|
1.IntroductionA laser-based light-scatter technique was reported for label-free phenotypic screening of bacterial colonies1 and has shown promising classification results for several bacteria, including Salmonella,2 Vibrio,3 Listeria,4 Campylobacter,5 and Escherichia coli.6 As the need for rapid identification and classification of microbial organisms increases in various fields such as food security, clinical studies, and biosurveillance, label-free optical diagnostics has been studied by several research groups owing to several merits of this technology.7–9 To provide more robust and accurate screening of these patterns, machine-learning techniques such as support vector machines (SVMs) were applied to features extracted from scatter patterns.10 In addition, the optical origin of the patterns was investigated based on elastic-light-scatter phenomena for a single wavelength,11 multiwavelengths,12 and a speckle analysis.13 One drawback of the current system is that the scattered signal must transmit through the colony and growth medium. Therefore, the current ELS system has shown limited performance success for bacterial colonies with high opacity or highly reflective growth media, such as yeast colonies or blood agar medium, respectively. In particular, blood agar medium is widely used in clinical and veterinary medicine to isolate specific bacteria that generate hemolysis.14,15 Media or colonies with high opaqueness block the incoming light and do not allow photons to penetrate; as a consequence, the forward-scatter patterns cannot be observed by the sensor. Therefore, to expand the applicability of the light-scattering technology into clinical and veterinary medicine, we have developed a reflection-type of scatterometer that can generate patterns in such circumstances. This technique involves reflected elastic scattering, which is designed to capture the patterns above the colonies. The reflectance-based method utilizes a green laser diode (532 nm), an R50:T50 beam-splitter, a large nonreflective screen placed directly above the colonies, and a high-resolution camera to capture patterns on the screen. Owing to the larger scattering angle in the backward direction, the new system was designed to effectively capture the larger scatter patterns. A prototype system was tested with four different bacterial genera (E. coli K12, Listeria innocua F4244, Salmonella typhimurium, and Staphylococcus aureus) grown on a blood agar plate with 5% sheep blood. Despite the opacity of the medium, the unique patterns of each genus can be observed, analyzed, and classified. 2.Materials and Methods2.1.Design of Reflective ScatterometerFigure 1(a) shows a schematic diagram of a newly designed reflective BARDOT system. A high-resolution digital camera (D810, Nikon Corp., Tokyo, Japan) with and an AF-S NIKKOR 85 mm lens (Nikon Corp., Tokyo, Japan) is positioned over the Petri dish at a distance of 490 mm (19.5′′), measured from the top surface of the Petri dish to the surface of the camera image plane [Fig. 1(b)]. Exposure program, ISO, F-number, exposure bias value, and focal length of the camera are fixed in manual mode, 800, f 8, 0.00 eV, and 85 mm, respectively. A black curtain covers the instrument completely to block unexpected environment light, and a 2-s shutter speed is used for the experiments. The shutter time is selected based on scattering pattern intensity, tested over a variety of shutter time options from 0.3 s to 3 s. A collimated, circular 5-mW beam from a 532-nm laser diode module (GLM-R1IB-05 Innovam Lasers, Montreal, Canada) acts as the light source [Fig. 1(c)]. As shown in Fig. 1(a), an R50:T50 plate beam splitter (BS) (BTF-VIS-30-SQW-5001M-C IDEX Optics & Photonics Marketplace, Albuquerque, New Mexico) is located over the agar plate at a 45-deg angle to deflect the incoming light to the sample. To avoid interference from an edge of the BS for the pattern, a rectangular BS is selected. The light from the source is reflected and backscattered by the bacterial colony and forms a pattern on a screen located in between the BS and the camera; this pattern is captured by the camera as an RAW format for analysis. As with the forward scatterometer, direct scatter-pattern capture without passing through any optical component guarantees the best quality; however, the screen is adapted owing to the larger pattern size produced by a larger diffraction angle (scattering angle) and the spatial limitation of the commercial large-area CMOS sensors. The screen was a diffuser film () mounted in a monitor. Since the quality of the screen-captured pattern from the reflection-type instrument is also suitable for the custom-built pattern-analyzer program, we utilized the screen rather than installing an expensive wide-imaging sensor. The screen is located 140 mm () above the sample to ensure the high spatial resolution and quality of the scatter patterns. 