1 July 2010 Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns
Author Affiliations +
J. of Biomedical Optics, 15(4), 045001 (2010). doi:10.1117/1.3463003
Bacterial colonies play an important role in the isolation and identification of bacterial species, and plating on a petri dish is still regarded as the gold standard for confirming the cause of an outbreak situation. A bacterial colony consists of millions of densely packed individual bacteria along with matrices such as extracellular materials. When a laser is directed through a colony, complicated structures encode their characteristic signatures, which results in unique forward scattering patterns. We investigate the connection between the morphological parameters of a bacterial colony and corresponding forward scattering patterns to understand bacterial growth morphology. A colony elevation is modeled with a Gaussian profile, which is defined with two critical parameters: center thickness and diameter. Then, applying the scalar diffraction theory, we compute an amplitude modulation via light attenuation from multiple layers of bacteria while a phase modulation is computed from the colony profile. Computational results indicate that center thickness plays a critical role in the total number of diffraction rings while the magnitude of the slope of a colony determines the maximum diffraction angle. Experimental validation is performed by capturing the scattering patterns, monitoring colony diameters via phase contrast microscope, and acquiring the colony profiles via confocal displacement meter.
Bae, Bai, Aroonnual, Robinson, Bhunia, and Hirleman: Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns



Pathogenic bacteria have caused numerous infectious diseases in host living organisms either by contact or food consumption. Recent outbreaks from numerous food sources such as tomatoes and peanut butter remind us of the vulnerability of the food supply chain to contamination with pathogenic bacteria. The gold standard to detect and isolate a bacterial species is to grow colonies in a petri dish. A bacteria colony is defined as a group of individual bacterium (108) compactly packed together to form an entity different from a surrounding environment. A colony consists of not only the bacteria itself but also extracellular materials, which are secreted as the colony grows. This combined structure can be analyzed to provide distinguishable phenotypic characteristics with proper interrogation methods. For example, differences of color, size, and shape can be utilized as distinguishing traces of a certain bacteria species under certain growth conditions. However, a selective medium that allows only certain types of species to grow and substantially suppresses the growth of others limits applicability to a small number of bacterial species. Therefore, it is advantageous to use nonselective media, which can grow various species on the same media. When different bacteria species are grown in this general media, their phenotypic characteristics tend to be quite similar, which renders morphological differentiation challenging.

Recently, a group of researchers reported a novel method of applying a forward scattering technique to identify a bacterial colony with highly accurate classification and broad applicability. The proposed technology, called BARDOT (bacteria rapid detection using optical scattering technology), provided highly repeatable and distinguishable forward scattering patterns all the way down to strain levels for some of the test cases. The previous research provided the theoretical modeling using scalar diffraction theory,1, 2 experimental verification for time-resolved scattering,3 and application to the actual food samples.4 In life science, automated bacterial colony counting is a topic of interest and various image processing techniques have been suggested for optimal performance considering the optical response characteristics of the colony.5, 6, 7, 8, 9 Several authors have modeled the profile of the bacterial colony elevation as a convex shape with different radii of curvature,1 a thin film with a decreasing tailing edge,5 and a Gaussian profile.6 The results of our most recent study on the smaller bacterial colony profile indicate that a Gaussian profile is quite satisfactory for describing the actual profile of the bacterial colony.10 Therefore, we model a bacterial colony as an optical amplitude/phase modulator with a Gaussian cross section in our study.

Similar ring-type diffraction patterns were observed from previous research involving neumatic liquid crystals (LCs), where the patterns were due to the laser-induced self-phase modulation phenomenon.11, 12, 13, 14, 15, 16, 17, 18, 19, 20 LC molecules were rotated in proportion to the intensity of the incident laser, which results in refractive index variations with a Gaussian profile. When this laser passes through an LC film of several hundred micrometer thickness, the optical wavefront is modulated to create a Gaussian wavefront that is propagated to a screen to create several diffraction rings. Some researchers investigated the field curvature effect on the diffraction rings,16, 17 while others worked on the relationship between Fredericksz transitions and primary/secondary diffraction rings.11, 12, 13, 14, 20

Although the fundamental idea was revealed, more quantitative analysis is required to understand the morphological parameters of the bacterial colony and their correlation to the forward scattering pattern. Since the wavefront after the bacterial colony combines the incoming wavefront with the depth-averaged effect of a phase modulator, we designate some important morphological parameters and analyze their correlations to the far-field scattering patterns. Section 2 introduces theoretical modeling of light colony interactions and experimental procedures. Section 3 provides the simulation results when diameter and colony height are varied independently as well as experimental measurements of scattering patterns, colony diameters, and profiles. Section 4 discusses the validation and correlation between the scattering pattern and colony morphology.


