PIERS Proceedings, Stockholm, Sweden, Aug. 12–15, 2013
754
Effect of Stationary Magnetic Fields on Different Bacterial Strains
P. Krepelka1 , E. Hutova1 , and K. Bartusek2
1
Department of Theoretical and Experimental Electrical Engineering
Brno University of Technology, Technicka 12, Brno 612 00, Czech Republic
2
Institute of Scientific Instruments, Academy of Sciences of the Czech Republic
Kralovopolska 147, Brno 612 64, Czech Republic
Abstract— The authors discuss the effects of stationary magnetic fields on the growth, morphology, and chemical composition of colonies of bacteria. Homogeneous and gradient stationary
magnetic field modified these bacterial cells by a exhibiting the magnetic flux density value of
5.5 to 8 T/m. The modified bacterium was compared with a reference sample placed in the
same conditions outside the magnetic field. Using neodymium magnets, we generated ten different magnetic field types of diverse densities and defined configurations. The model of the
magnetic field structure was computed by means of a finite element method (ANSYS) and experimentally verified via measurement with a Hall effect sensor. Within the follow-up stages of
the research, changes in the chemical composition of bacterial cells will be monitored. We used
NIR spectroscopy to examine the influence of a magnetic field on the metabolism of the bacteria.
Although the interpretation of the NIR spectra is a difficult task, this method is suitable for the
measurement of water-based samples. The changes are to be evaluated via an analysis of the
principal components.
1. INTRODUCTION
The bacterial strains applied in the experiment were Lactobacillus acidophillus, Staphylococcus
epidermidis, Enterococcus durans, and Escherichia coli, all in the form of colonies cultivated on the
growth medium and in an isotonic diluent. We examined the effect of a magnetic field on these
bacterial strains. Growth suppression caused by the elimination of the bacteria via the magnetic
field [1] was anticipated. The experiment was carried out under stable ambient conditions to exclude
possible effects of the environment. We used standard image processing methods to observe the
structure and size of the bacterial colonies on Petri dishes. These variations will bring an effect on
the number and strength of the molecular bonds. An appropriate technique for the determination
of these changes appears to consist in the vibration spectrum; the observation of the variations is
performed by means of the NIR spectrum.
2. IMAGE PROCESSING TECHNIQUES
A special algorithm is required for the counting and measurement of the colony radius. The
described technique is based on standard image processing methods. The image of the Petri dish
was taken by an HP scanjet G3110 with 600DPI (bottom down). The process of evaluating the
size of the colony radius is shown in Figure 1.
The preprocessing procedure first requires us to decrease the image size to optimalize the computation speed. Then, the image is converted to grayscale. In some cases, converting the image by
means of classic algorithms [2] may be sufficient. However, if the color of the colonies is known,
the conversion based on HSL components [3] is desirable. This is the best method of highlighting
the colonies against the background. In the next step, it is necessary to binarize the image. The
conversion to a black-and-white (binary) image can be performed using thresholding algorithms.
For this case, balanced histogram tresholding [4] is used. This method works with the image
histogram and checks the balance of this histogram. To achieve the balanced state, the technique
removes components on both sides of the histogram. When this iterative process finishes, the result
threshold is known.
After that, image segmentation is needed. Firstly, blobs are isolated by method introduced
at [5]. We have the set of blobs with one or more colonies. To determine whether we are dealing
with a single colony, the circularity criterion is used. This criterion is based on computing with the
blob perimeter and area. Equation (1) expresses the relation between these variables (a — blob
area, p — perimeter). Lower values reflect bigger circularity.
µ
¶
4πa 2
fc = 1 − 2
(1)
p
Progress In Electromagnetics Research Symposium Proceedings, Stockholm, Sweden, Aug. 12-15, 2013 755
Figure 1: Evaluation of the size of the colony radius.
Figure 2: The formation and skeleton of a colony.
If the measured blob has a sufficient circularity, it is regarded as a colony, and its radius is measured.
Otherwise, the algorithm for connected colonies was used. This algorithm computes the skeleton of
the formation. At each point of skeleton, it creates a shape vector [6] and determines the probability
of circle appearance. This probability is defined by the occurrence of a number of the same value.
If the probability is high enough, the related part of the formation is declared as a colony with a
defined radius, and this part is then removed from the formation. This procedure is repeated until
all formations are removed.
3. DETERMINING THE CHEMICAL COMPOSITION OF BACTERIAL CELLS
The chemical variations are observed by means of the NIR spectrum. The magnetic field influencing
the metabolism of the bacteria changes their membrane structure and ratio of lipids, proteins and
polysaccharide. These variations will bring an effect on the number and strength of the molecular
bonds, which will affect the NIR spectrum. Near-infrared spectroscopy is a spectroscopic method
that uses a narrow region of the electromagnetic spectrum (900 nm to 2500 nm). The generated light
passes over the sample. According to the strength and type of the molecular bonds, the spectrum
will absorb some of the light wavelength. We used a transmission method to acquire the spectra; the
length of the measuring path was 2 mm. The maximum recovery diluent (water, peptic digest of an
animal tissue, sodium chloride) was used for the storage and measurement. All samples were stored
in an Eppendorf micro test tube 1 ml. The Varian Cary 5E spectrometer gained a spectrum with
3 nm spectral resolution and 0.1 s average reading time. The preprocessing of the acquired spectra
was performed using normalization and the smooth first derivate (Savitzky-Golay) [7]. In this
experiment, the standard normal variate (SNV) was used for the analysis. The result of the SNV
expressed zero mean data with a normalized variance. After that, orthogonal signal correction was
used to increase the prediction accuracy. This orthogonal signal correction is based on a simple
idea related to suppressing the part of input variables that is unrelated to the predicted value.
