RAPID SPECTROPHOTOMETRIC DETECTION
FOR ANALYSIS OF BACTERIAL
CONTAMINATION IN WATER
by
Sarah L. Spence
c Copyright by Sarah L. Spence, 2011
All Rights Reserved
A thesis submitted to the Faculty and the Board of Trustees of the Colorado
School of Mines in partial fulfillment of the requirements for the degree of Master of
Science (Applied Physics).
Golden, Colorado
Date
Signed:
Sarah L. Spence
Signed:
Lincoln D. Carr, PhD
Thesis Advisor
Signed:
Cynthia Norrgran, MD
Thesis Advisor
Golden, Colorado
Date
Signed:
Tom Furtak, PhD
Department Head
Department of Physics
ii
ABSTRACT
Bacterial contamination in water is a hazard worldwide, from wells in third world
countries to reclaimed water on the International Space Station.
While traditional
water testing techniques detect living bacteria in approximately 48 hours, we demonstrated that optical techniques can detect bacteria in as little as six hours.
The
Beer-Lambert Law, as applied to spectrophotometric turbidity studies, correlates the
concentration of organismal growth in a solution to the absorption of visible light.
By passing light through a sample of contaminated broth, we directly measure the
intensity of the resulting light.
We use this to calculate the transmittance and the
absorption of light that passes through the solution. However, it is not entirely necessary to transform transmittance into absorption. A plot of transmittance over time
tracks the inverse of the bacterial growth curve. Escherichia coli (E. coli ) was used
as the contamination organism for this project.
A sharp drop in transmittance is
seen during the exponential growth phase of the bacteria being tested. We observed
this change within six to twelve hours following the inoculation of the Escherichia coli
into samples, using both a standard monochrometer and a device engineered specifically for this study. We employed cell counting algorithms to prove the consistency of
the optical techniques with the confirmed presence of bacterial growth. The software
algorithm utilizes a threshold to form a binary rendition of a full-color version of a
sample slide. It then uses this binary data to calculate both an area fraction and the
number of spherical or elliptical shapes present.
This cell counting process indeed
confirmed the growth of our intended contaminant.
Our cultures were grown and
maintained in a bioreactor for several months, in a full nutrient broth with glucose
used as its limiting nutrient. The bioreactors serve to control the growth phase of the
bacteria exhibited within the culture itself. By starving the bacteria, we were able to
set them into a stationary growth phase, while supplying an abundant amount of glu-
iii
cose forced the population into an exponential growth phase. The individual growth
patterns of each of these special cases were observed. We hypothesize that the length
of the lag phase in the transplanted bacteria is affected by the difference in nutrients
between the culture and the sample media.
We anticipated seeing extremely short
lag times in wild E. coli transplanted from water systems to a full nutrient broth.
The Optical Bacteria Detector (OBD) was designed to be an effective and inexpensive
device, with a limited use of consumables and minimum waste generation.
It is a
mobile, battery-operated field device that is in step with the spectrophotometer used
in the laboratory.
The OBD uses a phototransistor as a sensor and an LED with
wavelength of approximately 500 nm. Data from the monochrometer shows that the
sudden decrease in transmittance is most pronounced at this wavelength. The OBD
can be tuned to test for other bacteria, such as Salmonella sps. and Vibrio fisheri, by
changing the wavelength of the LED light source. Further work is being conducted
on a second model of the OBD that will have the capability of testing the sample for
scattering properties as well as absorption.
The effects of Tyndall scattering will
give the device the capability to suggest the size of the contaminant, which could be
a life-saving determination of whether an infection is bacterial or parasitic. It is our
hope that this research will be continued to detect the presence of many other lifethreatening pathogens, and continue to bridge the gap between the fields of biology
and physics.
iv
TABLE OF CONTENTS
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
LIST OF SYMBOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
LIST OF ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii
CHAPTER 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1
Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2
Selection of the Bacterial Contamination . . . . . . . . . . . . . . . . . . 2
1.3
Escherichia coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1
Anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.2
Motility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3
Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.4
Nutrient Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3.5
Growth Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.6
Physical Modeling of Biological Systems . . . . . . . . . . . . . 14
1.4
Previous Work
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.5
Biological Laboratory Techniques . . . . . . . . . . . . . . . . . . . . . 18
1.5.1
Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.5.2
Inoculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
v
1.6
1.5.3
Gram Staining . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.5.4
Identification of Bacteria . . . . . . . . . . . . . . . . . . . . . . 23
Experiment Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.6.1
Lab-grade Spectrophotometer . . . . . . . . . . . . . . . . . . . 26
1.6.2
Optical Bacteria Detector I . . . . . . . . . . . . . . . . . . . . 26
1.6.3
Optical Bacteria Detector II . . . . . . . . . . . . . . . . . . . . 26
CHAPTER 2 TURBIDITY STUDIES WITH SPECTROPHOTOMETRY . . . 27
2.1
Genesys 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2
Establishing Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . 30
2.3
Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.4
Testing Protocol
2.5
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.6
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6.1
Error Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.6.2
Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.6.3
Cell Counting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
CHAPTER 3 THE OPTICAL BACTERIA DETECTOR I: ABSORPTION
ONLY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.1
Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2
Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3
Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.1
Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.2
Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
vi
3.4
Testing Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.4.1
Lab Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4.2
Field Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5
Difficulties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.6
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.7
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.8
3.7.1
Error Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.7.2
Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 63
Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
CHAPTER 4 THE OPTICAL BACTERIA DETECTOR II: ABSORPTION
AND SCATTERING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.1
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.2
Engineering Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.3
Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.4
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.5
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.6
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
CHAPTER 5 CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
REFERENCES CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
APPENDIX A - VISUAL BASIC CODE FOR IMPORTING DATA . . . . . . . 84
APPENDIX B - GENESYS 6 AUTOMATION MACRO . . . . . . . . . . . Pocket
APPENDIX C - VISUAL BASIC CODE FOR IMPORTING DATA . . . . . Pocket
vii
LIST OF FIGURES
Figure 1.1 A representation of a bacterium with a single flagellum. The flagellum is powered by a proton turbine, and is produced by the bacterium after it has undergone approximately three divisions. Rotation of the flagellum in one direction will cause the bacterium to
move forward, while rotation in the other direction will cause the
bacterium to tumble and change directions. This results in random
walk behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Figure 1.2 The beginning of conjugation. One bacterium contains a plasmid
with the gene encoding for a pilus while the other does not. . . . . . 10
Figure 1.3 A pilus projects from the bacterium containing the plasmid. This
pilus is a temporary cytoplasmic connection between the two cells. . 10
Figure 1.4 Genetic material replicates and crosses into the adjacent cell via
the pilus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Figure 1.5 The pilus withdraws, resulting in two cells containing the same
plasmid. The affected cell is now able to perform conjugation on
other cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Figure 1.6 Schematic representation of the four phases of bacterial growth: lag
phase, exponential phase, stationary phase and death phase. The
lag phase occurs immediately after the bacteria is transplanted into
a new environment, and no significant population growth occurs.
The exponential phase is the result of each cell duplicating every
20-30 minutes. The stationary phase occurs when the population
has reached its carrying capacity, and the death phase occurs when
there is no longer enough nutrients to support the population. . . . 13
Figure 1.7 Our experimental representation of bacterial growth, created from
data seen in Chapter 2. The lag, exponential and stationary phases
are all clearly visible in this representation. . . . . . . . . . . . . . . 14
Figure 1.8 A stained sample slide photographed through a microscope camera.
These images are used to automate the cell counting process. . . . . 16
Figure 1.9 A binary rendition of the slide above. The color image was first
converted into a greyscale image, whereupon a threshold was used
to convert it into a binary representation for processing. . . . . . . . 17
viii
Figure 1.10 An inoculating loop used for the culturing of the bacteria. Can be
used for both broth and agar media. . . . . . . . . . . . . . . . . . . 20
Figure 1.11 A positive gram-stain. Gram-positive bacteria retain the purple
dye by way of a thick layer of peptidoglycan in their cell wall. . . . 22
Figure 1.12 Negative gram-stain. Gram-negative bacteria do not retain the
purple dye of the gram-stain. . . . . . . . . . . . . . . . . . . . . . . 22
Figure 1.13 Spherical bacteria, referred to as cocci. . . . . . . . . . . . . . . . . 23
Figure 1.14 Rod-shaped bacteria, referred to as bacilli. . . . . . . . . . . . . . . 24
Figure 1.15 Spiral bacteria, referred to as spirilla.
. . . . . . . . . . . . . . . . 24
Figure 1.16 General experimental setup. A light source is incident on a sample
of bacteria. The cells suspended in this sample absorb fractions
of the incident light. The transmitted light is then measured by a
detector on the other side. . . . . . . . . . . . . . . . . . . . . . . . 25
Figure 2.1 Genesys 6 setup. The Genesys 6 uses tungsten halogen and deuterium bulbs as a light source and a photodiode as a sensor. It
outputs data in percent transmittance. . . . . . . . . . . . . . . . . 27
Figure 2.2 Genesys 6 UV/Vis-Spectrophotometer. The Genesys 6 tests six
cuvettes at once, and tests over a wide range of wavelengths. . . . . 29
Figure 2.3 A basic bioreactor setup. The bioreactor maintains a constant environment for the bacterial culture, by controlling temperature, pH
and the amount of nutrient available. . . . . . . . . . . . . . . . . . 31
Figure 2.4 The bioreactor used in this experiment. A variable peristaltic pump
was used to control the rate of glucose addition in order to manipulate initial growth phase of the culture. . . . . . . . . . . . . . . . 32
Figure 2.5 A labeled photograph of the setup. A second pump is used to collect
culture solution from within the bioreactor. . . . . . . . . . . . . . . 35
Figure 2.6 Data taken by the Genesys 6 prior to implementation of new protocol. There is a wide spread in lag time for this data, however it
was collected using several distinct batches of bacteria, without a
stabilized protocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
ix
Figure 2.7 Data collected following the protocol revision. The spread of data
is significantly narrower and follows two groupings corresponding to
the two cultures used to collect the data itself. The transmittance
values follow the inverse of an expected bacterial growth curve. . . . 37
Figure 2.8 Cuvettes tested with differing levels of initial bacterial concentration. The yellow lines, which display a significantly smaller lag
phase, represent samples that were inoculated with five times as
much bacteria as the other samples. . . . . . . . . . . . . . . . . . . 37
Figure 2.9 A comparison plot of bacteria prepared in stationary and exponential growth phases. The bacteria intially prepared in the stationary
phase has a slightly shorter lag period than bacteria prepared in
the exponential growth phase. . . . . . . . . . . . . . . . . . . . . . 38
Figure 2.10 Analysis in discrepancy between average values for bacteria prepared initially in the stationary phase and the exponential phase.
The standardized difference between stationary and exponential
phase increases at the end of the lag phase for the bacteria prepared in stationary phase. . . . . . . . . . . . . . . . . . . . . . . . 40
Figure 2.11 Curve fitted to the average values of the Genesys 6 data. Error-bars
represent the variance in the Genesys 6 measurements over time for
a sample of constant transmittance. . . . . . . . . . . . . . . . . . . 45
Figure 2.12 Cell counting in terms of colony forming units. This method counts
individual cells, but does not account for cell clumping. . . . . . . . 46
Figure 2.13 Cell counting in terms of area fraction of cells. This is the preferred
method, as it accounts more closely for the clumping of cells. . . . . 47
Figure 2.14 A mathematical fit of the cell count data shown above, in colony
forming units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Figure 2.15 A mathematical fit of the area fraction data shown above.