Materials and Methods


Modeling Light-Colony Interactions


Derivation of diffraction modeling

We define the coordinate systems for the source, the colony, and the imaging coordinate as xs,ys ; xa,ya ; and xi,yi , respectively, as shown in Fig. 1 . Assuming a TEM00 mode of laser beam centered on the z axis, electric field on the aperture plane E1 can be expressed as1


where E0 is the on-axis field strength; three terms account for variations of amplitude of field, longitudinal phase, and radial phase, respectively; xa and ya are the coordinates in the aperture; and w(z1) and R(z1) are the beam waist and radius of the wavefront in the colony plane (z=z1) , which is defined by


where z0 is defined as the z location where 1e2 radius has expanded to 2 times of beam waist w0 . Then, we apply the Huygens-Fresnel principle in rectangular coordinates. Using Eq. 1, the electric field E2 at the imaging plane can be expressed as


where xi and yi are points on the image plane, denotes the colony surface, t(xa,ya) is the 2-D transmission coefficient, ϕ(xa,ya) is the 2-D phase modulation factor, rai is the distance from the aperture plane to the image plane, λ is the wavelength, and n2 and Δ2 represent the refractive index and thickness of Brain heart infusion (BHI) agar, respectively. Based on the scalar diffraction theory, the diffracted field on the image coordinates can be formulated via Fresnel approximation as


where fx and fy are defined as fx=xiλz2 and fy=yiλz2 . When Eq. 2 is rearranged, the constants and independent variables are consolidated as


where k is the wave number. Detailed assumptions and the derivation are shown elsewhere.1

Fig. 1

Experimental setup and definition of coordinate system: (a) forward scatterometer with laser diode (635nm) ; (b) confocal displacement measurement (CDM) system with 2-D rastering via XY stage system; and (c) coordinate system for source, aperture, and image coordinates and the bacterial colony morphology fitted with Gaussian shape. Here H0 is the center thickness, wb is the 1e radius, n1 and n2 are the refractive indices for colony and agar, and Δ2 represents the agar thickness.



Amplitude Component

Our scanning electron microscopy (SEM) observation revealed both microscopic and macroscopic details of how millions of individual bacteria were packed to form a colony.2 The results indicated that the individual bacterium tended to form a layered structure rather than a distribution of randomly oriented bacterium, which provided for us a ground on which to model the light-bacteria interactions using a layer model as a first-order approximation. We divided the bacterial colony profile as multiple layers of bacteria with a thickness of Δμm , considering the physical dimensions of the individual bacteria and extracellular materials. Although the reduction in transmitted light can originate from both reflections and absorptions, we assume the normal incident reflection is the major contributor of field loss to our layer model based on the interrogating wavelength and refractive index of a bacterium. With respect to this assumption, Fig. 2 shows the field attenuated for the k ’th layer of bacterium, which can be modeled as


where rk is the reflection coefficient for the k ’th layer and is assumed to be constant for all the layers. Considering all the multiple layers in the colony, the overall 2-D transmission coefficient is defined as


where the exponent l is defined with the colony profile H(xa,ya) , and the layer thickness Δ as


The different reflection coefficients rk , r1 , and r2 are defined since rk represents the interbacterium reflection, while r1 and r2 model the reflection between air-bacterium and bacterium-agar. This can be modeled as


where n0 , n1 , n2 , and nec are the refractive indices for air, bacteria, agar, and extracellular materials. Since our scalar diffraction theory does not consider the polarization effect, we simply model the attenuation from the thickness of the colony and take the absolute value of the reflection coefficients.

