Euiwon Bae

Label-free detection of multiple bacterial pathogens using light-scattering sensor

Technologies for rapid detection and classification of bacterial pathogens are crucial for securing the food supply. This report describes a light-scattering sensor capable of real-time detection and identification of colonies of multiple pathogens without the need for a labeling reagent or biochemical processing. Bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. Zernike moment invariants and Haralick descriptors aid in feature extraction and construction of the scatter-signature image library. The method is able to distinguish bacterial cultures at the genus and species level for Listeria, Staphylococcus, Salmonella, Vibrio, and Escherichia with an accuracy of 90-99% for samples derived from food or experimentally infected animal. Varied amounts of exopolysaccharide produced by the bacteria causes changes in phase modulation distributions, resulting in strikingly different scatter signatures. With the aid of a robust database the method can potentially detect and identify any bacteria colony essentially instantaneously. Unlike other methods, it does not destroy the sample, but leaves it intact for other confirmatory testing, if needed, for forensic or outbreak investigations.

Biophysical modeling of forward scattering from bacterial colonies using scalar diffraction theory

A model for forward scattering from bacterial colonies is presented. The colonies of interest consist of approximately 10(12) - 10(13) individual bacteria densely packed in a configuration several millimeters in diameter and approximately 0.1-0.2 mm in thickness. The model is based on scalar diffraction theory and accounts for amplitude and phase modulation created by three macroscopic properties of the colonies: phase modulation due to the surface topography, phase modulation due to the radial structure observed from some strains and species, and diffraction from the outline of the colony. Phase contrast and confocal microscopy were performed to provide quantitative information on the shape and internal structure of the colonies. The computed results showed excellent agreement with the experimental scattering data for three different Listeria species: Listeria innocua, Listeria ivanovii, and Listeria monocytogenes. The results provide a physical explanation for the unique and distinctive scattering signatures produced by colonies of closely related Listeria species and support the efficacy of forward scattering for rapid detection and classification of pathogens without tagging.

Light-scattering sensor for real-time identification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae colonies on solid agar plate.

The three most common pathogenic species of Vibrio, Vibrio cholerae, Vibrio parahaemolyticus and Vibrio vulnificus, are of major concerns due to increased incidence of water‐ and seafood‐related outbreaks and illness worldwide. Current methods are lengthy and require biochemical and molecular confirmation. A novel label‐free forward light‐scattering sensor was developed to detect and identify colonies of these three pathogens in real time in the presence of other vibrios in food or water samples. Vibrio colonies grown on agar plates were illuminated by a 635 nm laser beam and scatter‐image signatures were acquired using a CCD (charge‐coupled device) camera in an automated BARDOT (BActerial Rapid Detection using Optical light‐scattering Technology) system. Although a limited number of Vibrio species was tested, each produced a unique light‐scattering signature that is consistent from colony to colony. Subsequently a pattern recognition system analysing the collected light‐scatter information provided classification in 1−2 min with an accuracy of 99%. The light‐scattering signatures were unaffected by subjecting the bacteria to physiological stressors: osmotic imbalance, acid, heat and recovery from a viable but non‐culturable state. Furthermore, employing a standard sample enrichment in alkaline peptone water for 6 h followed by plating on selective thiosulphate citrate bile salts sucrose agar at 30°C for ∼ 12 h, the light‐scattering sensor successfully detected V. cholerae, V. parahaemolyticus and V. vulnificus present in oyster or water samples in 18 h even in the presence of other vibrios or other bacteria, indicating the suitability of the sensor as a powerful screening tool for pathogens on agar plates.

Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns

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.

Six-degree-of-freedom displacement measurement system using a diffraction grating

Six-degree-of-freedom displacement measurement systems are applicable in many fields: precision machine control, precision assembly, vibration analysis, and so on. This article presents a new six-degree-of-freedom displacement measurement system utilizing typical features of a diffraction grating. It is composed of a laser source, three position sensitive detectors, a diffraction grating target, and several optical components. Six-degree-of-freedom displacement is calculated from the coordinates of diffracted rays on the detectors. A forward and an inverse problem were solved to compute the full pose of an object through kinematic analysis. The experimental results show that the measurement system had a maximum error of ±10 μm for translation and ±0.012° for rotation. The repeatability is about 10 μm for translation and 0.01° for rotation.