. . . . . 49
Figure 2.16 A normalized absorption curve as obtained from the Genesys 6
compared to corresponding area fraction data. This plot shows the
correlation between actual cell growth and change in transmittance. 50
Figure 3.1 Basic setup for the OBD I. The OBD I uses a 560 nm LED as a
light source, and a PNZ150 phototransistor as a detector. . . . . . . 51
x
Figure 3.2 Circuit schematic for the OBD I. The output from the phototransistor is fed into an op-amp to amplify its signal, before being delivered
to a voltmeter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Figure 3.3 Top view of the housing for the OBD I. Features include an on/off
switch, a cap for the sample container, and an LCD display. . . . . 54
Figure 3.4 Internal view of the housing for the OBD I. The circuit boards are
mounted together, and the sample container is completely enclosed
to prevent interference from ambient light. . . . . . . . . . . . . . . 55
Figure 3.5 Photograph of the OBD I. . . . . . . . . . . . . . . . . . . . . . . . 56
Figure 3.6 Complete data set taken by the OBD I. This data was taken over
two separate runs using the same batch of bacteria. Though transmittance only falls to 0.60, these tests were performed using nutrient broth instead of LB. . . . . . . . . . . . . . . . . . . . . . . . . 59
Figure 3.7 Same data as above, a closer view, beginning at 12 hours. . . . . . . 60
Figure 3.8 Statistical analysis of the curve fitted to OBD data. Error-bars
are calculated from the variation in measurements observed by the
OBD I when testing a control sample. . . . . . . . . . . . . . . . . . 64
Figure 3.9 Experimental setup to test the linearity of the response of the OBD
I. A variable lab power supply was used to supply voltage to the
LED independent of the rest of the circuit. . . . . . . . . . . . . . . 66
Figure 3.10 Linearity analysis of the OBD I’s response. The initial jump at 2.0
V is the forward voltage drop necessary for the LED itself to turn
on. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Figure 4.1 Schematic for the scattering experiments with the OBD II. The
light source in this case is a laser of wavelength 523 nm, and the
detectors are provided by the same circuit as seen in Chapter 3. Detectors at 180◦ and 90◦ allow for us to calculate a ratio of scattered
to transmitted light. . . . . . . . . . . . . . . . . . . . . . . . . . . 70
xi
LIST OF TABLES
Table 2.1
Error contributions for Genesys 6 testing . . . . . . . . . . . . . . . 42
Table 2.2
Parameter Values for Genesys Data . . . . . . . . . . . . . . . . . . 44
Table 2.3
Parameter Values for Cell Population . . . . . . . . . . . . . . . . . 48
Table 2.4
Parameter Values for Area Fraction . . . . . . . . . . . . . . . . . . 48
Table 3.1
Error contributions for OBD I testing
Table 3.2
Parameter Values for OBD I
. . . . . . . . . . . . . . . . 63
. . . . . . . . . . . . . . . . . . . . . 64
xii
LIST OF SYMBOLS
Transmittance
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T
Absorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A
Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . c
Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I
Pathlength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . `
Molar absorptivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ε
Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . t
Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P
Diffusion Flux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J
Diffusion Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D
Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Frictional coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ξ
Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ρ
Potential
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . U
Reynolds Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Re
Dynamic viscosity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . η
Kinematic viscosity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ν
Efficiency factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qext
Extinction coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . γ
Particle Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N
Particle radius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a
xiii
Turbidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . τ
xiv
LIST OF ABBREVIATIONS
Optical Bacteria Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . OBD
Optical Bacteria Detector I . . . . . . . . . . . . . . . . . . . . . . . . . . . OBD I
Optical Bacteria Detector II . . . . . . . . . . . . . . . . . . . . . . . . . . OBD II
Deoxyribonucleic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA
Lysogeny Broth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LB
Colony forming unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFU
National Institutes of Health
. . . . . . . . . . . . . . . . . . . . . . . . . . . NIH
Light-emitting diode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LED
Center for Disease Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CDC
Lipopolysaccharides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LPS
Ribonucleic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RNA
Messenger Ribonucleic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . mRNA
Transfer Ribonucleic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . tRNA
Graphical User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GUI
Nephelometric Turbidity Units . . . . . . . . . . . . . . . . . . . . . . . . .
xv
NTU
ACKNOWLEDGEMENTS
This project has become such a large part of my life, that my life has become
a large part of this project itself. My friends, colleagues and family have in many
ways contributed to the success of the experiment and my perseverence to see it to
completion. I must first thank my advisor and mentor, Dr. Cynthia Norrgran, not
only for bringing me to this project, but for the many learning experiences since then.
She has been there for every stumbling block and victory along the way, and I will
never be able to fully express my gratitude for all she has done. I thank my other
advisor, Dr. Lincoln Carr, for teaching me that being a biologist and a physicist need
never be mutually exclusive, and Dr. Jeff Squier for his continuous input and support
throughout the project.
This project would have never advanced this far if not for the work of the students
who came before me. Alyssa Cobb, who showed this experiment was possible at all,
and Nick Hunter, who, in automating data collection, has saved me many long nights
alone in the lab, have my special thanks. I thank Sean Bingel for the many hours
spent staining and photographing slides. I must acknowledge Randy, Orlen, Mike and
Justin, without whom the OBD would still be a poorly soldered circuit living inside a
cookie tin. Erich Hoover has been an incredible resource for me as well; from circuit
design to thesis writing and even telling me that perhaps it is time for a break, he
has been both a friend and instructor throughout the course of this journey.
My family and friends have perhaps the most of my gratitude. I thank my mother
for her never-ending faith in me, as well as keeping me fed and caffeinated through
the many long nights of work. I thank my father for both his technical advice and
his moral support. I would never have gotten this far were it not for my parents’
love and encouragement. I am grateful for my friends, Ginger and Kim, who have
somehow endured the constant ramblings of a project they know little about, and of
xvi
course Christine, who did not even hesitate to edit this thesis. I thank Michal for his
ever-willingness to come to the rescue when I needed an extra set of hands, or just
someone to talk to.
Most importantly, I have to thank Shaun for never giving up on me, for keeping
me awake at all hours of the night to record data, for not talking to me until I finished
that last chapter, for spending Valentine’s Day weekend helping me write this thesis.
He has been a bastion of support for me. No matter where the Air Force has sent
him, he has always found a way to show his faith in me, and share in both victories
and defeats. I will never be able to thank him enough for how much he has helped
me this past year.
xvii
One of the advantages of being disorderly is that one is constantly making exciting
discoveries.
A. A. Milne
xviii
CHAPTER 1
INTRODUCTION
Water contamination is a serious health concern for human populations around
the world. In the developed world, bacterial contamination is detected through traditional laboratory techniques. The standard laboratory test for water contamination
involves filtering the water and plating a sample of it in agar. The specimen is incubated in a heat-controlled environment for 24 to 48 hours, and examined for the
formation of colonies. These processes are time consuming and therefore costly. [1]
Developing countries often do not have access to laboratory facilities capable of performing these tests and they are too expensive for routine use. For these reasons,
contaminated water sources are left unidentified and are still used for drinking water
by the inhabitants. A simple, inexpensive method for detection of bacterial contamination is needed to protect these people from contaminated water sources. [2]
The primary objective of this project is to develop a more effective means of detecting bacterial contamination in water. An ideal solution is a portable device that
is small enough to fit in the glovebox of a car. The device must be powered by a
replaceable, transportable power source (such as batteries), and be relatively inexpensive to produce (ideally less than $15). The device would be used in field conditions
where testing in a laboratory would be unrealistic to impossible, and produce results
in less than 24 hours.
1.1
Motivation
To develop physical models of bacterial behavior, we must find a physical observ-
able. This experiment uses the measurement of light intensity as a basis for modeling
bacterial growth. In order to use this information, we employ a wide use of physical
techniques. Automated data collection is required to record information at specific
1
intervals over a 24-hour period. A macro script was an efficient means to accomplish
this. Additionally, new ways of collecting this information, such as portable field
devices, require significant amounts of technical design. These analytical approaches
supply a large amount of numeric data that is not characteristic of biological studies. As such, physics-based error analysis and plot-fitting are used to quantify and
understand biological behavior.
In addition to these physical techniques, biological methods must be used to conduct a clean experiment. This contribution from biology is limited to the basic bacteriology and laboratory techniques, including bacterial properties affecting population
growth, cultures, inoculation, media use, staining and microscopy.
1.2
Selection of the Bacterial Contamination
The contamination of water by human waste is one of the most lasting water
purity problems in the world. Sites of natural disasters, such as the Haiti earthquake
of 2010, are particularly vulnerable. [3] Human waste can contain enteric bacterial
pathogens such as Salmonella sps., which causes widespread outbreaks, despite small
concentrations of the contaminant in the water. Escherichia coli (E. coli ) acts as
an indicator bacterium for enterobacteria species, because it can be detected more
easily than other enteric bacteria. [4] It has been shown that E. coli is present in the
gastrointestinal track of all humans in as little as 40 hours following birth. E. coli has
been found on every continent and is hard to eradicate. [5] During the Vietnam War,
it was found that 20% of wounds incurred during the war had been infected with E.
coli due to the poor water quality. [6] This enforces the idea that water quality is not
applicable only to drinking water, but water used for other purposes as well.
Although E. coli is indigenous to the human gut, it can be a dangerous pathogen.
The human immune system is accustomed to its own strain of E. coli present in a
specific distribution. When this balance is disturbed, an immune response results.
2
The most common disease caused by E. coli is gastroenteritis, which can range in
severity from mild to life-threatening. [5] The immediate reaction of the gastrointestinal system to E. coli, vomiting and diarrhea, is not itself particularly dangerous. The
fluid loss caused by these symptoms, however, can result in severe dehydration. In the
developed world, physicians are easily able to treat the dehydration with intravenous
(IV) fluids, but in the regions of the world suffering most from these infections do
not have the same capabilities. Dehydration is most dangerous to patients suffering
from unrelated immunodeficiencies, such as geriatric and pediatric patients, as well
as those subject to Human Immunodeficiency Virus (HIV) and Acquired Immune
Deficiency Syndrome (AIDS). These latter groups are particularly prevalent in the
developing world. Other common infections associated with E. coli include urinary
tract infections (UTIs) and neonatal meningitis. [7]
Rarely, E. coli infections can lead to more involved ailments. Hemolytic-uremic
syndrome is caused by E. coli O157:H7, and presents with hemolytic anemia, thrombocytopenia and acute renal failure. This condition primarily affects children, and
approximately one third of patients suffer permanent renal damage. [8] Peritonitis
and mastitis are characterized by the inflammation of the peritoneum, the membrane
encasing the abdominal cavity, and breast tissue respectively. They often must be
treated with surgery. [9] [10] Septicemia, also called sepsis or “blood poisoning”, is
a rare but serious complication of E. coli infection. Sepsis is an infection within the
blood, which is often fatal (up to 60% of patients within 30 days). E. coli can also
result in severe respiratory infections like Gram-negative pneumonia. [11]
E. coli is also the standard bacteria for research purposes. More is known about
E. coli than any other laboratory-grade bacterium. Because work with E. coli is
standardized, research can continue without need for extraneous experiments to determine its basic physiology. This project is focused on the study of E. coli K12,
because it is a safe, standardized lab strain. [12]
3
1.3
Escherichia coli
In order to model the behavior of E. coli, we must first understand their structure
and mechanisms. Bacterial structure, motility and metabolism shape the growth
curve.
1.3.1
Anatomy
By knowing the chemical composition of E. coli, we can make calculations regarding synthesis for reproduction and other intercellular pathways that affect their
behavior. It is no surprise to find that 55.0% of the dry weight of a single cell of E.
coli B/r is due to proteins, numbering approximately 2,350,000. It follows that for
a cell to divide into two complete daughter cells, an additional two million proteins
must be synthesized. The bacterium’s capacity to create these proteins acts as a
determining factor in the reproduction interval of the culture. The nucleic material
of the cell, RNA and DNA, follow at 20.5% and 3.1% total dry weight respectively.
Lipids, small hydrophobic molecules important in energy storage and cell membrane
structure, contribute 9.1% of the dry weight of the cell. Lipopolysaccharides (LPS),
larger molecules composed of a polysaccharide bonded covalently to a lipid, are also
key components of the cell wall and contribute an additional 3.4% of the dry weight.
Glycogen and peptidoglycan each contribute 2.5% to the dry weight. Glycogen is
an important molecule in energy storage for many organisms, while peptidoglycan
is a molecule that forms a meshwork in the cell walls of all bacteria (and whose absence is a defining characteristic of similar lifeforms known as archaea). Polyamines,
metabolites, cofactors and ions account for the remaining 3.9% [13]
A bacterial cell wall consists of three separate membranes. The outermost membrane is a lipid bilayer. Lipid bilayers conforming to the fluid mosaic model are the
basis of most membranes in biology. LPS molecules (phospholipids specifically) are
composed of hydrophobic lipid tails, and a hydrophilic head. The bilayer is formed
4
when the lipid tails are drawn to one another, and the heads are pointed outward away
from one another. Because the layers are not attached by any bonds, the molecules
are free to shift with respect to one another. The layers are asymmetric, containing
non-overlapping gaps filled with proteins. Lipid bilayers are also highly permeable to
hydrophobic molecules. However, the outer membrane of E. coli is not particularly
permeable to hydrophobic molecules, evidenced by the ineffectiveness of hydrophobic
antibiotics. A second layer, the murein sacculus, lies beneath the lipid bilayer in the
periplasm. This layer is composed of murein, also known as peptidoglycan. [14] This
thin layer of meshwork acts as a filter between the cell and its environment, and is a
crucial component of binary fission. Below the murein sacculus sits the cytoplasmic
membrane. The cytoplasmic membrane is a second lipid bilayer; however, it is richer
in transmembrane and other proteins. [15] [16] [17] [18] [19]
The cell wall provides many functions for bacteria; among these are protection
from the environment and, more importantly, protection against internal turgor pressure. [20] A single bacterium contains a high concentration of proteins and other
materials, causing a large osmotic pressure to pull water from the environment into
the cell. Without the structural integrity afforded the bacterium by its cell wall,
water would rush pass the cell membrane, until the cell becomes so engorged with
water it lyses. The rigidity of the cell wall prevents this occurrence, and operates as
a physical barrier between the cell and its environment. [21] [22] [23] [24] [25]
As prokaryotes, E. coli cells contain no membrane-bound organelles. They contain
a single, circular strand of DNA, and the means by which to transcribe this DNA
into RNA and translate that RNA into gene-expressing proteins. Ribosomes are the
particles responsible for protein production. The two subunits of a ribosome encode
proteins as prescribed by messenger RNA (mRNA) from amino acids delivered by
transfer RNA (tRNA). E. coli cells also have external structures for motility called
flagella, which will be discussed below. [26]
5
E. coli obtains nutrients from its environment through the process of diffusion
(which will be discussed in greater depth in Section 1.3.2). This is the simplest and
most efficient form of consumption in the biological world, but it comes at a cost.