Fig. 2

Schematic diagram for amplitude modulation model via multiple layers, where the rectangular box and the shaded box represent bacteria and extracellular material: Ek denotes the electric field incident on the kth layer of bacteria exiting as Ek+1 . The loss is modeled via the reflection coefficient rk with the layer ( bacterium+extracellular material) thickness as Δ .



Phase Component

The phase component of the diffraction integral is governed by the optical path length term


where H0 and H(xa,ya) are the center thickness and a profile of the bacterial colony. In addition, we define the effective diameter D of the Gaussian profile colony as


where wb is the 1e radius of a Gaussian profile, and F is the factor (1.6) multiplied to render a 1e3 radius. Equation 10 can be further rearranged when we assume the Gaussian profile of the bacterial colony as21


Therefore, the overall phase modulation ϕ(xa,ya) is contributed from the radial phase and the bacterial phase modulation, which is formulated as




where C2 is a constant C1 multiplied by exp(ikH0) .

Then, the intensity of the electric field (power) is computed via


where c is the speed of light in a vacuum, and ε is the permittivity. We then computed the diffraction pattern at the far field according to the parameters defined in Table 1 .

Table 1

Common simulation parameters.

Source Z1 100mm
w 0.5mm
λ 0.635 μm
P 1mW
Colony Z2 30mm
Δ 2 μm
n1 1.38
n2 1.33
nec 1.35
Image N 1024×1024




Sample Preparation

Salmonella montevideo cultures were selected to be inoculated in BHI broth and incubated at 37°C overnight. The culture was then diluted (10-fold dilution) in phosphate buffer saline (PBS). We spread 100μl of the appropriate concentration thoroughly on the BHI agar and cultivated at 37°C in the incubator. At the specified growth time, the plate was taken to BARDOT for measurement, which were 6.5h (diameter around 90μm ), 7h (120μm) , 8h (160μm) , and 9h (300μm) .


Forward Scatterometer

A forward scatterometer [Fig. 1] was built using (1) a 635-nm diode laser (Coherent 0221-698-01 REV B, California) with 1mW of power and a 1-mm diameter; (2) a single biconvex lens (BK7 H32-717, Edmund Optics, New Jersey) with a focal length of 150mm ; (3) a monochromatic IEEE1394 CMOS array (PixeLINK, Ottawa, Ontario, Canada) with 1280×1024 resolution and 6.7×6.7μm2 for each pixel, used as the imaging sensor; and (4) an XY linear translation stage (two of 850-HS, Newport, New York) and a multiaxis closed-loop controller (ESP300, Newport, New York) to move the plate in the x-y plane to align the laser with the center of each selected bacterial colony. The biconvex lens was used to control the beam diameter similar to the bacterial colony diameter via controlling the beam waist within the Raleigh range of the focused Gaussian beam. The focusing beam diameter d is computed via22


where ω02 is the beam waist of the focusing beam, f is the focal length of the lens, λ is the wavelength of the laser beam, and ω01 is the radius of the incoming Gaussian beam. The depth of focus, where the bacterial colony should be located, is two times the Raleigh range zR of the focused laser beam;


The calculated depth of focus for the current optical system configuration was around 36mm .


Phase Contrast Microscopy and Confocal Displacement Meter

The diameters of the bacterial colonies were measured by a phase contrast microscope (Leica Microsystems, Illinois). The objective was ×10 and the phase contrast image mode was used to image and measure the diameter of the colonies. Even though the phase contrast microscope provided qualitative phase information, it did not correlate the image with the colony profile quantitatively. Therefore, a confocal displacement meter (Keyence LT9010, New Jersey) was integrated with the X-Y motorized stage to obtain the height profile of each individual bacterial colony [Fig. 1]. We tested conventional laser triangular sensors to measure the profile, but the complexity of the sample (diffuse reflectivity, transparency, and multiple surface reflections) resulted in an unreliable shape profile (data not shown). The CDM instrument implements a “confocal principle” in the laser triangulation technique to deal with these issues and provided highly reliable surface scanning method by using a 670-nm laser light source with the spot size of 2μm and the vertical reference distance of 6mm and a scanning resolution up to23 0.01μm .