2.2.Sample PreparationE. coli K12, L. innocua, S. typhimurium, and S. aureus were selected as model organisms. All genera were prepared by subculturing from frozen stocks stored in a freezer that was kept at . From the frozen stock, each genus was cultured on trypticase soy agar (Bacto™, BD Diagnostics, Sparks, Maryland), and incubated at 37°C until colonies were able to be visibly located. One of the colonies was randomly selected to be subcultured on blood agar. The selected culture was then diluted serially two to three times with for each dilution. From the final diluted tube, was surface plated on a trypticase soy agar plate with 5% sheep blood (), (TSA II™, BD Diagnostics, Sparks, Maryland) to obtain bacterial colony counts of 50 to . The plates were incubated at 37°C until the colonies reached a diameter of 700 to . Colony diameter and elevation were measured using both a confocal microscope equipped with a Leica DFC310 FX CCD camera and Leica Application Suite V4.20 build 607 (all from Leica Microsystems, Bannockburn, Illinois) using a objective, and a custom-built colony morphology analyzer.16 2.3.Optical ModelingTo predict the pattern size, a geometric optics was utilized as a first-order approach. It is assumed that only a reflection by the curvature of the colony is deflecting the incoming light.17 Figure 2 shows the coordinate system of the analysis. Subscripts s, a, and i denote a source, an aperture, and an image plane, respectively. A bacterial colony is considered to be an optical reflector like a convex mirror. The curvature of the colony is modeled as a Gaussian profile with tailing edge as where is defined as the center height of the colony. For the tailing-edge representation, an effective radius of the colony, , is defined as where is the measured diameter of the colony, and is a factor (1.6) in the divisor to render a radius. The incoming light is assumed to be collimated light that covers the whole area of the colony. When one of the incoming photons reaches the point located on the curvature of the colony, it will be reflected back to the image plane. The normal line in Fig. 2 is derived as where is defined as the slope of the normal line. A point is the bisector between the starting point and is on the reflected photon trajectory. The point , which lies on the normal line, can be expressed in terms of and coordinates of and starting point as shown in Eq. (4). The slope between the starting point and is perpendicular to that of the normal line. This can be expressed as After substituting Eq. (4) in Eq. (3) and organizing Eq. (5), the derived equation as a matrix form with respect to is obtained. , coordinate point of that is on the reflected photon trajectory, is computed from Eq. (6). Since and are on the reflected line, the line equation for the reflected line is derived as Since the image plane where the reflected pattern is captured is located at , the half-scatter angle is computed by Eq. (7). The light source is assumed to be a round, collimated 2 mm laser beam, which illuminates the whole colony perpendicularly and does not block any light reflected from the colony. The distance from the colony to the image plane where the screen is located is set as 127 mm. The angle between the incident light (perpendicular to the ground) and reflected light at the most outer bound is considered the maximum half diffraction angle, which determines the pattern size. The aspect ratio (a colony elevation and diameter ratio), which plays a major role to determine the slope of the colony curvature, is fixed as for the simulation of Figs. 5(a) and 5(b).11,18 The elevation and diameter of the colony are set as 100 and , respectively. In studying the relationship between aspect ratio and diffraction angle, the aspect ratio varied from 0.015 to 1.5.2.4.Time-Lapse Backward-Scatter PatternsAll four bacteria samples were incubated (I2400 Incubator Shaker, New Brunswick Scientific, Edison, New Jersey) and colonies per plate were measured. The first measurement was taken when E. coli, S. typhimurium, L. innocua, and S. aureus were incubated for 6, 6, 11, and 8 h, respectively. The time-dependent data were collected by the following steps every hour for 5 h. First, utilizing the integrated colony morphology analyzer (ICMA),16 colony diameter and elevation were accurately measured for each colony. Second, backward-scatter patterns were captured using the reflection-type instrument. The thickness of the agar for each plate was maintained at . 