Diffusion is only a realistic means of consumption when the surface area to volume
ratio is very high, which occurs at small radii. As such, the physical form of an E.
coli bacterium is a rod of length 2 µm and diameter 1 µm. [27] [28] [29] [30] [31] [32]
[33]
1.3.2
Motility
Bacteria such as E. coli can grow specialized structures called flagella (see Figure 1.1). Flagella are comprised of an indeterminate number of fibers composed of a
single protein, flagellin. These fibers are arranged in a helical pattern extending from
the body of the bacterium to a length of 5-10 µm. An E. coli cell can contain any
number of flagella, though the presence of flagella is most prevalent in wild strains.
Flagella are powered by a proton turbine to propel the bacterium forward. The rotational element of the flagella is key to its motility, because reciprocal motion cannot
move an object forward. An E. coli bacterium can use its flagella to swim at up to
70 cell lengths per second. However, flagella are costly to make, and a bacterium
without any need to swim (such as one in a nutrient-rich environment) will stop the
production of them. Bacteria without any means to swim are much less likely to
survive harsh environments than those with flagella. [34] [23] [35] [36] [37]
The path of a bacterium can be approximated using a random walk model. The
flagellum only has the capability to propel the cell forwards. Reversing the direction
of rotation of the flagellum does not put the bacterium “in reverse,” but rather causes
it to tumble, turning it to an arbitrary orientation. [38] [39] Its flagellum can then
be used to propel the cell forward in this secondary direction. [40] [41] [42] By using
this random walk mechanism, a cell is able to significantly change its location to one
6
that is in a more nutrient-rich environment. This process, however, is completely
random. A bacterium does not have the processing power to direct its flagellum up a
concentration gradient. Random walks in three dimensions are complex fractals that
lie out of the scope of this project; however, it is important to understand the driving
force behind this form of bacterial motility. [43] [44] [45] [46]
Fick’s Law (1.1) describes mass transfer that is dependent on a concentration
gradient. [47] In this equation, J represents diffusion flux, D the diffusion coefficient,
φ concentration, and x position. Diffusion is how E. coli obtains the nutrients needed
to reproduce and thrive. [48] [49] [50] [51]
J = −D
∂φ
∂x
(1.1)
Unfortunately, Fick’s Law only describes the simple diffusion of nutrients down
a concentration gradient across the cell wall. Both Smoluchowski and Einstein formulated a diffusion model that accounts for the drift of particles. The Smoluchowski
Equation (1.2) is a realistic insight into the mechanisms and speeds at which E. coli
obtains its nutrition sources. It depends upon a density (ρ), time (t), spatial coordinates (r), a frictional coefficient (ξ), and a potential (U ). By considering the
contribution from friction, this equation acts as a superior model for real world systems. [43] [52]
∂ρ(r, t)
= D∇2 ρ(r, t) + ξ −1 ∇(ρ(r, t)∇U )
∂t
(1.2)
An important physical quantity to consider when analyzing bacterial motility is
the Reynolds number. The Reynolds number (Re) is a unitless comparison of the
inertial forces and the viscous forces acting on a given object in a given medium, as
is defined in (1.3), where U is the mean velocity of the object relative to the medium,
L is a characteristic linear value, ρ is the density of the medium, η is the dynamic
viscosity of the medium, and ν is the kinematic viscosity. [43]
ρLU
LU
Re =
=
η
ν
7
(1.3)
This quantity allows us to make various approximations, given that the Reynolds
number is either very large or very small. The Reynolds number for a single E. coli
swimming at 10 µ m/s through water is about 10−5 , which lies within the realm of
small Reynolds numbers. This is comparable to a human swimming through tar.
Most significantly, the Navier-Stokes equation (1.4), which models the turbulent flow
of Newtonian liquids, can be simplified and rewritten as the simpler Stokes equation
(1.5).[43]
1
∂v
+ (v · ∇)v = − ∇p + ν∇2 v
∂t
ρ
∇p = η∇2 v
(1.4)
(1.5)
This is an expression of laminar flow alone, where viscous forces outweigh inertial
forces, which is suitable for systems in which the Reynolds number is much less than
one.
Flagellum
Proton Turbine
Figure 1.1: A representation of a bacterium with a single flagellum. The flagellum is
powered by a proton turbine, and is produced by the bacterium after it has undergone
approximately three divisions. Rotation of the flagellum in one direction will cause
the bacterium to move forward, while rotation in the other direction will cause the
bacterium to tumble and change directions. This results in random walk behavior.
All considered, we can conclude that a bacterium must swim in order to acquire the
correct amount of nutrients, unless the environment is nutrient rich. As the population
grows, the relative amount of nutrients decreases. Non-flagellar bacteria are unable to
survive, and only flagellar bacteria will continue to contribute to population growth.
[43]
8
1.3.3
Reproduction
The mechanism by which bacteria reproduce is vital to understanding its growth
patterns. This section gives a brief overview of binary fission and the ways in which
bacteria achieve variation in their genetic sequences.
Bacteria, as prokaryotic cells, divide by way of binary fission. Binary fission is
a reproduction process that results in two identical copies (or daughter cells) of the
original mother cell. A bacterium contains a single, circular, double-stranded DNA
molecule. Cell division begins with the replication of this DNA molecule, starting at
a single point called the origin of replication, and moving both directions around the
chromosome until they meet again at the terminus of replication, creating two separate
molecules of DNA. During this time, the cell elongates, and begins to separate once
the DNA replication is completed. Septation is the process by which the cells “pinch
off;” a septum is formed by producing a new cell membrane and cell wall from the
center of the elongated cell. Once the septum is complete, the reproduction cycle has
come to an end, creating two identical cells. The amount of time between divisions
of E. coli is between 20 and 30 minutes depending on the specific strain and the
environment. [26] [53] [43]
Though binary fission produces identical replicas of the original cell, bacteria can
still exchange genetic information. This exchange of genetic information can occur
through conjugation, transduction, and transformation. Conjugation involves the
physical connection of cells by way of a pilus. A conjugative plasmid is necessary
to create the pilus. A pilus is a small outgrowth of cytoplasm that reaches out and
connects to another cell. This tubular pathway between cells allows segments of
DNA to be exchanged with the gene that codes for the pilus. Many traits can be
passed between cells in this manner, including antibiotic resistance. The process of
conjugation is illustrated in Figure 1.2, Figure 1.3, Figure 1.4, and Figure 1.5. [26]
Transduction transfers genetic information by way of a virus (also known as a
9
DNA
Plasmid
Figure 1.2: The beginning of conjugation. One bacterium contains a plasmid with
the gene encoding for a pilus while the other does not.
Pilus
Figure 1.3: A pilus projects from the bacterium containing the plasmid. This pilus
is a temporary cytoplasmic connection between the two cells.
Figure 1.4: Genetic material replicates and crosses into the adjacent cell via the pilus.
10
Figure 1.5: The pilus withdraws, resulting in two cells containing the same plasmid.
The affected cell is now able to perform conjugation on other cells.
phage). Normally, when a phage attacks a cell, it injects its genetic information,
which overtakes the cell machinery. The cell then produces copies of the phage, until
the cell bursts, releasing new phages. [26]
In transduction, the genetic information injected into the cell by the phage is
incorporated into the existing genetic information of the cell. The cell then propagates
this new DNA through either conjugation or binary fission. Transformation is the
uptake by the cell of genetic information from its environment. The new genetic
information is then incorporated in the same way as in transduction. Of course,
genetic variation can also come about as a result of mutation. [26]
1.3.4
Nutrient Usage
In order to survive, bacteria have evolved to have the ability to consume more than
one source of energy. E. coli prefers to consume glucose over any other form of sugar.
However, in environments that do not contain glucose, a bacterium can activate a
gene that allows it to consume lactose instead. The lac operon is not induced in an
environment that contains glucose. When lactose is present instead of glucose, the lac
operon is turned on. The lac operon controls the genes that encode for the enzymes
(β-glactosidase, lactose permease and lactose transacetylase) to metabolize lactose.
11
If the bacterium is introduced to an environment containing glucose, the lac operon
is repressed. [54] [26]
When the environment lacks the concentration of nutrients necessary to sustain
the bacterial population, E. coli, like many species of bacteria, is able to enter a
dormant state, where its metabolic needs are minimized, and population growth is
stopped. This dormancy can be initiated by both low nutrient concentration in the
environment and other factors affecting metabolism, such as temperature. Bacterial
cultures in laboratories are kept at low temperatures in a refrigerator to induce dormancy. Different strains can survive different lengths of dormancy, though lab strains
(such as E. coli K-12) must still be recultured regularly. [39]
1.3.5
Growth Curve
Bacteria has a specific, sigmoidal curve that defines its growth, divided into four
subsections: lag phase; exponential growth phase; stationary phase; death phase. The
growth curve is charted as number of viable bacteria vs. time, where viable bacteria
is defined to be bacteria that is living and capable of reproduction. The lag phase occurs immediately after the bacteria have been transplanted into a new location. The
number of viable bacteria does not significantly increase during this time, because
the bacteria are occupied healing any damage accumulated during transportation, as
well as coming to terms with their new environment. The new environment may contain a different composition of nutrients, and the bacteria will change their metabolic
processes to best make use of the nutrients now available to them. The exponential
growth phase is then marked by a sudden increase in the number of viable bacteria. Because bacterial cell division involves essentially doubling the number of viable
bacteria during each cell cycle, an exponential curve aptly represents rapid growth.
However, this growth eventually evens out into a stationary phase, where the limited
supply of food and the buildup of metabolic wastes make the environment unable to
12
support a larger colony of bacteria. In most population models, this is referred to as
the carrying capacity. An expression describing carrying capacity (1.6), relates the
population size (P ), the rate of population growth (r) and the carrying capacity itself
(K). [26]
P
dP
= rP (1 − )
dt
K
(1.6)
The solution to this differential equation is then
KP0 ert
P (t) =
.
K + P0 (ert − 1)
(1.7)
When this buildup and lack of food become too great, the bacteria enter the death
phase and gradually die off. Figure 1.6 and Figure 1.7 illustrate this growth trend.
[26]
Stationary
Phase
Exponential
Phase
Death
Phase
Bacterial Population
Lag
Phase
Time
Figure 1.6: Schematic representation of the four phases of bacterial growth: lag phase,
exponential phase, stationary phase and death phase. The lag phase occurs immediately after the bacteria is transplanted into a new environment, and no significant
population growth occurs. The exponential phase is the result of each cell duplicating
every 20-30 minutes. The stationary phase occurs when the population has reached
its carrying capacity, and the death phase occurs when there is no longer enough
nutrients to support the population.
13
Figure 1.7: Our experimental representation of bacterial growth, created from data
seen in Chapter 2. The lag, exponential and stationary phases are all clearly visible
in this representation.
1.3.6
Physical Modeling of Biological Systems
Physical models must be applied in order to fully understand bacterial behavior.
The Beer-Lambert Law relates the concentration of a solute to the absorption
of light by the solution, as shown by (1.8), where A is absorption, ε is the molar
absorptivity, ` is the path-length of the light, and c is the concentration of the solute.
[55]
A = ε`c
(1.8)
This states that absorption is related to the changing concentration of the bacteria. However, this form is not very useful, as absorption is not a quantity that
can be measured directly. Instead, transmittance can be measured and converted to
absorption using (1.9). [55]
A = − log10 (T )
(1.9)
Transmittance, in turn, is defined by (1.10) in terms of the measured intensity, I,
and the initial intensity, I0 .
T =
I
I0
(1.10)
Combining (1.9) and (1.10) yields (1.11). This is a simple, realistic way in which
to indirectly measure absorption.
14
A = − log10
I
I0
(1.11)
(1.11) can then be combined with (1.8) to yield (1.12), which gives the concentration of the solute in terms of measurable quantities.
− log10 (I/I0 )
c=
ε`
(1.12)
The molar absorptivity ε is a constant that is intrinsic to the solute itself and is a
measure of the degree to which a single particle of solute absorbs light at a particular
wavelength. Because this property varies with the wavelength of light in question, it is
most easily observed and calculated experimentally for the specifications in question.
By way of the Beer-Lambert Law, one can determine that a change in the transmittance of light through a medium is consistent with the growth of some microbe in
that medium. Whether or not the most prevalent microbe growing in the solution is
the microbe in question must be determined by other means, such as the wavelength
of light used and the medium itself.
One of the major assumptions made in this project is that the change in transmittance of the sample is actually caused by growth of E. coli. To verify this, individual
cells of E. coli were counted at one hour time intervals.
In order to count individual cells, heat-sealed slides of the sample must be made
and stained, as detailed in Section 1.5.3. Because the number of cells present on a
slide can be too great to count by eye, another method must be employed. Images
of the slides should be saved as image files by using a microscopic camera for further
analysis. An example image of such a slide can be seen in Figure 1.8.
ImageJ, an open source program made available by the National Institutes of
Health (NIH) makes the cell counting process much less tedious. The software can
define a threshold and create a binary analog to the original image (see Figure 1.9).