We theoretically investigated the effect of morphological variation of the bacterial colonies to diffraction patterns on the imaging plane and measured the actual bacterial colony morphologies via microscope and profilometer. To decouple the effect of morphological parameters, we computed the colony center thickness effect and the colony diameter effect separately.


Height Variation versus Diffraction Pattern

The diameter of the bacterial colony D was kept constant at 1.024mm and the center thickness H0 of the bacterial colony was modified from 10to110μm in 5-μm steps. The maximum thickness was adapted from the previously published experimental results from the confocal microscopy.1 The corresponding profile was modified with Eqs. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and their respective diffraction patterns were displayed for radii of 0to5mm due to their circularly symmetric patterns. Since the dynamic range of the diffraction pattern varied significantly, we plotted the logarithm of the intensity (in milliwatts per square millimeters). Figure 3 shows the result for H0 for 30, 50, 70, and 90μm . The computational results indicated the following two observations:

  • 1. When the H0 value increased for a constant diameter, the maximum diffraction angle increased.

  • 2. When the H0 value increased for a constant diameter, the total number of rings also increased.

For example, for H0=30μm , the maximum half diffraction angle was 2.13deg , while the H0=90μm resulted in 5.71deg (with an intensity cutoff value of 5×105mWmm2 ). Meanwhile, the total numbers of rings (peaks) were counted as 28 and 65, respectively. Note also that the intensity level of the region between Xi=0 and Xi=0.5 monotonically decreased as H0 increased, while the location of the first peak did not vary significantly.


Diameter Variation versus Diffraction Pattern

In this study, we kept the H0 value of the bacterial colony as 50μm and investigated the effect of the D variations when it was varied from 0.384to1.05mm in 10-μm steps. The corresponding profile was also modified with Eqs. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and their respective diffraction patterns are displayed in Fig. 4 for the colony diameters of 0.384, 0.48, 0.704, and 0.96mm . The diffraction calculation provided the following understandings:

  • 1. When the center thickness was kept constant, the maximum diffraction angle was inversely proportional to the colony diameter.

  • 2. When the center thickness was kept constant, the total number of rings was also kept constant irrespective of the colony diameter variation.

For example, the maximum half diffraction for a diameter of 0.384mm was 7.676deg , while for a diameter of 0.96mm , it was only 3.676deg . The total number of rings did not change much once the H0 was kept constant. Compared to the results of Sec. 3.1, the first peak location translated outward and peak value decreased as the D increased.

Fig. 3

Forward scattering pattern for center thickness (H0) variation when diameter is kept constant: H0=(a) 0.03, (b) 0.05, (c) 0.07, and (d) 0.09mm . The number of peaks and maximum scattering angle increases as H0 increases.


Fig. 4

Forward scattering pattern for colony diameter variation when H0 is kept constant and D is (a) 0.384, (b) 0.48, (c) 0.704, and (d) 0.96mm . Note that the maximum scattering angle decreases as D increases.



Correlation between Morphology and Diffraction Patterns

To understand the correlation between the colony morphology and the diffraction patterns, we repeated the calculation for different D (Sec. 3.1) and different H0 (Sec. 3.2). Figure 5 shows the relationship between the H0 and the total number of peaks (diffraction rings) when D was varied for D=0.768 , 0.896, and 1.024mm . The result clearly indicated that the H0 value was the major factor contributing to the total number of diffraction rings observed at the imaging plane irrespective of the D value. Figure 5 indicates the relationship between the colony diameter and the total number of peaks, which remained relatively constant across the diameter but increased as the H0 values increased, which confirmed the result from Fig. 5.

Fig. 5

Correlation of the number of rings when (a) colony thickness and (b) colony diameter were varied.


Figure 6 displays the phase [Fig. 6] and the first derivative of the phase [Fig. 6] for a diameter of 1.024mm and H0 values of 30, 50, and 70μm . Since the center thicknesses are different, the phase lag from 70μm is approximately 2.5 times as great as that from a 30-μm thickness, as shown in Fig. 6. The derivative of the phase in Fig. 6 for the three thicknesses shows the minima at the Xi=0.23mm location since this is the deflection point of the phase in Fig. 6. The magnitude of the derivative of the wavefront for 70μm shows the largest value, which corresponded to the half angle of 4.42deg , while 30μm shows the smallest value of 2.13deg .