2.5.Image Processing and AnalysisSince the shutter speed is fixed at 2 s, each pattern was captured, cropped to , and converted to gray-scale using the “Luminance” algorithm.19 The captured scatter patterns were analyzed by Baclan™, a stand-alone quantitative image-processing software that utilizes different types of features and provides a supervised learning-based classification of the captured pattern.10,20,21 After extracting features by rotationally invariant Zernike moments (order 10) and Haralick texture moments (minimum of 1 and maximum of 4 pixels) from each scatter pattern, a classifier was constructed by SVM. Compared to the well-known linear discriminant analysis, SVM is able to construct decision hyperplanes in a multidimensional space that provides the best separation distances among the data classes.22,23 For validation, 10-fold cross-validation with repetition of 30 times was performed to calculate the five statistical parameters (PPV, NPV, specificity, sensitivity, and accuracy). Detailed derivation for scatter pattern analysis was previously reported.20,24 Using the captured data, an SVM algorithm was used for training; the performance of the classifier was provided by the cross-validation matrix. Based on the CV matrix, five statistical parameters were calculated: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy (Table 1). where TP, TN, FP, and FN stand for true positive, true negative, false positive, and false negative, respectively.Table 1Statistical performance of the Baclan™ software for the reflection scatter patterns.
3.Experimental Results3.1.Forward- and Backward-Scattering PatternFigure 3 shows a comparison of forward- and backward-scatter pattern, for E. coli K12 both on a transparent medium (TSA) and opaque mediums (black inked TSA and blood agar plate) The green solid line shows the incoming light, while the red and green dotted lines represent light propagation for the forward- and backward-scatter patterns, respectively. A forward-scattering pattern is observed only with a transparent medium [Fig. 3(c)], whereas the backward-scattering pattern is observed regardless of the opacity of the medium. A red-dashed line shows the reflection of the forward-scatter pattern by the Petri-dish surface, which propagates in a backward direction [Fig. 3(a)]. Since the forward-scattering pattern is not generated in the opaque-medium case [Figs. 3(b) and 3(d)], this backward pattern is observed only with transparent media. The shape of the reflection and the forward scattering from the colony are identical. Even when the pattern is observed in the backward direction, however, it is not considered an actual backward-scatter pattern since the origin of the pattern is forward scatter. The half-diffraction angle of the reflection against the incoming laser has a similar value (0.17 rad) to that of its forward-scatter pattern (0.07 rad), which is -fold smaller than the diffraction angle of the backward-scatter pattern (0.633 rad). 3.2.General Level Backward-Scatter PatternsFigure 4 shows snapshots of backward-scatter patterns for the four bacteria genera on 5% sheep blood agar as measured by the reflective BARDOT system. On visual inspection, the backward-scatter patterns for the genera show distinguishable characteristics. For example, the pattern of E. coli shows an overlapped comb with a spoke-shaped pattern. A small number of ring-shaped patterns appears at the periphery. Unlike other patterns, the boundary between the inner and outer sections of the pattern is unclear. For L. innocua, a speckle cloud with tilde-shaped patterns is observed in the center area, with thin rim patterns with overlapped comb-shaped patterns in the peripheral area. Note that the outermost boundary area of L. innocua shows only outward spoke patterns without a circular rim pattern. For Salmonella, very small speckles are observed in the center area, while relatively thick rims overlapped with spoke patterns are observed at the boundary. For S. aureus, interference of the outgoing wave generated the most unique patterns among the four genera tested; center regions were dominated by speckle-like patterns, while the outer rims showed typical ring patterns with irregular edges. 