A binary image is created from the high resolution jpeg image of the slide by first
converting the image to greyscale. This greyscale image is then subject to a threshold
15
Figure 1.8: A stained sample slide photographed through a microscope camera. These
images are used to automate the cell counting process.
determined by isodata algorithm. This algorithm starts by taking an initial threshold
to determine a basic distinction between objects and background. This is the initial
value used in an iterative process that calculates the sum of the average values above
and below the threshold. The threshold is incremented and this process continues
until the value of the threshold is greater than the composite average value. (1.13)
demonstrates the basis of this algorithm. [56]
Background Average + Object Average
Threshold =
2
(1.13)
The threshold is then used to create a binary rendition of the original image,
that is values higher than the threshold are replaced by 1 and values at or below the
threshold are replaced by 0. This binary rendition allows the software to count the
number of circular or elliptical shapes present. The software searches for a collection
of pixels occupying a determined area that satisfy specific shape requirements. This
is analogous to an approximate count of the number of cells present, without the bias
introduced by manual counting.
The software also is able to, instead of counting the number of cells present,
16
Figure 1.9: A binary rendition of the slide above. The color image was first converted
into a greyscale image, whereupon a threshold was used to convert it into a binary
representation for processing.
calculate the area fraction, or the fraction of the total area of the image that is
composed of spots representing cells. This latter method reduces the error introduced
by the phenomenon of clumping - where many cells may be grouped together and
counted as one “circle” by the former method.
1.4
Previous Work
A. Cobb studied the growth of E. coli in detail using transmittance techniques by
way of the Beer-Lambert Law. This research has led to a proof of concept that the
presence and growth of bacteria can reliably be detected by the change in transmittance caused by increased turbity due to bacterial growth. Additionally, her research
has indicated that E. coli responds best to and shows a significant change in transmittance when exposed to a light source of wavelength between 500nm and 600nm.
Though previous experiments have indicated that 400nm may be a more optimal
wavelength at which to perform these studies, we have chosen to use Cobb’s values
because they were obtained with the same equipment and techniques as we are using
17
in this particular set of experiments. The ultraviolet (UV) spectrum was left specifically unexplored, because UV light causes bacterial death. In fact, UV lamps are
used in operating rooms for sterilization.
From this, efforts were made to create a device to perform the transmittance
analysis of bacterial growth in a field setting. Because lab tools to track transmittance
over time can be very costly (up to thousands of dollars), a field device would have to
be a simplified version that only performed the necessary functions to reliably detect
the presence of bacteria. This device is the OBD mentioned in Section 1.1. Several
groups of undergraduate students have endeavored to design an OBD, which have
ranged in sophistication from simplistic to a programmed Stamp chip, as well as a
variety of physical designs.
1.5
Biological Laboratory Techniques
Laboratory techniques for biological specimens differ greatly from physics and
chemistry techniques. There is an increased need for clean techniques working with
bacteria, not only to prevent the spread of an infection, but also to prevent contamination with other biologic organisms. These techniques are the basis of microbiological
work in any laboratory.
1.5.1
Media
The media in which bacteria are grown can greatly influence the growth speed of
the organisms and the carrying capacity of the culture. There are two main categories
of bacteria culture media, which differ mainly in physical state. Media can be either
differential or selective. Most kinds of organisms can grow in differential media, but
exhibit different growth behaviors. Selective media allows only organisms with certain
properties (for example, gram-negative bacteria) to grow. [57] The most commonly
used medium is called agar, which is a gel-type substance that is usually placed in
18
a petri dish, or occasionally a test tube. The sample is then streaked across the
surface of the agar, generally with an inoculating loop. This process is called plating.
After incubation, bacterial growth can be observed as visible colonies growing on the
surface of the agar, and the amount of growth is measured in the number of colonies
present (not the size of the colonies), thus the term colony forming unit or cfu. The
agar can be prepared in such a way that it is nutrient rich. The types of nutrients
included in the broth determine which species of bacteria are able to grow in this
media. [57]
This project, however, primarily uses a different form of bacteria media: broth.
Broth is a thin, watery liquid that provides a complete, nutrient-rich environment
for the intended organism. A given broth generally corresponds to an agar that is
identical in nutrient content, differing only in physical form. E. coli is grown in LB
(lysogeny broth) or its corresponding agar. LB contains peptides, casein peptones,
vitamins, minerals and some trace elements. It can be made using three different
formulas: LB-Miller, LB-Lennox, and LB-Luria. Unless otherwise stated, the broth
used in the following experimentation was made according to the LB-Luria formula.
Unlike an agar plating of bacteria, a bacteria culture grown in broth does not produce
colonies. Bacterial growth in a broth media is evident by a change in turbidity; that
is, the liquid becomes murkier and sometimes experiences a slight change in color.
[57]
1.5.2
Inoculation
In order to culture a new sample of E. coli, an inoculating loop is used. Inoculating loops utilize surface tension to collect the same, small volume of a given liquid
consistently. An example of an inoculating loop can be seen in in Figure 1.10.
An inoculating loop is sterilized by exposing it to an open flame until the metal of
the loop turns red hot. The loop is then considered sterile, and should not be rested
19
Figure 1.10: An inoculating loop used for the culturing of the bacteria. Can be used
for both broth and agar media.
against any surface, as this will cause the loop to become contaminated. It should be
noted that following sterilization, the loop must be allowed to cool for a short period
of time (in the case of this experiment, 45-60 s) before exposure to the inoculum in
order to ensure that no bacteria is damaged. [57]
To collect the sample, the lip of the culture container must be briefly placed into
the flame as well as the edge of the lid, in order to create an air current that prevents
contamination of the culture by airborne pathogens. The lid should never be put
down, as this can also cause the culture to become contaminated. The inoculating
loop is then dipped into the broth and removed with the culture container held at an
angle. The lip of the culture container as well as the lid should be flamed once more
before recapping. [57]
As with the culture container, the lip and lid of the sample container must be
briefly flamed, and the lid must never be put down. The inoculating loop should
be inserted into the sample broth and then removed. The lip and lid of the sample
container must then be flamed once more before recapping. Following inoculation,
the loop must not be put down until re-sterilized. This is done by again exposing it
20
to an open flame until the metal becomes red hot. [57]
1.5.3
Gram Staining
There are two major divisions of bacteria – gram-negative and gram positive.
Gram-positive (see Figure 1.11) bacteria have a thick outer coating of peptidoglycan,
whereas gram-negative bacteria (see Figure 1.12) have a thin layer of peptidoglycan
located between two other cell membranes. [58] Gram-staining shows whether a
specific type of bacteria is gram positive (i.e. keeps the purple stain) or gram negative
(where the purple stain is washed away). To perform a gram-stain, a heat-sealed
slide of the sample must first be made. Heat-sealed slides are created by using an
inoculating loop to place a small portion of the liquid sample on the center of the
slide. The slide is then passed over a flame until the all water has evaporated from the
slide. The heat-sealed slide is then dyed. Crystal Violet, the first dye, is applied for 30
seconds. The slide is then rinsed with water. This process is then repeated with the
second dye, Iodine. Together, these dyes stain any cells on the slide purple. The slide
is then rinsed with an alcohol (usually methanol) for three seconds, and is quickly
rinsed with water. The alcohol dehydrates the cell membrane, so the gram-positive
bacteria retains the stain, while the gram-negative bacteria does not. The slide is then
counterstained with safranin. Safranin is a red/pink stain. When observed under a
microscope, gram-positive bacteria appears purple, while any gram-negative bacteria
will appear either pink or red depending on the intensity of the stain. Gram-staining is
important in the medical world, as gram-negative and gram-positive bacteria behave
differently in the presence of antibiotics such as penicillin. A gram-stain can direct
a physician to choose the most effective antibiotic for the infection in question. In
the research world, gram-staining is important for identification of bacteria under a
microscope. [58] [57]
21
Figure 1.11: A positive gram-stain. Gram-positive bacteria retain the purple dye by
way of a thick layer of peptidoglycan in their cell wall.[59]
Figure 1.12: Negative gram-stain. Gram-negative bacteria do not retain the purple
dye of the gram-stain.[59]
22
1.5.4
Identification of Bacteria
Beyond the gram-staining divisions, bacteria can also be differentiated based on
shape. There are spherical (cocci), rod-shaped (bacilli), and spiral (spirilla) bacteria.
By determining the shape and gram-stain of a bacterium in a microscope, the species
of the bacterium can be determined (or the possibilities narrowed down). Identifying
bacteria is important in growth studies in order to confirm that the intended species
of bacteria is indeed the most prevalent species in the test sample. E. coli is a gramnegative rod-shaped bacterium. Under a microscope, E. coli appears as either a pink
or red rod, or sometimes spherical. It is important to note that in a culture of a
rod-shaped bacteria viewed under a microscope, a spherical item does not necessarily
mean the sample is contaminated, as rod-shaped bacteria can be viewed end-on and
appear spherical in the “flattened” view from the microscope. [59]
Figure 1.13: Spherical bacteria, referred to as cocci. [59]
1.6
Experiment Overview
This experiment uses the principles of the Beer-Lambert Law to model bacterial
growth. Monochromatic light is passed through a sample cuvette, and the intensity of
the transmitted light is measured by a sensor (see Figure 1.16). The initial intensity
23
Figure 1.14: Rod-shaped bacteria, referred to as bacilli. [59]
Figure 1.15: Spiral bacteria, referred to as spirilla. [59]
24
(immediately after the sample is created) is defined to be 100% transmittance. As
this intensity measurement changes over time, the transmittance changes according
to (1.10), which, by way of (1.12), indicates a change in bacterial concentration in
the sample.
Colloidal Solution
Light Path
Sensor
Light Source
Cuvette
Figure 1.16: General experimental setup. A light source is incident on a sample of
bacteria. The cells suspended in this sample absorb fractions of the incident light.
The transmitted light is then measured by a detector on the other side.
Because the Reynold’s number for an E. coli in water is so small, the cells are
suspended in the broth, forming a colloidal solution. Colloidal solutions often result
in the scattering of light, which can be seen in Figure 1.16, and is discussed in more
detail in Chapter 4. The benefits of suspended bacteria resembling a colloidal solution
include being able to introduce the light source at any height within the sample, as the
suspension guarantees that the particles will not settle out to the bottom, thereby
making particle density non-variable with respect to depth. Though this height is
held constant throughout this experiment, any slight discrepancies can be assumed
not to result in any additional systematic error.
25
1.6.1
Lab-grade Spectrophotometer
The Genesys 6, a lab-grade spectrophotometer, was used in this experiment to
obtain a baseline for the behavior of transmittance in response to bacterial growth,
to which subsequent measurements were compared. The Genesys 6 is also capable
of collecting transmittance data for all wavelengths of light in the visible spectrum.
This capability was used to determine the optimum wavelength of light at which to
observe the change in transmittance due to bacterial growth. At this wavelength, the
drop in transmittance occurs most quickly and is the most sharp.
1.6.2
Optical Bacteria Detector I
The Optical Bacteria Detector I (OBD I) accomplishes the same overall goals
as the lab-grade spectrophotometer, with the exception of testing at multiple wavelengths. The OBD I uses the optimum wavelength determined by the use of the
lab-grade spectrophotometer, and tests for changes in bacterial concentration in the
same way. The notable difference with the OBD I is that it is a portable field device,
manufactured in-house at a very low cost.
1.6.3
Optical Bacteria Detector II
The Optical Bacteria Detector II (OBD II) is the second generation of the OBD
I. The OBD II was also manufactured in-house with the goal of becoming a portable
field device. It utilizes data collection of both light transmittance and light scattering.
Differences in light scattering can determine the size of the organisms present in the
sample.
26
CHAPTER 2
TURBIDITY STUDIES WITH SPECTROPHOTOMETRY
In order to build the OBD, a complete set of data encompassing all visible wavelengths was needed to determine specifications, and to provide a reliable baseline for
how readings from the OBD should behave. UV-Vis spectrophotometry is common
amongst colorimetrists, but, unfortunately, not microbiologists. Turbidity is a quality
of bacterial cultures that is often overlooked or at least taken for granted in microbiology. As such, there is no biological instrument yet designed for the purposes of this
experiment. Instead, a standard UV-Vis spectrophotometer was used. Because this
device was designed for the purpose of analyzing chemicals, not suspensions of microorganisms, we were forced to apply various alterations and additions to the standard
protocol and software used. This chapter describes these experimental modifications,
and the process of collecting and analyzing this data.
Light Path
Colloidal Solution
Sensor
Sample Cuvette n=0-5
Monochrometer
Cuvette
Figure 2.1: Genesys 6 setup. The Genesys 6 uses tungsten halogen and deuterium
bulbs as a light source and a photodiode as a sensor. It outputs data in percent
transmittance.
27
2.1
Genesys 6
In order to collect this data, a lab-quality spectrophotometer was used. The
Genesys 6 is a UV-Vis spectrometer capable of running six samples (contained in
standard cuvettes) at a time. The software associated with the Genesys 6 requires
a baseline scan. This baseline scan is performed using the control sample of the
group (the cuvette labeled “B” in the Genesys machine). The baseline scan is used
to subtract out background noise, as well as to give an initial intensity measurement
to calculate an initial transmittance value, which in turn is later used to calculate
percent transmittance (see (1.10)). The wavelength range over which to test the
sample can also be adjusted. In this experiment, the range is limited to the visible
spectrum (390nm - 750nm). The Genesys 6 then outputs .dat files that report the
percent transmittance (based on the baseline scan) resulting from each wavelength
tested on the sample. [36]
Unfortunately, the Genesys 6 is not automated or designed for time-dependent
studies, which means data must be taken manually at each desired timestep. This
becomes very time consuming and prevents the data from being as consistent, due
to the inability to necessarily take data at the same time interval for each data set.