Fig. 6

(a) Phase and (b) derivative of phase of the wavefront after the incident beam passes the bacterial colony when the center thickness is 0.03, 0.05, and 0.07mm .



Scatterometer Measurement

Using the forward scatterometer, a series of scatterogram was aquired as the bacteria grew. The measurement was aquired as the incident laser beam waist was varied close to the colony diameter. The top row of Fig. 7 shows scatterograms from approximately 89, 119, and 144μm with Z2 set around 8mm . Meanwhile, the bottom row displays the result for the colony diameters of approximately 193, 253, and 302μm . For these sets, Z2 was set to approximately 12mm such that larger area of sensor was utilized to the resolve the multiple number of rings. The result clearly indicates the increase of the number of rings as the colony diameters increased, which were measured from phase contrast microscope.

Fig. 7

Recorded forward scattering patterns against the colony diameter for Salmonella monteviedo. The diameter was varied from 89to302μm .



Colony Profile Measurement

To accurately assess the H0 , CDM was used to acqure the profile, which was operated on the profile mode with 220 data points with a 5-μm interval23 in Y direction. Figure 8 displays the colony profile measurement and corresponding diameter measured from the phase contrast microscope. Figure 8 consolidates the 1-D crossectional profile across the H0 location for seven different colony diameters. The result indicates the following two obervations: (1) proporationality between D and H0 , which is approximately 10:1 ratio, and (2) the tailing edge profile was clearly captured for the most of the colony profiless, which supports our assumption on the Gaussian colony profile. Figures 8 and 8 show the phase contrast image and corresponding 2-D profiles from CDM in peudocolors.

Fig. 8

Bacterial colony profile measurements: (a) the cross-sectional profile (vertical axis not to scale) for various size of bacterial colonies (109to450μm) , (b) the corresponding phase contrast images, and (c) the 2-D profile from CDM. (Note: second figure of (b) shows 187μm horizontal diameter but the vertical diameter is measured to be 204μm ).


To provide quantitative validations between theory and experiment, correlations between three different approaches were investigated: estimation from nematic crystal research,12, 15 estimation from the proposed model, and the experimental result (Fig. 9 ). The red square indicates the estimated ring counts [Fig. 9] and maximum diffraction angle [Fig. 9] from the H0 measurement of the CDM, while the green triangle represents the those from the experiment (Fig. 7). Finally, the blue circle shows the estimation from the proposed diffraction model. In the experiment, the beam diameter was controlled to be similar to the colony diameter therefore Eq. 12 was applied to convert from H0 to phase difference. To compare with our previous modeling effort, we fitted the measured colony profile with three different models and estimated their performance, which were Gaussian profile fit (G-fit), two-radius fit (TR-fit), and one-radius fit (OR-fit). The performance was measured by calculating the sum of the squared difference from the proposed model and the 1-D CDM profile across the peak location. Table 2 shows the results and indicates that the G-fit and TR-fit are comparable to each other and clearly capture the colony profile, while the OR-fit (single spherical profile) does not reflect the actual colony profile.

Fig. 9

Comparison of model and experimental result: (a) comparisons of the ring count versus colony diameter and (b) maximum half cone angle versus colony diameter. Red squares in (a) denote the estimated ring count from the H0 measured from CDM using Eq. 16, while in (b) the derivative of phase is calculated from the profile and Eq. 17 was used for estimation. The green triangle represents the actual ring count from scatterograms (Fig. 7), while the blue circle shows the estimated ring count from the proposed model. (Color online only.)


Table 2

Comparison of the Gaussian (G), two-radius (TR), and one-radius (OR) fits to the measured colony profiles.