3.3.Simulation of the Reflected Scatter DimensionFigure 5 displays the pattern size predicted from the bacterial colony model. This calculation was based on Eq. (1)5 and the maximum normal vector from the surface was used as the pattern size at the imaging plane. Figure 5(a) displays the predicted ray-tracing from the incoming laser assuming that each line represents a photon from the 2-mm-diameter laser source that completely covers the -diameter bacterial colony. The green-dotted line represents the maximum reflected angle that occurs at the deflection point of the bacterial colony profile. Figure 5(b) shows a close-up of the bacterial colony area. Figure 5(c) displays a comparison of the theoretical calculation and the experimental results for all four organisms tested. Yellow circles represent the geometrical optics model while the three crossbars show the corresponding experimental data for E. coli (red), S. typhimurium (green), and L. innocua (blue). The other crossbar, S. aureus (purple), is a prediction of half diffraction angle based on its aspect ratio, which was not able to be measured due to its excessively larger pattern size. In this study, all four bacterial genera was plated and incubated in order to achieve diameter size of to . The aspect ratio and diffraction angle are natural characteristics of each genus for a certain growth rate. When the colony diameter size is kept as specified, E. coli, S. typhimurium, and L. innocua result in a smaller aspect ratio of colony along with smaller pattern size. In contrast to the other three, the aspect ratio of S. aureus has the irregular characteristic of an enormously large sized pattern due to its large aspect ratio. Obviously, the model indicates the half diffraction angle of reflection is increasing as the aspect ratio of the colony increases. Furthermore, the experiment data also well-correlates to the model in spite of minor error. Therefore, a legitimate pattern size prediction can be made by the model, as large diffraction of S. aureus is predicted by its high aspect ratio. 3.4.Time-Lapse Backward-Scattering PatternsFigure 6(a) shows a cross-sectional view of E. coli K12 on blood ager; Fig. 6(b) shows the aspect ratio and pattern size (half diffraction angle). Each profile corresponds to an elevation from a single colony on the same plate with the same incubation time. Variation in diameter and aspect ratio is observed even on the same plate. Once the respective aspect ratio and backward-scatter pattern size are plotted for the colonies tested, we can observe a positive correlation between the aspect ratio and pattern diameter [Fig. 6(b)]. An exception can be noted at E. coli colony #4. The aspect ratio of the colony is 0.0482 which should have been higher than what is shown. The purple line in Fig. 6(a) is the morphological profile of E. coli colony #4. The lower aspect ratio can be explained by the tailing edges of the profile at to and 400 to region because the diameter of the colony was overestimated compared to the slope of E. coli colony #4. Therefore, similar to the forward-scatter experiments,25 a high aspect ratio produces a larger backward-scatter pattern. Figure 7 displays the time-dependent aspect ratio for all four genera. Starting from time point , 5 to 15 colonies of each genus at each time point were collected and statistics were calculated. E. coli shows a maximum aspect ratio at hours and then decreases, while L. innocua and S. typhimurium plateau after . Meanwhile, S. aureus begins with the largest aspect ratio (0.15) and gradually decreases at each time point. 