Thanks to N. Hunter, there is now a macro program that collects data hourly from
each sample, over a period of 22 hours with consistent specifications. The macro
utilizes basic computer commands to cycle through a script that automatically selects
the same options on the Genesys 6 graphical user interface (GUI) one would select
manually. This GUI is available as open source software as well, and is called Datalyse.
[60] The script cycles through this process each hour, having been set on a time delay
counter. The code used for this macro can be seen in Appendix B. This improvement
has allowed consistent data collection following the initial setup.
28
Figure 2.2: Genesys 6 UV/Vis-Spectrophotometer. The Genesys 6 tests six cuvettes
at once, and tests over a wide range of wavelengths.
29
2.2
Establishing Initial Conditions
Among the many difficulties in dealing with biological specimens is the inability
to know which growth phase (see Section 1.3.5) a culture is in before testing begins.
It was hypothesized that this initial condition could affect the lag time or the slope
of the exponential growth rate after the bacteria is recultured.
A bioreactor (see Figure 2.3) is the most efficient means for controlling initial
conditions of living substances. A bioreactor operates by maintaining a constant environment for the organism (this method works extremely well for both bacteria and
fungi). The bioreactor uses a temperature control system based on cycling heated/cooled water of the appropriate temperature around the bioreactor’s culture enclosure.
The temperature is set at the optimum temperature for growth of the organism being
maintained. The bioreactor chamber itself is filled with the desired broth in which to
grow the organism. A limiting nutrient is then gradually added to the mixture. It is
the rate at which this nutrient is added that can be used to control the growth phase
of the culture. Limiting this nutrient (“starving” the bacteria), will force the bacteria
into a stationary phase, while providing an excess of this nutrient will encourage the
bacteria to enter the exponential growth phase. Additionally, the bioreactor can control foam and pH levels within the culture. A sensor for each is constantly in contact
with the culture media, which is monitored by an external computing system that
utilizes a negative feedback loop to pump a culture-friendly acid, base, or antifoaming agent into the enclosure. A motor and compressed air system keep the enclosure
oxygenated (utilizing the same principles used in maintaining fishtanks) and properly
mixed. This creates an consistent, controlled environment for the organisms. [38]
In this project, LB was used as the broth and glucose was used for the limiting
nutrient. The optimum temperature for growing E. coli is around 37◦ C (which,
of course, is regular human body temperature). This setup eliminated systematic
error occurring as a result of a culture being in a constant growth fluctuation. The
30
AntiFoam
E. coli
Acid Base
Nutrient
Temperature
Control
Temperature,
Foam and pH
Sensors
Motorized
Stirrer
Culture
Figure 2.3: A basic bioreactor setup. The bioreactor maintains a constant environment for the bacterial culture, by controlling temperature, pH and the amount of
nutrient available.
31
particular setup used in this experiment can be seen in Figure 2.4.
Figure 2.4: The bioreactor used in this experiment. A variable peristaltic pump was
used to control the rate of glucose addition in order to manipulate initial growth
phase of the culture.
2.3
Assumptions
As with all experiments, certain assumptions had to be made in order to gather
the necessary data. By far, the most important assumption was that the observed
change in transmittance of a sample was due to bacterial growth, as opposed to other
changes that could result in similar effects. Similarly, one must assume that the
intended species of bacteria (E. coli) is growing in the sample, as opposed to another
species that is able to subsist off of the same nutrients provided by the broth. Data
supporting the validity of these assumptions can be seen in Section 1.3.6. It must
also be assumed that the testing environment is clean. Though this would not be
32
the case in a field setting, it is necessary in order to gather the baseline data against
which the field data is compared. To account for all of these assumptions, bacterial
samples are kept isolated to the greatest degree possible, and standard precautions
for handling biohazards are observed.
2.4
Testing Protocol
A very specific procedure for testing samples in the Genesys 6 is necessary to
acquire the proper data.
1. Arrange bacterial collection line (see Figure 2.5) in pump so it is in the pumpingout position.
2. Secure sterile retrieval bottle to the end of bacterial collection line.
3. Pump approximately 2cc of contaminated broth into retrieval bottle.
4. Reverse the position of the bacterial collection line to pump the remaining broth
in the line back into the biostat.
5. Sterilize edges of broth bottle in alcohol flame.
6. Use sterile syringe to dispense 3 cc of broth into each of six cuvettes.
7. Sterilize edges of broth bottle and replace cap.
8. Sterilize inoculating loop.
9. Inoculate five cuvettes with bacteria from the retrieval bottle, sterilizing the
inoculating loop between each.
10. Sterilize edges of retrieval bottle and empty into bleach.
11. Turn on Genesys and allow self-calibration.
33
12. Open Datalyse on the computer.
13. Place blank cuvette in the B-slot in the Genesys.
14. Choose the Genesys by selecting Choose Device, Other Devices, Genesys 6
UV/VIS.
15. Run baseline scan by choosing Genesys, Baseline Scan, confirm default settings.
16. Place the cuvettes in slots 1-5.
17. Set up macro by choosing the macro icon in the lower right hand corner.
18. Double click 2-19-09.
19. Change trigger to Word/Phrase.
20. Save.
21. Close. Do not shutdown.
22. Select Datalyse window.
23. Type “YEW”.
24. To clean up, place all cuvettes in a tub of dilute bleach.
25. In case of spill, dilute an equal amount of bleach into water. Spread bleach
solution over affected area and wipe up using paper towels, while wearing gloves.
2.5
Results
Data was taken with the Genesys 6 over many years, using many different cultures
of bacteria, and our knowledge related to the experiment has increased, changed the
way in which the experiment has been performed. Initially, specimens were not kept
34
Figure 2.5: A labeled photograph of the setup. A second pump is used to collect
culture solution from within the bioreactor.
in bioreactors, nor was the initial condition of the bacteria attempted to be controlled
in any way. There was no data involving lab temperature, nor was there good recordkeeping concerning which data was taken with which bacteria subculture nor which
batch media broth was used. Data sets taken under these conditions can be seen
in Figure 2.6. Note that these data sets are widely spread, and even converge to
different transmittance values. Though it is hypothesized that the above-mentioned
systematic errors were responsible for these discrepancies, there is no means by which
to prove this hypothesis either way.
Following the disappointment encountered when analyzing the data seen in Figure 2.6, measures were taken to eliminate these systematic errors. Bioreactors were
employed to control the initial state of the bacteria, and more diligent recordkeeping
filled in the previously missing information regarding broth and temperature. Data
collected following this revised protocol can be seen below in Figure 2.7. Note that
the lag times were more consistent following the implementation of these procedural
35
Figure 2.6: Data taken by the Genesys 6 prior to implementation of new protocol.
There is a wide spread in lag time for this data, however it was collected using several
distinct batches of bacteria, without a stabilized protocol.
corrections.
Included in this new data collection is data taken with differing initial concentrations of bacteria in each cuvette. This experiment was performed by numbering
the cuvettes to be put in the Genesys 6 by their corresponding label in the machine
(0-5). A corresponding number of inoculating loops of bacteria were added to each
cuvette; that is, the baseline sample had none, the first sample contained one loopful
of bacteria, the second two loopfuls, and so on and so forth. As expected, the cuvettes
containing more bacteria initially (in this case, those represented by the yellow lines)
entered the exponential growth phase first.
In order to observe the behavior of bacteria from one initial state transplanted
into a new environment, the bioreactor was used to prepare cultures in both the
exponential and the stationary phases of growth. Bacteria from each initial phase
was tested with the Genesys 6, and can be viewed in Figure 2.9. Note the distinct
differentiation between bacteria initially prepared in the stationary phase as opposed
to bacteria prepared to be in the exponential growth phase. It is also important to
address another revision in protocol that took place before these measurements. The
36
% Transmittance
100
80
60
40
20
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Time (hr)
Figure 2.7: Data collected following the protocol revision. The spread of data is
significantly narrower and follows two groupings corresponding to the two cultures
used to collect the data itself. The transmittance values follow the inverse of an
expected bacterial growth curve.
Figure 2.8: Cuvettes tested with differing levels of initial bacterial concentration. The
yellow lines, which display a significantly smaller lag phase, represent samples that
were inoculated with five times as much bacteria as the other samples.
37
broth used, both for culturing and for testing was changed from LB to a nutrient
broth. LB is very well suited for the growth of E. coli, while nutrient broth is geared
toward growing a variety of specimens. This explains the change in slope of the
growth curve and the increased difficulty in keeping samples uncontaminated.
Figure 2.9: A comparison plot of bacteria prepared in stationary and exponential
growth phases. The bacteria intially prepared in the stationary phase has a slightly
shorter lag period than bacteria prepared in the exponential growth phase.
Despite the wide spread of data sets present in this data collection, it is clear both
that there is a definitive shape the plots conform to, and that this shape is consistent
with what is accepted as the model of population growth for bacteria.
2.6
Analysis
Though the curves seen in Figure 2.6 do follow the general trends of bacterial
growth, they offer little to no insight regarding mathematical behavior of the organisms. However, the curves are concentrated in two distinct groupings, one centered
about 7 hours, the other centered about 16 hours. It was this observation that led to
the hypothesis that initial growth conditions were affecting the lag times in testing.
38
Taking care to eliminate systematic errors did well to decrease the spread of data
from about 12 hours to about 4 hours (Figure 2.7). This data, however, also suffered
the inconvenience of displaying two distinct groupings. Not only are there two points
of high concentration for the exponential phase, but these two groups also diverge
during the stationary phase - one group continues in a downward slope, whereas
the other has a slight increase in transmittance, further supporting the hypothesis
that some initial condition was causing the discrepancy. Comparing data taken from
bacteria in these two initial growth phases, there are indeed two distinct groups, as
the different cultures respond separately to a new environment. Interestingly enough,
it is the stationary phase that begins to grow first. Figure 2.10 explores the difference
between the transmittance values as
Stationary(t) − Exponential(t)
Inconsistency = Abs
1/2(Stationary(t) + Exponential(t))
(2.1)
.
We see a large spike in this value at eight hours, as the transmittance of the
stationary phase bacteria begins to decrease. This shows that there is a significant
difference between the lag time associated with bacteria prepared in the stationary
and exponential phases.
We hypothesize that the explanation for the extra lag in bacteria that had previously been in a constant state of exponential growth is that they suffer shock from
being transplanted into an environment with a relatively low level of glucose. The
bacteria may need time to adjust to the new nutrient concentrations.
2.6.1
Error Contributions
Even with a meticulous, standardized protocol, there are many opportunities for
error to contribute to our experimental results. The presence of biological specimens
introduces an additional level of uncertainty that is very important to standardize.
The possible sources of error, listed in order from most influential to least influential,
39
0.06
Inconsistency
0.05
0.04
0.03
0.02
0.01
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18
Time (hr)
Figure 2.10: Analysis in discrepancy between average values for bacteria prepared
initially in the stationary phase and the exponential phase. The standardized difference between stationary and exponential phase increases at the end of the lag phase
for the bacteria prepared in stationary phase.
are as follows:
• Systematic Error
– Biological
1. Genetic variation in culture
∗ A culture can evolve over a short period of time, due to the fast
reproductive rate of E. coli.
∗ Re-culturing can introduce unknown elements to the culture, and
any of the means of genetic variation discussed in Chapter One
may or may not occur.
∗ Cultures started from various initial samples may not behave identically.
2. Contamination
∗ Equipment used is classified as clean, not sterile.
40
∗ Protocol is clean, not sterile. Commensual bacteria from tester
may contaminate samples.
3. Broth inconsistency
∗ No batch of broth is identical to another. Water may boil off
during sterilization, affecting the broth concentration.
∗ Broth inside the bioreactor has a different nutrient composition
than broth used in testing, due to the glucose added as limiting
nutrient.
4. Culture concentration
∗ As in testing samples, the concentration of bacteria in a culture
increases over time. This corresponds to a slight increase in initial
inoculum concentration in test samples.
– Procedural
1. Time discrepancies
∗ It takes the Genesys 6 four minutes to analyze a sample, while it
only takes two minutes to prepare a sample. This leads to a two
minute time discrepancy.
∗ This becomes very prevalent towards the end of a run, because the
time discrepancy is cumulative.
2. Temperature of inoculating loop
∗ The inoculating loop is cooled before inoculation so as not to kill
any bacteria.
∗ Inconsistent inoculating loop temperature at time of inoculation
can change the number of living bacteria captured by the inoculating loop.
∗ Cooling time was standardized to 90 seconds to regulate this.
41
• Statistical Error
1. Tolerance of Genesys 6
– Directly affects raw measurements.
– Characterized by Genesys 6 manual.
– Represented as vertical error-bars in data analysis.
2. Cell Counting
– Multiple methods yield different results.
– Does not affect spectrophotometric studies.
3. Analytic Methods
– Fit parameters accurate to one part in 100.