Diameter (μm) Height (μm) Σ(PM−Fit)2




Maximum Diffraction Angle

According to the results in Sec. 3, the maximum diffraction angle and the total number of rings are closely related to the bacterial colony morphology. These two parameters are important since they are directly measurable from the experiment and thus provide the phenotypic characteristics of the bacterial colonies. First, the maximum half diffraction angle is the result of the interference of two or more wavevectors pointing at the same direction. According to Eq. 13, the Fresnel diffraction pattern is the fast Fourier transform (FFT) of the input wavefront right after the wave passes through the bacterial colony. This accumulates all the light-matter interactions such as absorption, transmission, and reflection, which results in an overall phase modulation. Since the bacterial colony is modeled as a Gaussian profile, the wavefront emerging from the bacterial colony also resembles this Gaussian profile. Coupled with the radial phase effect, the overall wavefront is determined by Eq. 14. Therefore, the maximum diffraction angle is governed by the direction of the wave vector, which is the normal vector to the emerging wavefront. This implies that the magnitude of the slope of the wavefront is the key factor to determining the maximum diffraction angle, a theory that was also suggested by other researchers using nematic crystals.12, 13, 14, 15, 16 Previous research from nematic LCs reported the equation for estimating the maximum half cone angle θ2max as,


where k is the wave number.20 Figure 6 provides the derivative of the phase profile based on model profile of Fig. 6. The maximum half cone angle of the total number of rings (when D=1.024 ) for the series of H0 for 0.03, 0.05, and 0.07mm as 2.13, 3.21, and 4.42, which agrees with 1.91, 3.08, and 4.25 from Eq. 18.


Number of Diffraction Rings

The total number of rings is another parameter closely related to the colony morphology. As shown in Fig. 5, the diffraction rings are dependent on the center thickness and are not dependent on the the colony diameter. This is because the optical path difference (OPD) increases as the H0 is increased. To help clarify this argument, we provide a schematic diagram in Fig. 10 , which represents the wavefront from the bacterial colony. Since the wavefront captures the colony profile, the emerging wavefront is also a Gaussian profile. Since the diffraction peaks and valleys are generated from the interference of two or more wave vectors, we can relate the phase lag (ΔΦ) to the number of rings. Since the wave vectors k1 and k2 will interfere in at the far field, their OPD determines whether we observe a peak or valley, depending on constructive and destructive interference. As shown in Fig. 10, this OPD is determined from δ1 and δ2 , which decreases when the bacterial colony profile gets flatter ( H0 is decreased). Therefore, we can infer that the increase of H0 results in a larger OPD and creates a larger number of diffraction rings. This conclusion coincides with conclusions drawn from nematic crystal research,12, 15 where the number of rings Nring was estimated as


This fact is confirmed when we compare the estimation of Eq. 19 with our result in Sec. 3. Figure 5 provides the mean value of the total number of rings for the series of H0 for 0.03, 0.05, and 0.07mm as 29.0, 39.8, and 50.9, which is close to 28, 40, and 52 when estimated from Eq. 19 and Fig. 6. For example, when H0 is 0.03mm , ΔΦ is computed to be 186 (by substracting the phase at Xi=0.8 and Xi=0 since the Gaussian incident beam diameter is 1.6mm for wb=0.5 ), while Eq. 19 gives approximately 182.

Fig. 10

Schematic diagram for understanding the interference pattern generated by the emerging wavefront: ΔΦ represents the phase lag from the colony center thickness H0 , k1 and k2 represent the wave vectors, and δ1 and δ2 represent the OPD difference of these two wave vectors.



Experimental Verification

The proposed Guassian colony profile model was compared with experiment and other models of Eqs. 18, 19. Figure 9 shows the comparision of the number of rings versus the colony diameter up to the 350-μm range, where the predicted and measured ring counts agree well. Some differences are still observed, though especially for the smaller colonies where the bright central spot lowers the accuracy in the experimental ring counts due to the limited dynamic ranges of the CMOS sensor. Figure 9 displays a similar comparison for a maximum diffraction angle. Both the proposed model and Eq. 19 predict similar results and the experimental measurement matched within a reasonable range of angles. A possible error source could arise from the difference between the Gaussian fit and the real colony and the position measurement of Z2 , since the scattering angle masurement is sensitive to the colony-image plane distance. The result from Table 2 clearly shows the possibility of using a Gaussian profile to analyze the optical characteristics of the colony. Although the TR-fit model showed comparable accuracy with the G-fit model (except for diameters larger than 350μm ), the proposed model requires only the H0 and D values, while the TR-fit requires iterative optimizations of three different spherical profiles to accurately model the profile. Thus, we can directly estimate the morphological characteristics of the bacterial colony based on observation of the diffraction pattern. This explanation can be applied to the previous results from the time-resolved scattering pattern analysis.3 Our theory argues that vertical growth velocity is approximately equal to lateral growth velocity in the lag and logarithmic bacterial growth phase, which results in a wide diffraction pattern since the colony is narrow and tall. On the other hand, once the colony reaches the stationary point, vertical growth is slowed compared to lateral growth; hence, the diffraction pattern tends to shrink since the colony shape becomes wider and shorter.