3.5.Performance of the SVM-Based ClassifierOne interesting result is that the combination of Zernike moments and Haralick texture patterns was critical to ensure high classification rates on the backward-scatter patterns (Fig. 8). In contrast to the forward-scatter patterns, all four genera generate highly disordered patterns in which the Haralick texture patterns are contributing to a more accurate classification than the Zernike patterns, which are responsible for circularly symmetric patterns. When the analysis was performed with only the Zernike polynomial, average PPV decreased to for Listeria and E. coli. Therefore, all results were obtained using the optimal parameter setting given in Sec. 2.5. Table 1 shows the five statistical parameters calculated from the CV matrix whose diagonal elements resulted in more than 95% classification for all four classes. All five statistical measures calculated by Eqs. (6)–(11) provided excellent results of 94% to 100% for each genera. 3.6.Comparison Between Forward- and Backward-Scatter PatternsForward- and backward-scatter patterns from the same microorganisms were compared using two different instruments. Forward-scatter patterns were captured by the standard BARDOT instrument on TSA media while the backward-scatter patterns were acquired by the reflection-type instrument. Based on the qualitative observation, the former generates more circularly symmetric ring patterns than the latter. For example, forward-scatter patterns of E. coli, L. innocua, and S. typhimurium display dominating ring patterns along with additional radial spoke patterns. In particular, S. aureus generated perfectly concentric ring patterns due to their smooth and high aspect ratio colony profile. Meanwhile, backward-scatter patterns predominantly expressed web-like random interference patterns even though their characteristic length or density was different among the genera. Interestingly, backward-scatter pattern of S. aureus was completely different from the rest of the three samples and showed a brighter center and weaker edge portion of the interference patterns. 4.DiscussionThe backward-scatter patterns of the bacterial colony on blood agar are not effectively generated utilizing a 1-mW, 635-nm laser, which is the standard laser source for the forward scatterometer utilized in the standard BARDOT because the color of the blood agar plate, a major target medium plate, is red owing to the blood in the medium. First, for this wavelength, laser light is highly dispersed and diffusively scattered through the substrate by the blood cells and thus does not generate effective signals in the backward-scatter direction [Fig. 1(c)]. Second, owing to the beam-splitter design, the system is naturally losing half of the incoming power of the laser beam interrogating the bacterial colony. Third, assuming a refractive index of 1.38 to 1.5 for bacterial cells, the Fresnel equation with normal incidence angle provides a reflectance of 2.5% to 2.8% only, so the incident power of the laser must be significantly increased to 5 mW at 532 nm. Even though only forward-scatter patterns were utilized when interrogating colonies on a transparent medium, by default some signals are reflected backward. However, for transparent media, backward-scatter patterns are composites of both reflection and transmission phenomena. To prove this concept, three media plates were compared against each other: standard TSA, special TSA medium (half transparent + half black ink), and standard blood agar medium. Backward scatter from standard TSA medium generated overlapped scatter patterns with smaller patterns in the center from transmitted scattered light reflected at the bottom of the Petri dish and much larger reflection patterns from the colony surface [Fig. 3(a)]. When we interrogate colonies on the special TSA medium, we can observe that the smaller transmitted scatter patterns have disappeared. This situation is similar to that of the blood agar medium, in which only surface reflected patterns were captured in the backward direction. This situation was quantitatively determined by the laser power meter where optical density (OD) of the blood agar plate was 2.08 (0.84% transmission) and OD of the TSA plate was 0.169 (67.6% 11 transmission). The higher OD value of blood agar shows that it is difficult to have lights transmitted through the media to generate scatter pattern in forward direction. The size of forward-scatter pattern increases as it gets farther away from the colony with a half diffraction angle of .11 The half diffraction angle of backward-scatter pattern is related to the aspect ratio (ratio of colony center elevation to diameter) as it is in the forward-scatter case. However, the diffraction angle is