Table 2.1 lists, in descending order, the quantitative values for these sources of error. An empty value field indicates a source of error that has been corrected following
initial testing, and thus eliminated in data sets used for statistical analysis.
Table 2.1: Error contributions for Genesys 6 testing
Source of Error
Type of Error
Value
Genetic Variability
Biological
±0.73 hr
Genesys 6 Tolerance
Statistical
Up to ±0.014 (transmittance)
Time Discrepancies
Procedural
+2.0 ± 0.2 min
Analytic Methods
Statistical
1 part per 100
Contamination
Biological
Determined by Control Sample
Broth Inconsistency
Biological
-
Culture Concentration
Biological
-
Inoculating Loop Temperature
Procedural
-
Cell Counting
Statistical
-
42
2.6.2
Statistical Analysis
The group of ‘revised’ data is composed of all standard experimental runs from
2009 onward. Though the data appears to suffer from a wide spread, closer inspection
reveals that this is due to horizontal shifts. This would normally be very disconcerting,
however further inspection reveals that data sets taken from the same culture have
extremely small spreads. The greater the spread in time between the actual testing
(for example, data observed in May compared to data observed in September), the
greater the time shift observed when plotted.
Taking this into consideration, the average time shift of these curves will correspond to the time shift present when all data sets are fitted mathematically to a single
curve. Assuming this analysis is sufficient, we will proceed to analyze only the error
in transmittance and absorption values.
Like most population models, the bacterial growth curve is an S-shaped or sigmoid
curve. A popular model for this curve is (2.2), where P is population, a, b, c and
k are adjustable parameters, and t is time. This model stems from equation (1.7),
having generalized the parameters. The parameters of this model allow us to account
for different durations of the lag phase, various slopes of the exponential phase, and
various carrying capacities.
P =
a
b + ce−kt
(2.2)
However, the raw data we collect is not in a form that abides by this curve.
Instead, we calculate percent absorption (a quantity that is directly correlated with
the population of the culture) from percent transmittance by (2.3).
A=1−T
(2.3)
Each data point was converted to percent absorption, and imported into Mathematica for data processing. The FindFit function was used to fit all of the data
43
points in the revised set to the model presented in (2.2). The resulting parameters
(see Table 2.2) are expressed with two significant figures of accuracy. This model
only accounts for the lag, exponential and stationary phases, however further modeling could include the death phase as well. The death phase is apparent in this data
as a decrease in absorption around 16 hours. This new model would likely be of the
same form with an additional exponential decay.
Table 2.2: Parameter Values for Genesys Data
Parameter Value
a
1.35
b
0.023
c
10.8
k
0.77
All data points for each time step were then averaged to provide the points seen
in Figure 2.11.
The error contributed by the device itself was calculated by analyzing all of the
control sets within the revised data collection. Theoretically, all values of uncontaminated controls should be precisely 100%, as the baseline function of the Genesys 6
software sets it as such. The standard deviation of these data points was calculated
and set as the vertical errorbars seen in Figure 2.11. A reduced χ2 analysis of this
data yields χ2red = 1.36. Ideally, χ2red = 1, and when χ2red >1, the fit has not fully
expressed the data. As stated above, this discrepancy is likely due to this model not
including the death phase, which is apparent in the plot itself.
Figure 2.11 is very encouraging, as the fitted curve lies within the errorbars assigned from the error analysis. The point which is in least agreement with the curve
is at the upper cusp of the S-curve, which, for bacterial detection, is not a point with
which we are particularly concerned.
These observations have helped develop a better understanding of the correlation
44
Figure 2.11: Curve fitted to the average values of the Genesys 6 data. Error-bars
represent the variance in the Genesys 6 measurements over time for a sample of
constant transmittance.
between light transmittance and bacterial growth. It is with this knowledge that a
smaller, more adaptable device can be created in order to apply this theory in the
field.
2.6.3
Cell Counting
The cell counting data can be seen in Figure 2.12 and Figure 2.13.
Using the fitting methods described above, the area fractions and cell counts calculated by ImageJ were modeled mathematically. The parameters for cell population
as a function of time (see Table 2.3) and area fraction as a function of time (see
Table 2.4) are expressed below using two signficant figures.
45
Figure 2.12: Cell counting in terms of colony forming units. This method counts
individual cells, but does not account for cell clumping.
46
Figure 2.13: Cell counting in terms of area fraction of cells. This is the preferred
method, as it accounts more closely for the clumping of cells.
47
Table 2.3: Parameter Values for Cell Population
Parameter Value
a
13.0
b
0.025
c
0.43
k
0.74
Table 2.4: Parameter Values for Area Fraction
Parameter Value
a
0.87
b
0.22
c
8.98
k
0.79
Figure 2.14 and Figure 2.15 show the curves corresponding to Table 2.3 and Table 2.4 respectively, superimposed over their supporting data sets.
Area Fraction
4
3
2
1
2
4
6
8
10
12
Time HhrL
Figure 2.14: A mathematical fit of the cell count data shown above, in colony forming
units.
The same bacterial culture used for cell counting was then tested for transmittance
48
Population HcfuL
500
400
300
200
100
2
4
6
8
10
12
Time HhrL
Figure 2.15: A mathematical fit of the area fraction data shown above.
changing by the Genesys 6. This transmittance data can be seen in Figure 2.16. The
correlation of these data sets support the assumption that bacterial growth is indeed
causing the change in sample turbidity, as opposed to other factors.
There is a certain amount of inherent error involved in the cell counting process.
Most notably, using the same threshold for each image is only suitable when each
slide is stained similarly. However, it is extremely difficult to achieve a consistent
level of staining, particularly over a period of time. We have eliminated the human
factor in pursuit of also eliminating bias; however, it has also decreased our ability to
use human judgment. The other significant error incurred is the subject of clumping.
There are many occurrences of cells growing extremely close to one another on the
slide, so much so that the software is unable to count them as multiple entities. This
is why we have chosen to rely on the area fraction data, as this method calculates
the portion of the image contributed by cells. Also, cell counting is affected by the
orientation of bacteria. This contribution is lessened when dealing with spherical
bacteria, however E. coli is rod-shaped. A rod-shaped bacterium oriented on-end
49
Relative Growth
1.2
1
Area
Fraction
Absorption
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
7
8
9
10
11
Time (hr)
Figure 2.16: A normalized absorption curve as obtained from the Genesys 6 compared
to corresponding area fraction data. This plot shows the correlation between actual
cell growth and change in transmittance.
appears to be circular under a microscope. An algorithm constructed to determine
the number of ellipses present would therefore have to be modified to deal with the
elliptical eccentricity associated with bacterial orientation. Though area fraction still
accounts for these particular bacteria, they contribute less to the actual value of area
fraction than a bacterium on its side. By eliminating human bias, we also eliminate
the ability to account for these inconsistencies.
50
CHAPTER 3
THE OPTICAL BACTERIA DETECTOR I: ABSORPTION ONLY
This chapter details the development and testing of the Optical Bacteria Detector
(OBD).
Light Path
Colloidal Solution
Phototransistor
LED
Cuvette
Figure 3.1: Basic setup for the OBD I. The OBD I uses a 560 nm LED as a light
source, and a PNZ150 phototransistor as a detector.
3.1
Requirements
In order to be effective as a field substitute to machines like the Genesys 6, the
OBD must meet a variety of requirements. Most importantly, the change in transmittance due to bacterial growth must be clearly evident in the results gleaned from
the OBD. These measurements will be taken with the same timestep (one hour) as in
testing with the Genesys 6; however, the data will be taken and recorded manually.
Ideally, when in use in the field, only an initial measurement and a followup measurement (observed after a predetermined period of time) will be taken and compared
against one another to determine the presence of bacteria. The OBD will use cuvettes
as sample collection containers. Cuvettes will be covered, and pre-filled with either
powdered broth or a liquid broth concentrate. As discussed in Section 1.1, size and
cost of the device are extremely important as well. Because this device will eventually
51
be used in remote areas and third world countries, it must be easily transportable
and very affordable. These constraints have shaped the design and capabilities of the
OBD itself.
3.2
Limitations
As with all devices, the OBD I suffers from certain limitations. The device does
not measure transmittance values for multiple wavelengths, nor is it automated. An
automated device of this sort would perform tests at zero and twelve hours, and
determine if the value has changed, and if it has, supply some sort of indicator, such
as an LED or an alarm. The device is also unable to determine the specific species
of bacteria growing in the water source. Though use of selective media could more
closely determine certain qualities of the bacteria, such as response to a gram-stain,
the device cannot determine these properties directly. With these limitations in mind,
we can proceed to use the OBD I to detect contamination in water.
3.3
Design
The design of the OBD can be divided into the physical design and the layout of
the electronics. This section serves to describe and provide figures of both.
3.3.1
Circuit
The circuit design for the OBD follows a very simple phototransistor circuit layout.
A standard green LED of wavelength 560nm provides the light source. Though LEDs
do not produce monochromatic light, the specificity they provide is close enough for
the purposes of the OBD. A phototransistor (PNZ150) is used as the light sensor.
The signal from the phototransistor is amplified by a LM358 op-amp, and delivered
to a voltmeter. The voltage outputted by the op-amp is read in milliVolts via the
voltmeter. A schematic of the circuitry of the OBD can be seen in Figure 3.2.
52
1 MΩ
1 kΩ
1.5 V
1.5 V
LM358
+
1.5 V
1.5 V
1 kΩ
1 kΩ
Vout
Figure 3.2: Circuit schematic for the OBD I. The output from the phototransistor is
fed into an op-amp to amplify its signal, before being delivered to a voltmeter.
This circuit design can be improved by ensuring the linearity of the system. Instead of biasing the phototransistor by connecting it to ground through a resistor, it
can be directly fed into the op-amp. Also, a voltage regulator, most likely a switching
regulator, can be used between the battery supply and the circuit itself, to supply a
more ideal voltage source.
3.3.2
Housing
The housing for the device proved to be particularly challenging to design, as
it must prevent external light from shining into the cuvette. The main part of the
housing is built from a plastic container, with a metal screw-on lid. The metal sheet
allows for openings for the electronic readout, an on/off switch, and the slot for the
cuvette. The slot for the cuvette is a black plastic box, fitted to the size of the
cuvette. Small holes are drilled through opposite sides to allow access for the LED
and the phototransistor. A similar black, plastic cover blocks out the remainder of the
53
ambient light, and is attached to the metal lid by a length of fishingline. Schematics
of this housing can be seen in Figure 3.3 and Figure 3.4, along with a photograph of
the prototype in Figure 3.5.
Sample
Slot
Cap
Data Readout
ON
Power
Switch
OFF
Figure 3.3: Top view of the housing for the OBD I. Features include an on/off switch,
a cap for the sample container, and an LCD display.
3.4
Testing Procedure
Two procedures apply for testing samples with the OBD: lab testing and field
testing. The lab testing procedure was used to verify that the OBD was performing
as anticipated, while the field testing procedure is that which would be used to test
a potentially contaminated body of water for the presence of E. coli.
54
Digital
Multimeter
Cap
Lid
Circuit
Board
Power
Supply
LED
Sensor
Sample
Slot
Base
Figure 3.4: Internal view of the housing for the OBD I. The circuit boards are mounted
together, and the sample container is completely enclosed to prevent interference from
ambient light.
55
Figure 3.5: Photograph of the OBD I.
56
3.4.1
Lab Testing
The protocol for using the OBD to test bacterial samples is very similar to that
detailed in Section 2.4. Following the inoculation of samples, an individual baseline of
each sample cuvette must be taken. A reading for each cuvette must then be recorded
for each sample every hour for the following 24 hours. Results can be normalized to
one another by setting the initial values at 100%. Though data-taking by this method
is more tedious than using the Genesys 6, the nature of the device and manual data
acquisition allows more than five samples to be tested in a given 24 hour period.
3.4.2
Field Testing
When using the OBD for field testing, different inoculation techniques must be
used. Because the concentration of E. coli is much lower in a contaminated body
of water, using an inoculating loop to culture bacteria from the water to a cuvette
of sterile broth may not transfer enough viable bacteria to grow a healthy culture.
Instead, water must be collected from the field source using a needle and syringe, and
injected into a covered cuvette containing either highly concentrated liquid broth,
or the base broth powder. The sample water will bring the broth to the desired
concentration. A baseline measurement must be recorded for each sample at this
initial time, and either subsequent measurements should be recorded every hour, or
a single follow-up measurement must be recorded between 10 and 14 hours later.
3.5
Difficulties
In the development of the OBD, many complications arose. These complications
varied from difficulties involving the media, to inconsistencies in the OBD itself. In
order to ensure that the OBD is a reliable means of detecting bacteria, these issues
had to be addressed before significant amounts of data acquisition.
Initially, field samples were to be collected by taking 3 cc of sample water and
57
injecting it into a covered cuvette containing a pre-measured amount of powdered
broth. The cuvette would then be shaken to mix the broth. However, should the
environmental temperature be significantly higher than room temperature, the broth
powder tends to melt and congeal, making it very difficult to mix the broth media.
To avoid this difficulty, prepared field testing cuvettes will contain 1 cc of triple
concentrated liquid broth.
