simulation also did
not include microbial inactivation due to singlet oxygen,
superoxide, nitric oxide, and UV and their combined effect
[22, 44]. Only reactive oxygen species were included in the
predictive model. This could explain the underprediction of
microbial inactivation by numerical simulation model compared to experimental data.
It has been reported that plasma operating pressure influences the generation of UV radiation. Low pressure plasma,
i.e., vacuum plasma could potentially release UV at the
range between 200 and 290 nm [38]. Atmospheric pressure plasma generally emits UV radiation between 305 and
390 nm. UV radiation in this range is not effective enough
to inactivate microorganisms [47]. Many researchers have
reported that UV radiation has minimal or no significant
effect in terms of microbial inactivation in atmospheric
plasma processing. There is no general agreement among
researchers on the role of UV radiation in atmospheric pressure plasma decontamination processes [14, 15, 17, 29, 37].
Hasan [15] used a log-linear model based on reactive species
to predict microbial inactivation and included a UV component in the model. But the effect of UV in that predictive
model was found to be minimal compared to the effect of
Fig. 12 Concentration distribution of a O•, b O3, c OH, and d H2O2
in the FE-DBD plasma after steady state at 3.5 kHz
Fig. 13 Experimental and predicted microbial inactivation kinetics of
E. aerogenes at 1 kHz for FE-DBD plasma system (error bars indicate
the standard deviations and n = 5; data that do not share the same letter are significantly different (P < 0.05))
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Food Engineering Reviews
Fig. 14 Experimental and predicted microbial inactivation kinetics of
E. aerogenes at 2 kHz for FE-DBD plasma system (error bars indicate
standard the deviations and n = 5; data that do not share the same letter are significantly different (P < 0.05))
and the plate count detection limit was 2 log CFU/surface.
There might be more than 5 log inactivation on those conditions after plasma processing, but it could not be confirmed
due to the inherent limits of the plate count method. To make
use of any novel process (including plasma technology) in
the food industry, the process must be understood or at least
be predictable to a large degree. The results of this study
give a preliminary understanding of complexities inherent
in the prediction of microbial inactivation by plasma. These
results should help in the design of plasma equipment or
may provide a starting point for future microbial inactivation experiments.
In order to find out the effect of the variation in the
G-value on the numerically predicted microbial inactivation,
the numerical simulation was carried out by using G-values
with ± 10% error. Microbial inactivation increased by about
12% at 10% higher G-value and decreased by about 12% at
10% lower G-value, which was not unexpected.
Conclusions
We experimentally investigated microbial inactivation efficacy of FE-DBD and developed an empirical microbial
inactivation model as a function of numerically predicted
reactive species. The inactivation model used a COMSOL®
Multiphysics–based numerical simulation model to predict
reactive species concentrations and distributions in the FEDBD system. The predicted species concentrations were
used to estimate microbial inactivation kinetics. Our results
showed that FE-DBD plasma treatment reduced the population of E. aerogenes on the model system by up to 5 log
CFU/surface. The microbial inactivation model based on
numerically predicted reactive species and microbial inactivation rate from the literature were under-predicted microbial inactivation compared to experimental results. There
was a maximum of 1 log difference between the prediction
results and the experimental results.
Fig. 15 Experimental and predicted microbial inactivation kinetics
of E. aerogenes at 3.5 kHz for FE-DBD plasma system (error bars
indicate the standard deviations and n = 5; data that do not share the
same letter are significantly different (P < 0.05))
reactive species in the plasma. The model can be improved
by including more species (e.g., reactive nitrogen species,
peroxyl nitrate, etc.), which would also require testing the
microbial inactivation efficacy of each reactive species individually. This would also potentially require the addition of
models for the interaction effects of UV and reactive species.
The model also over predicted microbial inactivation at
3.5 kHz for 2 min. This might be explained by the detection
limit of the total plate count method. There were no colonies
recovered at 3.5 kHz for 2 min plasma process conditions,
13
Acknowledgement Authors would like to thank C. & J. Nyheim
Plasma Institute, Drexel University Plasma Agricultural laboratory
members for their help in the operation of FE-DBD plasma; Dr.
Juzhong Tan, Department of Food Science, Rutgers University, for
his valuable inputs.
Funding This study is financially supported by the Turkish Ministry of
Education. Partial funding is provided by the New Jersey Agricultural
Experiment Station.
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