much larger than the forward diffraction due to the Gaussian profile (convex shape). Owing to the larger diffraction angle of reflected light, both of the imaging sensor size and the distance between the colony and the imaging sensor are the critical design factors. Because of the BS, which is located in between the sample and camera, a minimum distance must be maintained. Therefore, a large screen is utilized instead of an image sensor to capture the full-size pattern with a high-resolution DSLR camera. The origin of the backward-scatter patterns can be understood as constructive/destructive interference of incoming photons from multiple sources. First, incoming photons are reflected back from the surface of the colony (air–colony interface). Second, the medium-colony interface acts as another source of reflection. Figure 4 shows a stark contrast among the genera in the scatter patterns, which represent the structural and material difference (refractive index) of the colony morphology. For example, S. aureus is known to generate a circular and clear-edged colony on blood agar, and the backward-scatter patterns show circularly symmetric ring patterns on the outer rim area of the patterns. There are both macroscopic and microscopic origins on the construction of the backscatter patterns. For example, the curvature and slope of the colony determine the size of the backscatter patterns and center elevation dictates the number of rings observed.11 For microscopic reasons, cell arrangement or the cell shape can be a factor that determines the interference patterns. Colony refractive index is known as 1.38 to 1.526–28 while agar’s RI is not exactly known. However, based on the mixture of agar powder and water,29 it will be close to 1.33 to 1.35 range. At this moment, the modeling effect needs a more bottom-up approach to model the microscopic effect into the scatter pattern which will be our future task. Replicating the exact theoretical model in the experiment is somewhat difficult but based on the four genera suggests that the thickness does have some effect on the diffusiveness of the backscatter patterns. However, since the cell shape of S. aureus is another factor that can contribute, further development of an individual cell-based optical model will reveal the nature of this phenomena in more detail. A critical factor related to the differentiability of the backward-scatter patterns is ensuring the capture of whole patterns and not just the center part of the pictures. Even though the image-processing tool (Baclan™) works well with the partial patterns, holistic information of the colony is encoded in the two-dimensional scatter pattern. Compared to the forward-scatter patterns, reflection scatter patterns showed more web-like interference patterns (Fig. 9). Many forward-scatter patterns were dominated by circularly symmetric features where the Zernike polynomial was the crucial analyzer needed, whereas for the backward patterns, the role of Haralick texture features becomes more important to ensure better classification. 5.ConclusionA reflection-type scatterometer is proposed for clinical and veterinary applications of light-scatter technology. The instrument was designed with a BS, a 5-mW, 532-nm laser, and a screen to capture the larger reflection scatter patterns. Four genera of samples were interrogated and provided excellent differentiability based on the combination of Zernike polynomial and Haralick texture pattern analysis. AcknowledgmentsThis material was based upon work supported by the U.S. Department of Agriculture, Agricultural Research Service, under Agreement No. 1935-42000-072. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture. Authors EB and JPR declare interests in Doclu LLC, which develops the next generation data analysis software for ELS system. The terms of this arrangement have been reviewed and approved by Purdue University in accordance with its policy on objectivity in research. ReferencesP. P. Banada et al.,
“Optical forward-scattering for detection of Listeria monocytogenes and other Listeria species,”
Biosens. Bioelectron., 22
(8), 1664
–1671
(2007). http://dx.doi.org/10.1016/j.bios.2006.07.028 BBIOE4 0956-5663 Google Scholar
A. K. Singh et al.,
“Laser optical sensor, a label-free on-plate salmonella enterica colony detection tool,”
mBio, 5
(1),
(2014). http://dx.doi.org/10.1128/mBio.01019-13 Google Scholar