The OBD itself suffered a few complications. The first of which was sensitivity
to ambient light, particularly in direct sunlight. Initially, the readings from the OBD
were dependent on whether or not the device was being used in direct sunlight or in
the shade. In order to circumvent this issue, greater measures were taken to block
light from the testing chamber, as well as to create a better seal on the removable
cap. It is still recommended that the OBD be tested in approximately the same level
of ambient light, to ensure that this will not affect the device’s performance.
The OBD is also slightly sensitive to heat. The effect on readings is most easily
observed when a sample is tested outside in the summer (anywhere from 90-100◦ F),
followed by testing indoors at room temperature (approximately 70◦ F). At higher
temperatures, the OBD can read up to 3 mV higher than at room temperature, as
the electronic components themselves are sensitive to heat. This is only problematic
when the OBD is expected to be taken into a different temperature environment
during a test. For non-prototype versions of the OBD, the interior of the device will
be filled with a thermally insulating foam that will prevent any electronic elements
from being overly affected by the change in temperature. The prototype OBD will
remain as is, in order to leave it open for adjustments to improve performance. Again,
purposefully changing the OBD’s temperature environment is not recommended, in
order to eliminate systematic error in testing.
58
3.6
Results
Currently, the only results available from the OBD I are from lab testing. A plot
showing the complete set of results obtained from lab testing of the OBD I can be
seen in Figure 3.6, and a closer view in Figure 3.7. These results are normalized to
100%.
Transmittance
1.0
0.8
0.6
0.4
0.2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Time (hr)
Figure 3.6: Complete data set taken by the OBD I. This data was taken over two
separate runs using the same batch of bacteria. Though transmittance only falls to
0.60, these tests were performed using nutrient broth instead of LB.
These results may appear different than those seen in Chapter 2, however they
were obtained while using the nutrient broth instead of LB. We must compare this
data to such plots as Figure 2.9. Both the data represented in Figure 2.9 and the
data obtained with the OBD I reach a final transmittance value of approximately
0.60. This indicates that the OBD I is measuring the same properties as the Genesys
6, which was our intent. We hypothesize that the long lag time present in these
samples is simply a characteristic of this culture of bacteria.
59
Transmittance
1.0
0.8
0.6
0.4
0.2
12
13
14 15 16
Time (hr)
17
18
19
Figure 3.7: Same data as above, a closer view, beginning at 12 hours.
3.7
Analysis
Though much of the analysis of data taken with the OBD I resembles that of the
data taken with the Genesys 6, there are more sources of error to consider, and a
different baseline from which to calculate model parameters.
3.7.1
Error Contributions
The biologically contributed error in testing with the OBD I is much the same as
that described in the previous chapter. However, due to a new protocol, procedural
error changes significantly. The sources of systematic and statistical error present in
testing with the OBD is listed below:
• Systematic Error
– Biological
1. Genetic variation in culture
60
∗ A culture can evolve over a short period of time, due to the fast
reproductive rate of E. coli.
∗ Re-culturing can introduce unknown elements to the culture, and
any of the means of genetic variation discussed in Chapter One
may or may not occur.
∗ Cultures started from various initial samples may not behave identically.
2. Contamination
∗ Equipment used is classified as clean, not sterile.
∗ Protocol is clean, not sterile. Commensual bacteria from tester
may contaminate samples.
3. Broth inconsistency
∗ No batch of broth is identical to another. Water may boil off
during sterilization, affecting the broth concentration.
∗ Broth inside the bioreactor has a different nutrient composition
than broth used in testing, due to the glucose added as limiting
nutrient.
4. Culture concentration
∗ As in testing samples, the concentration of bacteria in a culture
increases over time. This corresponds to a slight increase in initial
inoculum concentration in test samples.
– Procedural
1. Time discrepancies
∗ It takes only 30 seconds to analyze a sample with the OBD I,
while it takes two minutes to prepare a sample. This leads to a 90
second time discrepancy in the opposite direction as when testing
61
with the Genesys 6.
∗ This becomes very prevalent towards the end of a run, because the
time discrepancy is cumulative.
2. Temperature of inoculating loop
∗ The inoculating loop is cooled before inoculation so as not to kill
any bacteria.
∗ Inconsistent inoculating loop temperature at time of inoculation
can change the number of living bacteria captured by the inoculating loop.
∗ Cooling time was standardized to 90 seconds to regulate this.
3. Fluctuations in laboratory temperature
∗ Controlled by storing samples in a warm water bath set to 37.0◦
C.
∗ Though body temperature is optimal for growth of E. coli, it is
not necessary. It is only necessary to standardize the temperature,
though body temperature is the logical choice.
4. Handling of samples
∗ Cuvettes are capped, and handled only when wearing a fresh pair
of nitrile gloves.
∗ It is easy to determine whether or not a sample has been subjected
to smudging or contamination, both by direct observation and by
observing the data itself. Only two samples were clearly affected
(due to visible smudges on the cuvettes) and were not included in
statistical analysis.
• Statistical Error
1. Tolerance of OBD I
62
– Directly affects raw measurements.
– Characterized by fluctuations in transmittance over time when analyzing a control sample.
– Characterized in linearity of sensor.
– Represented as vertical error-bars in data analysis.
2. Analytic Methods
– Fit parameters accurate to one part in 100
Table 3.1 numerically describes the contributions of these errors, listed descending
by numerical value. These should be taken into consideration when reviewing the
contents of Section 3.7.2. An empty value field indicates that initial testing observed
the contribution of error, and the source of error has been sufficiently corrected.
Table 3.1: Error contributions for OBD I testing
Source of Error
Genetic Variability
OBD I Tolerance
Time Discrepancies
Analytic Methods
Contamination
Broth Inconsistency
Culture Concentration
Inoculating Loop Temperature
Laboratory Temperature
Handling of Samples
3.7.2
Type of Error
Value
Biological
±0.73 hr
Statistical
Up to ±0.011 (transmittance)
Procedural
-1.5 ± 0.2 min
Statistical
1 part per 100
Biological
Determined by Control Sample
Biological
Biological
Procedural
Procedural
Procedural
-
Statistical Analysis
The FindFit process used in the previous chapter was then used to model the
behavior of bacteria when measured the OBD I. The model described in (2.2) was
again used to chart the behavior, and the parameters a, b, c, and k are again expressed
to two significant figures in Table 3.2.
63
Table 3.2: Parameter Values for OBD I
Parameter
Value
a
6.2 ∗ 10−3
b
4.7 ∗ 10−4
c
61.42
k
0.76
As done with the data in Chapter 2, an average absorption value was expressed for
each of the timesteps involved. An error for each of these values was then calculated
from the variance of the OBD I exposed to a stable sample that experiences no
change in turbidity. This value is much higher than in the previous chapter, because
the tolerance value for the OBD I is very low. This curve and errorbar analysis is
shown in Figure 3.8.
% Absorption
14
12
10
8
6
4
2
5
10
15
20
Time (hr)
Figure 3.8: Statistical analysis of the curve fitted to OBD data. Error-bars are
calculated from the variation in measurements observed by the OBD I when testing
a control sample.
This analysis demonstrates that the bacterial growth model applies to this data
64
very accurately. All but one of the average values calculated lie within the errorbar
range of the fitted growth curve. Though this point is at an important timestep in our
analysis, (the lower cusp of the S-curve), it is of greater value than the curve itself.
This suggests that, on average, the sudden drops in transmittance values observed on
the OBD are actually precursors to the drop predicted by the curve described by the
parameters in Table 3.2.
This data shows that the OBD has reliable capabilities, and that field testing
will be the final trial to see whether or not it is a capable field device. The OBD
accurately portrays the trend of bacterial growth, and follows predictable patterns.
3.8
Linearity
A particular concern when constructing the OBD I was the linearity of its response.
Phototransistors are easily saturated and this can affect the reliability of the data
obtained through them. In order to determine the linearity of the system, I chose
to use the voltage and current dependence of the light intensity from the LED to
test the system’s response. It proved very difficult to use a filter to test linearity,
because the geometry of testing conditions were too confined, and observing response
outside of testing conditions is irrelevant. Instead, I wired the LED through the same
resistance value as used in the OBD, and connected powered it via an adjustable lab
power supply. This setup can be seen in Figure 3.9.
I then proceeded to vary the supply voltage to the LED in 0.25 V increments,
and observed the change in reading from the OBD I. This method was tested over a
supply voltage range of about 2.0 V (the forward drop voltage of the LED) and 9.0 V.
As seen in Figure 3.10, the sensor was not saturated, even beyond normal operating
conditions. Having taken these results into consideration, it is reasonable to believe
that the OBD I is operating in a linear regime.
65
Voltage Current
(V)
(A)
Variable
Power
Supply
Voltage
PWR GND
1 kΩ
ON
Phototransistor
(PNZ150)
LED
OFF
Circuit Board
Battery
Supply
Figure 3.9: Experimental setup to test the linearity of the response of the OBD I. A
variable lab power supply was used to supply voltage to the LED independent of the
rest of the circuit.
66
700
OBD Reading (mV)
600
500
400
300
200
100
0
0
2
4
6
8
10
Supply Voltage to LED (V)
Figure 3.10: Linearity analysis of the OBD I’s response. The initial jump at 2.0 V is
the forward voltage drop necessary for the LED itself to turn on.
67
CHAPTER 4
THE OPTICAL BACTERIA DETECTOR II: ABSORPTION AND SCATTERING
The transmittance of light through a bacterial sample is a concept that has been
explored very little by the scientific community. Though the information gained
from testing such a delicate property on unreliable and everchanging samples may be
limited, it is not without its benefits. This chapter explores the scattering properties
of light through bacteria.
4.1
Theory
Scattering of light off of particles is very dependent on the size of the particles
in question. Ideally, the particles are spherical with a diameter on the order of the
wavelength of light used. An E. coli bacterium measures one micron in diameter and
two microns in length, and is not spherical. However, the spherical approximation is
a good place to begin. The scattering of light off of spherical particles is called Mie
scattering. Mie scattering is used in the medical field to detect abnormalities (usually
cancer) by observing the interference patterns of the scattered light. [61]
Similar to Mie scattering is Tyndall scattering, which negates the restrictions of
shape that Mie scattering imposes. Tyndall scattering is used to determine the size
of colloidal particles in a suspension, which is essentially the goal of this application
of scattering. Tyndall scattering is most effective when shorter wavelengths of light
are incident on the suspension. [61]
Extinction measurements are highly important when analyzing colloidal solutions.
Extinction can be expressed as a combination of the two quantities this study has
been based on, absorption in scattering. Let us define extinction as in (4.1), where
E is extinction and S is scattering. [61]
E =S+A
68
(4.1)
Considering this, we find that our earlier assumptions are not quite right. Assuming the intensity of light lost is due to absorption alone is incorrect and to accurately
determine the absorption value we must observe both the transmitted intensity and
the scattered intensity. This will be discussed further in Section 4.2.
We can now use our definition of extinction mathematically. Let us define now I
and I0 as measured intensity and initial intensity respectively. ` we will again define
as pathlength and we will define a new variable, γ to be the extinction coefficient.
The extinction coefficient is a function of an efficiency factor (Qext ) (4.2) for a medium
containing N particles of radius a per unit volume (4.3). [61]
4
Qext = 2 Re[S(0)]
x
γ = N πa2 Qext
(4.2)
(4.3)
We can now determine the intensity ratio to be defined as a function of ` and γ
(4.4).
I
= e−γ`
I0
(4.4)
These supply a very elegant mathematical description of the extinction coefficient,
but a simpler way to view it is simply the turbidity. Upon close inspection, we see that
extinction indeed arises from turbidity itself. We can define τ to be the turbidity of the
sample measured in Nephelometric Turbidity Units (NTU), which are the standard
turbidity units for measuring the turbidity of drinking water, and rewrite (4.4) as
(4.5). [61]
I
= e−τ `
I0
(4.5)
A region of particular interest is the concept of higher order Tyndall spectra.
La Mer and Johnson have done a good deal of work regarding this effect, and have
classified the order of Tyndall spectra by particle radius. At its smallest, the radius
of E. coli is 0.5 µm and at its largest it is 1 µm. La Mer and Johnson describe the
existence of eight orders of Tyndall scattering for particles of radius 0.5 µm and ten
69
orders for particles of radius 0.6 µm, however the study did not include particles of
larger radius. They observed these higher orders as resulting red bands. [61]
4.2
Engineering Design
The circuitry for the OBD II is identical to that of the OBD I (seen in Figure 3.2),
though doubled to accomodate two sensors. Currently, the OBD II is a benchtop
device (unlike the OBD I field device), and thus lacks the more customized housing
of the OBD I. In order to detect the effects of scattering, two sensors are arranged
at 180◦ from the light source, and another arranged at 90◦ , as seen in Figure 4.1.
The light source in this case is a pen laser of wavelength 523 nm , which allows for
a more precise wavelength and orientation of incident light. Samples tested with the
benchtop model were tested in a dark room to ensure there is no interference from
ambient light.
Sensors
Pen Laser
Sample
Figure 4.1: Schematic for the scattering experiments with the OBD II. The light
source in this case is a laser of wavelength 523 nm, and the detectors are provided
by the same circuit as seen in Chapter 3. Detectors at 180◦ and 90◦ allow for us to
calculate a ratio of scattered to transmitted light.