Scalar diffraction theory was applied to model a bacterial colony as optical amplitude and phase modulators. Assuming a Gaussian profile for the colony shape, two main morphological characteristics of center thickness and colony diameter were calculated to correlate with the corresponding diffraction pattern. The results indicated that the maximum diffraction angle is dependent on the magnitude of the slope of the wavefront emerging from the bacterial colony, while the total number of rings is dependent on the center thickness of the bacterial colony. These results verify a fast method of understanding the bacterial colony morphology, which can be related to the understanding and classification of bacterial species.


This research was supported through a cooperative agreement with the Agricultural Research Service of the U.S. Department of Agriculture Project No. 1935-42000-035 and the Center for Food Safety and Engineering at Purdue University.



E. Bae, P. P. Banada, K. Huff, A. K. Bhunia, J. P. Robinson, and E. D. Hirleman, “Biophysical modeling of forward scattering from bacterial colonies using scalar diffraction theory,” Appl. Opt.0003-6935 46(17), 3639–3648 (2007).10.1364/AO.46.003639Google Scholar


P. P. Banada, S. L. Guo, B. Bayraktar, E. Bae, B. Rajwa, J. P. Robinson, E. D. Hirleman, and A. K. Bhunia, “Optical forward-scattering for detection of Listeria monocytogenes and other Listeria species,” Biosens. Bioelectron.0956-5663 22(8), 1664–1671 (2007).10.1016/j.bios.2006.07.028Google Scholar


E. Bae, P. P. Banada, K. Huff, A. K. Bhunia, J. P. Robinson, and E. D. Hirleman, “Analysis of time-resolved scattering from macroscale bacterial colonies,” J. Biomed. Opt.1083-3668 13(1), 014010 (2008).10.1117/1.2830655Google Scholar


P. P. Banada, K. Huff, E. Bae, B. Rajwa, A. Aroonnual, B. Bayraktar, A. Adil, J. P. Robinson, E. D. Hirleman, and A. K. Bhunia, “Label-free detection of multiple bacterial pathogens using light-scattering sensor,” Biosens. Bioelectron.0956-5663 24(6), 1685–1692 (2009).10.1016/j.bios.2008.08.053Google Scholar


M. A. Bees, P. Andresen, E. Mosekilde, and M. Givskov, “The interaction of thin-film flow, bacterial swarming and cell differentiation in colonies of Serratia liquefaciens,” J. Math. Biol.0303-6812 40(1), 27–63 (2000).10.1007/s002850050004Google Scholar


R. Bernard, M. Kanduser, and F. Pernus, “Model-based automated detection of mammalian cell colonies,” Phys. Med. Biol.0031-9155 46(11), 3061–3072 (2001).10.1088/0031-9155/46/11/320Google Scholar


G. Corkidi, R. Diaz-Uribe, J. L. Folch-Mallol, and J. Nieto-Sotelo, “COVASIAM: an image analysis method that allows detection of confluent microbial colonies and colonies of various sizes for automated counting,” Appl. Environ. Microbiol.0099-2240 64(4), 1400–1404 (1998).Google Scholar


P. R. Barber, B. Vojnovic, J. Kelly, C. R. Mayes, P. Boulton, M. Woodcock, and M. C. Joiner, “Automated counting of mammalian cell colonies,” Phys. Med. Biol.0031-9155 46(1), 63–76 (2001).10.1088/0031-9155/46/1/305Google Scholar


J. Marotz, C. Lubbert, and W. Eisenbeiss, “Effective object recognition for automated counting of colonies in Petri dishes (automated colony counting),” Comput. Methods Programs Biomed.0169-2607 66(2–3), 183–198 (2001).10.1016/S0169-2607(00)00128-0Google Scholar