K. Huff et al.,
“Light-scattering sensor for real-time identification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae colonies on solid agar plate,”
Microb. Biotechnol., 5
(5), 607
–620
(2012). http://dx.doi.org/10.1111/j.1751-7915.2012.00349.x Google Scholar
K.-P. Kim et al.,
“Novel PCR assays complement laser biosensor-based method and facilitate listeria species detection from food,”
Sensors, 15 22672
–22691
(2015). http://dx.doi.org/10.3390/s150922672 SNSRES 0746-9462 Google Scholar
Y. He et al.,
“Rapid identification and classification of Campylobacter spp. using laser optical scattering technology,”
Food Microbiol., 47 28
–35
(2015). http://dx.doi.org/10.1016/j.fm.2014.11.004 FOMIE5 0740-0020 Google Scholar
Y. Tang et al.,
“Light scattering sensor for direct identification of colonies of Escherichia coli serogroups O26, O45, O103, O111, O121, O145 and O157,”
PloS One, 9
(8), e105272
(2014). http://dx.doi.org/10.1371/journal.pone.0105272 POLNCL 1932-6203 Google Scholar
U. Minoni, A. Signoroni and G. Nassini,
“On the application of optical forward-scattering to bacterial identification in an automated clinical analysis perspective,”
Biosens. Bioelectron., 68 536
–543
(2015). http://dx.doi.org/10.1016/j.bios.2015.01.047 BBIOE4 0956-5663 Google Scholar
A. Suchwalko et al.,
“Bacteria species identification by the statistical analysis of bacterial colonies Fresnel patterns,”
Opt. Express, 21
(9), 11322
–11337
(2013). http://dx.doi.org/10.1364/OE.21.011322 Google Scholar
P. R. Marcoux et al.,
“Optical forward-scattering for identification of bacteria within microcolonies,”
Appl. Microbiol. Biotechnol., 98
(5), 2243
–2254
(2014). http://dx.doi.org/10.1007/s00253-013-5495-4 AMBIDG 0175-7598 Google Scholar
B. Rajwa et al.,
“Discovering the unknown: detection of emerging pathogens using a label-free light-scattering system,”
Cytometry. Part A, 77
(12), 1103
–1112
(2010). http://dx.doi.org/10.1002/cyto.a.v77a:12 1552-4922 Google Scholar
E. Bae et al.,
“Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns,”
J. Biomed. Opt., 15
(4), 045001
(2010). http://dx.doi.org/10.1117/1.3463003 JBOPFO 1083-3668 Google Scholar
H. Kim et al.,
“Scalar diffraction modeling of multispectral forward scatter patterns from bacterial colonies,”
Opt. Express, 23
(7), 8545
–8554
(2015). http://dx.doi.org/10.1364/OE.23.008545 Google Scholar
H. Kim et al.,
“Laser-induced speckle scatter patterns in Bacillus colonies,”
Front. Microbiol., 5 537
(2014). http://dx.doi.org/10.3389/fmicb.2014.00537 Google Scholar
L. Beutin, S. Zimmermann and K. Gleier,
“Rapid detection and isolation of shiga-like toxin (verocytotoxin)-producing Escherichia coli by direct testing of individual enterohemolytic colonies from washed sheep blood agar plates in the VTEC-RPLA assay,”
J. Clin. Microbiol., 34
(11), 2812
–2814
(1996). JCMIDW 1070-633X Google Scholar
D. M. Musher, R. Montoya and A. Wanahita,
“Diagnostic value of microscopic examination of Gram-stained sputum and sputum cultures in patients with bacteremic pneumococcal pneumonia,”
Clin. Inf. Dis., 39
(2), 165
–169
(2004). http://dx.doi.org/10.1086/421497 CIDIEL 1058-4838 Google Scholar
H. Kim et al.,
“Development of an integrated optical analyzer for characterization of growth dynamics of bacterial colonies,”
J. Biophotonics, 6
(11–12), 929
–937
(2013). http://dx.doi.org/10.1002/jbio.v6.11/12 Google Scholar
F. L. Pedrotti and P. Leno, Introduction to Optics, 3rd ed.Prentice Hall, Englewood Cliffs, New Jersey
(1993). Google Scholar
J. A. Shapiro,
“Pattern and control in bacterial colony development,”
Sci. Prog., 76
(301–302), 399
–424
(1992). SCPRAY 0036-8504 Google Scholar
C. Kanan and G. W. Cottrell,
“Color-to-grayscale: does the method matter in image recognition,”
PloS One, 7 e29740
(2012). http://dx.doi.org/10.1371/journal.pone.0029740 POLNCL 1932-6203 Google Scholar
E. Bae et al.,
“Development of a microbial high-throughput screening instrument based on elastic light scatter patterns,”
Rev. Sci. Instrum., 83
(4), 044304
(2012). http://dx.doi.org/10.1063/1.3697853 RSINAK 0034-6748 Google Scholar
B. Bayraktar et al.,
“Feature extraction from light-scatter patterns of Listeria colonies for identification and classification,”
J. Biomed. Opt., 11 034006
(2006). http://dx.doi.org/10.1117/1.2203987 JBOPFO 1083-3668 Google Scholar
C.-C. Chang and C.-J. Lin,
“LIBSVM: a library for support vector machines,”
ACM Trans. Intell. Syst. Technol., 2