4.3
Protocol
In order to obtain data with the OBD II, a sample must first be taken. Samples are
prepared by dipping one end of the capillary tube into the desired culture. Capillary
70
action will draw the liquid up into the tube. The top of the tube must then be covered
as the sample is moved to the OBD II and pressed into the clay block so that it stands
erect. The light-blocking box should then be positioned over the device. After power
is applied to the system, the voltage readings for both the transmittance sensor (the
one positioned at 180◦ ) and the scattering sensor (the one positioned at 90◦ ) should
be measured. These values represent the intensity of light both transmitted and
scattered by the particles in the sample.
4.4
Results
As initial testing of the scattering properties of a bacterial culture suspended in
broth, the intensity of light at 180◦ and 90◦ to the incident beam was measured for
each of 20 samples following 18 hours of growth. By comparing these measurements,
we can develop a ratio of scattered light to transmitted light for E. coli in LB.
The mean value for this ratio was 0.460 to three significant figures, with a standard deviation of 0.030 to three significant figures, when observed with the PNZ150
phototransistor and a pen laser of wavelength 523 nm.
4.5
Analysis
Though these measurements are a starting point for further scattering studies,
there was a great deal of error and inconsistency involved. The intensity of the pen
laser is dependent on the charge of its batteries. This charge was low in testing,
resulting in a lower, variable intensity. When new batteries were introduced, the
intensity of the laser saturated the available reading from the OBD II.
Additionally, cuvettes were used as the sample containers. For the purpose of
measuring transmittance for studies invoking the Beer-Lambert law, cuvettes are
ideal, because the calculation involves a concentration as opposed to the behavior of
a single particle in that solution. A large cross-sectional area like that used here results
71
in interference of the different scattering behaviors of various particles. Also, because
E. coli are rod-shaped, they will exhibit different scattering behavior depending on
the angle at which light is incident, causing the combined behavior of many particles
to be increasingly erratic.
It is for this reason that analysis of scattering behavior when observed through
cuvettes is limited to a few applications. By observing the scattering and transmittance intensities at each time interval, we can calculate a more precise value of
light absorption than by measuring transmittance alone. With more knowledge and
measurement of the constants in the Beer-Lambert law, we could find a value for
the concentration of E. coli in the solution. Also, more detailed calculation of the
ratio between transmittance and scattering could prove useful in microbial identification. We hypothesize that different contaminant organisms will exhibit a different
scattering-to-transmittance ratio that falls within a range unique to their classification, for example bacterial vs. fungal. This would provide an additional option to
selective media, as a means by which to determine contaminant source.
4.6
Future Work
These scattering effects must be observed for other species in order to be truly use-
ful. Because Tyndall scattering offers little to no information regarding the shape of
the particle involved, it is the information about the size that presents great promise.
Scattering effects do not require a large number of particles to be observed (as opposed
to change in transmittance), so theoretically, a raw sample, not cultured over a length
of time, will yield information. This information, whether corresponding directly to
a size of particle, or presenting a sort of signature for that species, would suggest
under which specifications a growth-transmittance study should be performed. For
example, a scattering study that yields results suggesting that the particles present
are larger (say, with a cross-sectional area of 50 microns) would lead one to perform a
72
growth-transmittance study for protozoa, not bacteria, using the corresponding media and wavelength appropriate for these organisms. The use of scattering would
allow for quicker, more accurate detection of contamination in drinking water. [40]
In order to decrease the effects of interference between too many particles, a
capillary tube will be used to collect the sample instead of a cuvette. The smaller
cross-sectional area of the capillary tube can accommodate no more than three cells
of E. coli. The capillary tube will be held erect in a block of clay, between the laser
and the sensors. A black, light-absorbent box must be used to block out any ambient
light.
For use of Tyndall scattering, it is important to use non-monochromatic light,
in order to observe the colors of the visible light being scattered. To determine
these colors through methods unbiased by human observation, filters should be used.
Multiple sensors will be put in place, particularly for the detection of red and blue
light, each at 90◦ from the incident beam-path. Each will have a filter that will
only allow transmittance of that sensor’s prescribed wavelength range. The intensity
measured at each of these sensors will provide a quantifiable measurement of which
colors are being scattered most.
It is our hypothesis that this experiment will yield additional information regarding the nature of bacterial growth and contamination in water. Though a benchtop
model is described here, the design can easily be accomodated to fit in a portable
system like the OBD I.
73
CHAPTER 5
CONCLUSION
This thesis has explored the transmittance properties of light through bacteria,
and demonstrated the correlation between transmittance plotted over time and the
growth phases of the bacteria. Many of the challenges and assumptions in testing
bacteria have been addressed, from the question of initial conditions to the various
other unpredictabilities involved in dealing with biological specimens. We have employed the use of bioreactors in which to control culture environment and examine
the effects on initial conditions. We have also prepared stained slides and utilized
software from the NIH to count the number of actual cells present at a given time,
and shown that the cell count corresponds directly to the change in absorption of
light.
Having controlled these various issues, the change in transmittance of light through
bacteria was observed over time using the Genesys 6. This testing has given a baseline
for how light transmittance responds to bacterial growth, and allowed for observation
of the effects of other factors on this bacterial growth curve. The effects on lag time in
the growth curve were confirmed to change with the initial concentration of bacteria
in the sample container, as well as the effects on lag time of the initial growth phase
of the bacterial culture. This has helped us to interpret the results observed from the
OBD.
We have developed a device capable of the timely detection of E. coli and performed initial testing with it. The OBD has demonstrated the ability to detect
bacteria as the Genesys 6 has done, while staying within the confines of both budget
and size. This design was expanded as well to include the capabilities of observing
scattering effects of bacteria. The OBD, however, has many limitations. It is not a
substitute for traditional laboratory tests, as specific properties of the bacteria can
74
be observed directly by these means. The OBD can only detect the presence of organisms, and, to an extent, the identity of these organisms. The selectivity of the
broth media determines that only a certain subset of organisms can thrive off of it
(those whose complete nutrient needs are met by the media), as well as the scattering
properties of the organism. Even amongst single-celled organisms, size varies enough
to change the scattering behavior of incident light. These properties make the OBD
useful for confirming a suspected contamination, or identifying the presence of a contamination, though not the species. Though traditional lab methods take longer,
they yield more useful information than the OBD ever will.
By nature of dealing with biological specimens in this experiment, there are many
errors involved. Through the course of experimentation, many separate cultures of
bacteria have been used. Additionally, the same culture may change over the course of
weeks or months, adapting to its environment. This can cause unpredictable changes
in its behavior. Contamination also has a large effect on results. The materials
used are assumed to be clean, though they are not sterile. Even still, the definition
of clean does not ensure that there are not dormant species within the cuvettes or
other equipment. Contamination can also occur due to errors in following laboratory
technique, and human contribution of organisms from comensual bacteria or illness.
Initially, we were concerned that the temperature of the inoculating loop was killing
the bacteria in the course of inoculation. This concern was abated by standardizing
the cooling time of the inoculating loop to 90 seconds. Inconsistency in broth can
also cause the bacteria to behave differently. In particular, when using the bioreactor,
the culture broth is extremely rich in nutrients while the sample broth is not. This
can cause the bacteria to enter a state of shock and briefly go dormant to conserve
nutrients. Additionally, the concentration of bacteria in the culture can ultimately
affect the growth rate, by affecting the initial concentration of bacteria in the sample.
This is particularly evident when using the bioreactors, as they have a relatively low
75
bacterial concentration compared to a standard culture vial.
Errors in this system are not necessarily linked to the biological aspect. Any
experiment incurs a certain amount of systematic and statistical error. One of particular concern is related to temperature. Bacterial growth is highly dependent on
the temperature of the environment, particularly for E. coli, which is indigenous to
the human gastrointestinal tract (body temperature). E. coli tested in the Genesys
6 is slightly more immune to this effect, as the electronic components of the device
create their own incubator. Samples tested in the OBD, however, sit out in the lab
with no temperature control. This was abated by suspending the samples in a fixedtemperature water-bath. Additionally, because the OBD I samples are changed out
once an hour, there is an increased probability of smudging or contaminating the
sample mid-run. There is also a good deal of time discrepancy caused by systematics.
For example, there is approximately a two minute delay between the inoculation of
each sample; however, there is a five minute delay between the testing of each sample.
This could account for additional horizontal shifts in the various curves.
Both models of the OBD are still in a development stage. Though the OBD I provides the data we had anticipated, there is much room for improvement. Currently,
the sensor for the OBD I is not in a linear regime. That is, a fifty percent decrease in
light intensity does not correspond to a fifty percent decrease in the voltage reading
provided by the OBD. By changing the value of the resistor in line with the phototransistor, we could set the sensor to operating in a linear regime. With a linear
response, we will see a more pronounced decrease in transmittance at the beginning
of the exponential growth phase. The optimal resistance value can be found by using a potentiometer to vary the resistance, and observe the response when a filter of
known transmittance is introduced to the system. This adjustment would optimize
the effectiveness of the OBD I.
The next logical step in this project is to perform field testing. While the OBD
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functions under the stable conditions of a lab, and the fairly predictable growth and
properties of cultured E. coli and clean broth, its real function is out in the field where
conditions are not clean and stable (ambient light and temperature, for example,
are constantly changing). Temperature changes, particularly those occuring between
daytime and nighttime, can influence the growth of the bacterial samples, resulting
in an atypical bacterial growth curve. In practical use, however, this is irrelevant.
Ideally, the OBD will be used to test the initial reading of a sample, and a reading
12 hours later. If the transmittance has decreased by a significant amount, bacteria
should be assumed to be present. In this way, temperature fluctuations do not affect
the key result of a test with the OBD. Attempts at field testing were made during the
OBD’s infancy, but these exact challenges called for a revision on the OBD’s physical
design. No further attempts were made to field test following the OBD’s revision,
as climate changes made the contamination site (the confluence of the South Platte
River and Cherry Creek) more difficult to utilize. The OBD should be tested in a
variety of environments with known and unknown levels of contamination. Should
field testing prove the OBD sufficient under these conditions, efforts can be made to
produce many OBD units and disperse them in parts of the world where laboratory
testing is difficult or even impossible. The cost restrictions imposed on the OBD
make this feasible.
Following the implementation of an E. coli OBD, development of devices for other
organisms should soon follow. E. coli was the most obvious place to start, as it is a
standard in the world of bacteriology, but it is by far not the largest global threat.
Research on other species of bacteria, such as cholera and salmonella, could help
reduce the number of outbreaks in regions with lesser testing equipment. However,
the technology used by the OBD should not be confined to use only in the detection
of bacteria. Parasites must be considered also. Giardia is a protozoan parasite that
has infected 50% of drinking water in the United States, and is considered by the
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Center for Disease Control (CDC) to be serious enough as to be included on the list
of bioterrorism threats. Fungi can be devastating as well. [41] Bret is a yeast used
in making some forms of beer, but is devastating to the wine industry, because not
only does it ruin the product, but it becomes embedded in wine barrels and building
structure. [45] A rapid detection of Bret could save the industry millions of dollars.
The potential applications of this technology are numerous, providing a rapid,
inexpensive means for detecting the presence of biological contamination. Beyond the
interest of scientific advancement is the human interest motivation for this research.
The ability to test for the presence of these lifeforms without advanced laboratory
equipment is the next big step in wiping out diseases that have ceased to exist in the
developed world. While some strive to provide the underprivileged with the means
to fight an ailment, this work provides a means by which to prevent infection from
the beginning. With the advancement of this research, the worldwide detection of
the presence of more and more pathogens will become possible.
78
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83
APPENDIX A - VISUAL BASIC CODE FOR IMPORTING DATA
The following code is used to import individual data files into Excel 2003 in such
a way that rows represent data from individual wavelengths and columns represent
data from individual points in time.
Hit Alt + F11 to open vb editor and go to [Insert] - [Module]. Paste the following
code onto the right pane. Hit Alt + F11 again to return to Excel. Hit Alt + F8,
choose “test” then click on [Run] to import data.
Sub t e s t ( )
Dim myDir As String , f n As String , f f As I n t e g e r , t x t As
String
Dim d e l i m As String , n As Long , b ( ) , f l g As Boolean , x , t As
Integer
myDir = ‘ ‘ c : \ b a c k s l a s h t e s t ’ ’
d e l i m = vbTab
f n = Dir ( myDir \& ‘ ‘ ∗ . dat ’ ’ )
Do While f n <> ‘ ‘ ’ ’
Redim b ( 1 To Rows . Count , 1 To 1 )
f f = FreeFile
Open myDir \& ‘ ‘ \ b a c k s l a s h ’ ’ \& f n For I n p u t As \# f f
Do While Not EOF( f f )
Li ne Input \# f f , t x t
x = S p l i t ( txt , d e l i m )
I f Not f l g Then
n = n + 1 : b(n , 1 ) = fn
End I f
I f UBound( x ) > 0 Then
n = n + 1
b(n , 1 ) = x (1)
End I f
f l g = True
Loop
Close \# f f
f l g = False
t = t + 1
ThisWorkbook . S h e e t s ( 1 ) . C e l l s ( 1 , t ) . R e s i z e ( n ) . Value = b
n = 0
f n = Dir ( )
Loop
End Sub
84