Within the process, a signal correction is performed that does not remove useful information from
the input data. For the analysis, the classic chemometric method of principal component analysis
(PCA) is used. All the above-mentioned algorithms were implemented in Mathworks MATLAB
7.9.0.
Figure 3 shows a slight shift of the peak near 1385 nm between the reference sample and the
sample affected by a magnetic field on the Enterococcus durans. A similar effect was observed
in all measured bacteria strains. Another singularity was observed at 1185nm; in all affected
PIERS Proceedings, Stockholm, Sweden, Aug. 12–15, 2013
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samples, we measured the absorption increase. These changes in the spectra may be caused by
changes in the chemical composition of a bacterial cell. A peak shift is observed at the edge of a
strong O-H first overtone peak of water; this anomaly can be caused by different concentrations
of organic compounds The peak around 1190 nm can be associated with the second overtone of
the C-H stretching molecule bond. The C-H bonds occurs in amino acids found in proteins. The
described situation can be a proof of a different biochemical process inside bacteria cells. For
meaningful results, more measurements would be needed with a focus on the different bacteria
concentrations and various temperatures. These changes were confirmed by the PCA analysis.
The PCA is a well-known technique applied to identify statistical trends in data. This method
provides a procedure for the reduction of a complex data set to a lower dimension to reveal the
sometimes hidden dependencies. The goal of the PCA is to reduce the number of variables to a
smaller set of components by analyzing the variance in the variables. The components are created
as linear combinations of the variables. The weight of these combinations is shown in Figure 4(a).
We can notice negative peaks at 1390 nm and 1180 nm, which confirms our previous observations.
The middle section of Figure 4 expresses the residual error of the constructed model (affected vs.
unaffected) according to number of components. With more components involved in the creation
of the model, the residual error decreases. By two components unaffected and affected bacterial
strains can be distinguished using computed weights at mentioned peaks (Figure 4(b)).
(a)
(b)
Figure 3: (a) The preprocessed difference spectrum, (b) detail of peak shift.
(a)
(b)
(c)
Figure 4: (a) The weight of component 1; (b) mean residual error of the model; (c) classification by the
PCA.
4. MODEL OF THE MAGNETIC FIELD
The characteristics of the applied magnetic fields were determined using the finite element method
in the ANSYS system. The representation of the resulting magnetic flux density vectors is shown
in Figure 5. In the experimental measurement of the magnetic flux density gradient, we assumed
that the highest value of the gradient will be achieved between the magnets [8].
5. CHARACTERIZATION OF THE EXPERIMENT
The experiment was conducted in steps as shown in Figure 6.
Progress In Electromagnetics Research Symposium Proceedings, Stockholm, Sweden, Aug. 12-15, 2013 757
Figure 5: The results of the ANSYS-based modeling [8].
Preparation and
cultivation of the
bacterial colonies
Acquire image of the
colonies. Acquire NIR
spectrum. Insert into
the magnetic field
Growth of the
colonies (inside/
outside) magnetic
field
Acquire image and
spectrum. Process
results
Figure 6: Description of the experiment.
Figure 7: Percentage growth of bacteria on the of experiment.
We used two types of stationary magnetic fields. The first type was created at the ferrite magnet
exhibiting the magnetic field gradient value of 5.5 and 5.68 T/m. The second type was created by
the neodynium magnets. Magnets exhibit the magnetic field gradient. Density values are from 10
to 11 T/m. The third group of Petri dishes were used as a reference. Experiment takes 7 days at
laboratory temperature (22◦ C).
6. RESULTS AND CONCLUSION
Four bacterial strains were stored in a maximum recovery diluent. The solution was used to
prepare the colonies and to facilitate the NIR measurement. After the formation of the colonies,
the first image of the reference and the exposed samples was acquired, and the NIR spectra were
measured. The experiment lasted for 7 days at a laboratory temperature (22◦ C). At the end of
the experiment, another image was acquired and the resultant spectrum obtained. As expected,
the magnetic field had suppressed the growth of the colonies. To highlight the differences, a longer
time or higher temperature would be necessary. The Enterococcus durans and the Staphylococcus
epidermidis increased their radii by about 3% in the reference sample and 1–2% in the exposed
sample. The other set strains exhibited similar results. Because of the inappropriately selected
conditions, the growth was negligible and difficult to measure. For a convincing proof of growth
suppression by the magnetic field, more measurements should be performed. The biological effect of
the electromagnetic field may be based on the interference among ionic channels in the membrane,
which affect the transportation of ions into the cells. Another explanation lies in the formation of
free radicals caused by the magnetic field [1].
More interesting results were obtained by examining the NIR spectra. Two significant changes
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PIERS Proceedings, Stockholm, Sweden, Aug. 12–15, 2013
were observed in the spectrum. First, there occurred changes in the compound concentration
proved by the motion of the absorption peak to 1385 nm. Second, the ratio of proteins changed, it
demonstrates decrease of peak at 1185 nm. This could constitute the proof of a different biochemical
process inside bacteria cells.
ACKNOWLEDGMENT
The research described in the paper was financially supported by project of the BUT Grant Agency,
No. FEKT-S-11-5/1012 and project from Education for Competitiveness Operative Programme
CZ.1.07.2.3.00.20.0175 (Electro-researcher).
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