(3), 1
–27
(2011). http://dx.doi.org/10.1145/1961189 Google Scholar
V. N. Vapnik and V. Vapnik, Statistical Learning Theory, Wiley, New York
(1998). Google Scholar
B. Bayraktar et al.,
“Feature extraction from light-scatter patterns of Listeria colonies for identification and classification,”
J. Biomed. Opt., 11
(3), 034006
(2006). http://dx.doi.org/10.1117/1.2203987 JBOPFO 1083-3668 Google Scholar
E. Bae et al.,
“Biophysical modeling of forward scattering from bacterial colonies using scalar diffraction theory,”
Appl. Opt., 46
(17), 3639
–3648
(2007). http://dx.doi.org/10.1364/AO.46.003639 APOPAI 0003-6935 Google Scholar
A. E. Balaev, K. N. Dvoretski and V. A. Doubrovski,
“Determination of refractive index of rod-shaped bacteria from spectral extinction measurements,”
in Saratov Fall Meeting 2002: Optical Technologies in Biophysics and Medicine IV,
375
–380
(2003). Google Scholar
S. J. Hart and T. A. Leski,
“Refractive index determination of biological particles,”
(2006). Google Scholar
P. Liu et al.,
“Real-time measurement of single bacterium’s refractive index using optofluidic immersion refractometry,”
Proc. Eng., 87 356
–359
(2014). http://dx.doi.org/10.1016/j.proeng.2014.11.743 Google Scholar
E. Bae et al.,
“On the sensitivity of forward scattering patterns from bacterial colonies to media composition,”
J. Biophotonics, 4
(4), 236
–243
(2011). http://dx.doi.org/10.1002/jbio.v4.4 Google Scholar
BiographyHuisung Kim received his BS degree from Hongik University in Korea and his MS degree from Gwangju Institute of Science and Technology (GIST), in Korea. In 2016, he received his PhD in mechanical engineering at Purdue University. His current research is on bacteria detection using optical light-scattering technique. Before he joined Purdue University, he worked at GIST on development of a fiber type confocal microscope module for integrated microscopes. His research interest involves laser applications, mechanical and optical design and control, signal and image analysis, and instrumentation. Iyll-Joon Doh is a PhD student in the Mechanical Engineering Department at Purdue University whose research interest involves mechanical and electrical design, precision control, instrumentation, and laser applications in biomedical areas. His current research is on bacteria identification using elastic light-scattering techniques. Prior to beginning the PhD program, he received his master’s degree of science in mechanical engineering at Purdue University in 2016. Jennifer Sturgis received her BS degree from Purdue University and has worked in the Laboratory of Professor J. Paul Robinson for the past 15 years. For 10 years, she managed the imaging facilities of Purdue University Cytometry Laboratories, a university-wide core providing confocal and fluorescence microscopy facilities. She has worked with light-scattering technology for microbial identification for the past 5 years. Arun K. Bhunia is a professor of food microbiology in the Department of Food Science, Purdue University, and currently conducts research on pathogen detection, employing optical and electrical sensors including protein biochip, light-scattering sensors, fiber-optic sensors, and cell-based sensors. He is also studying mechanisms of pathogenesis for Listeria monocytogenes during the intestinal phase of infection. He is also active in teaching graduate level courses: microbial foodborne pathogens, microbial techniques for food pathogens, and intestinal microbiology and immunology. J. Paul Robinson is currently the SVM professor of cytomics and professor of biomedical engineering in the Weldon School of Biomedical Engineering, Purdue University. He has published over 160 peer-reviewed publications, 30 book chapters, and edited 8 books. His laboratory focuses on diagnostic technology development from single cell analysis through microbial detection systems. Euiwon Bae is currently a senior research scientist of mechanical engineering at Purdue University. He received his BS degree from Korea University, his MS degree from Korea Advanced Institute of Science and Technology, and his PhD from the School of Mechanical Engineering at Purdue University, specializing in optical metrology. He has over 30 scientific articles and his research interest includes discrete dipole approximation modeling and developing instruments via light scattering for biological samples. |