N. Bai, E. Bae, A. Aroonnual, A. K. Bhunia, J. P. Robinson, and E. D. Hirleman, “Development of a real-time system of monitoring bacterial colony growth and registering the forward-scattering pattern,” in Sensing for Agriculture and Food Quality and Safety, Proc. SPIE0277-786X 7315, 73150Z–73158 (2009).Google Scholar


F. Bloisi, L. Vicari, F. Simoni, G. Cipparrone, and C. Umeton, “Self-phase modulation in nematic liquid-crystal films—detailed measurments and theoretical calculations,” J. Opt. Soc. Am. A0740-3232 5(12), 2462–2466 (1988).10.1364/JOSAB.5.002462Google Scholar


S. H. Chen, J. Y. Fan, and J. J. Wu, “Novel diffraction phenomenon with the first order Fredericksz transition in a nematic liquid crystal film,” J. Appl. Phys.0021-8979 83(3), 1337–1340 (1998).10.1063/1.366835Google Scholar


S. D. Durbin, S. M. Arakelian, and Y. R. Shen, “Laser-induced diffraction rings from a nematic-liquid-crystal film,” Opt. Lett.0146-9592 6(9), 411–413 (1981).Google Scholar


I. C. Khoo, J. Y. Hou, T. H. Liu, P. Y. Yan, R. R. Michael, and G. M. Finn, “Transverse self-phase modulation and bistability in the transmission of a laser-beam through a nonlinear thin-film,” J. Opt. Soc. Am. A0740-3232 4(6), 886–891 (1987).10.1364/JOSAB.4.000886Google Scholar


K. Ogusu, Y. Kohtani, and H. Shao, “Laser-induced diffraction rings from an absorbing solution,” Opt. Rev.1340-6000 3(4), 232–234 (1996).10.1007/s10043-996-0232-1Google Scholar


R. P. Pan, S. M. Chen, and C. L. Pan, “A quantitative study of the far-field laser-induced ring pattern from nematic liquid-crystal films,” Chin. J. Physiol.0304-4920 30(4), 457–466 (1992).Google Scholar


E. Santamato and Y. R. Shen, “Field-curvature effect on the diffraction ring pattern of a laser-beam dressed by spatial self-phase modulation in a nematic film,” Opt. Lett.0146-9592 9(12), 564–566 (1984).10.1364/OL.9.000564Google Scholar


M. Sheikbahae, A. A. Said, T. H. Wei, D. J. Hagan, and E. W. Vanstryland, “Sensitive measurement of optical nonlinearities using a single beam,” IEEE J. Quantum Electron.0018-9197 26(4), 760–769 (1990).10.1109/3.53394Google Scholar


A. Shevchenko, S. C. Buchter, N. V. Tabiryan, and M. Kaivola, “Creation of a hollow laser beam using self-phase modulation in a nematic liquid crystal,” Opt. Commun.0030-4018 232, 77–82 (2004).10.1016/j.optcom.2004.01.010Google Scholar


J. J. Wu, S. H. Chen, J. Y. Fan, and G. S. Ong, “Propagation of a Gaussian-profile laser-beam in nematic liquid-crystals and the structure of its nonlinear diffraction rings,” J. Opt. Soc. Am. A0740-3232 7(6), 1147–1157 (1990).10.1364/JOSAB.7.001147Google Scholar


J. W. Goodman, Introduction to Fouirier Optics, McGraw-Hill, New York (1996).Google Scholar


A. E. Siegman, Lasers, University Science Books, Sausalito (1986).Google Scholar


Keyence-Corporation, “Surface scanning laser confocal displacement meter LT-9001 series,” Keyence Corporation, Tokyo (2006).Google Scholar

Euiwon Bae, Nan Bai, Amornrat Aroonnual, J. Paul Robinson, Arun K. Bhunia, E. Daniel Hirleman, "Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns," Journal of Biomedical Optics 15(4), 045001 (1 July 2010). http://dx.doi.org/10.1117/1.3463003



Light scattering



Phase contrast

Code division multiplexing

Back to Top