Laser Optical Sensor, a Label-Free On-Plate Salmonella enterica Colony Detection Tool

ABSTRACT We investigated the application capabilities of a laser optical sensor, BARDOT (bacterial rapid detection using optical scatter technology) to generate differentiating scatter patterns for the 20 most frequently reported serovars of Salmonella enterica. Initially, the study tested the classification ability of BARDOT by using six Salmonella serovars grown on brain heart infusion, brilliant green, xylose lysine deoxycholate, and xylose lysine tergitol 4 (XLT4) agar plates. Highly accurate discrimination (95.9%) was obtained by using scatter signatures collected from colonies grown on XLT4. Further verification used a total of 36 serovars (the top 20 plus 16) comprising 123 strains with classification precision levels of 88 to 100%. The similarities between the optical phenotypes of strains analyzed by BARDOT were in general agreement with the genotypes analyzed by pulsed-field gel electrophoresis (PFGE). BARDOT was evaluated for the real-time detection and identification of Salmonella colonies grown from inoculated (1.2 × 102 CFU/30 g) peanut butter, chicken breast, and spinach or from naturally contaminated meat. After a sequential enrichment in buffered peptone water and modified Rappaport Vassiliadis broth for 4 h each, followed by growth on XLT4 (~16 h), BARDOT detected S. Typhimurium with 84% accuracy in 24 h, returning results comparable to those of the USDA Food Safety and Inspection Service method, which requires ~72 h. BARDOT also detected Salmonella (90 to 100% accuracy) in the presence of background microbiota from naturally contaminated meat, verified by 16S rRNA sequencing and PFGE. Prolonged residence (28 days) of Salmonella in peanut butter did not affect the bacterial ability to form colonies with consistent optical phenotypes. This study shows BARDOT’s potential for nondestructive and high-throughput detection of Salmonella in food samples. IMPORTANCE High-throughput screening of food products for pathogens would have a significant impact on the reduction of food-borne hazards. A laser optical sensor was developed to screen pathogen colonies on an agar plate instantly without damaging the colonies; this method aids in early pathogen detection by the classical microbiological culture-based method. Here we demonstrate that this sensor was able to detect the 36 Salmonella serovars tested, including the top 20 serovars, and to identify isolates of the top 8 Salmonella serovars. Furthermore, it can detect Salmonella in food samples in the presence of background microbiota in 24 h, whereas the standard USDA Food Safety and Inspection Service method requires about 72 h. High-throughput screening of food products for pathogens would have a significant impact on the reduction of food-borne hazards. A laser optical sensor was developed to screen pathogen colonies on an agar plate instantly without damaging the colonies; this method aids in early pathogen detection by the classical microbiological culture-based method. Here we demonstrate that this sensor was able to detect the 36 Salmonella serovars tested, including the top 20 serovars, and to identify isolates of the top 8 Salmonella serovars. Furthermore, it can detect Salmonella in food samples in the presence of background microbiota in 24 h, whereas the standard USDA Food Safety and Inspection Service method requires about 72 h.

Smartphone-based colorimetric analysis for detection of saliva alcohol concentration.

A simple device and associated analytical methods are reported. We provide objective and accurate determination of saliva alcohol concentrations using smartphone-based colorimetric imaging. The device utilizes any smartphone with a miniature attachment that positions the sample and provides constant illumination for sample imaging. Analyses of histograms based on channel imaging of red-green-blue (RGB) and hue-saturation-value (HSV) color space provide unambiguous determination of blood alcohol concentration from color changes on sample pads. A smartphone-based sample analysis by colorimetry was developed and tested with blind samples that matched with the training sets. This technology can be adapted to any smartphone and used to conduct color change assays.

On the sensitivity of forward scattering patterns from bacterial colonies to media composition

Morphology of colonies is important for taxonomy and diagnostics in microbiology where the response to environmental factors is sensitive enough to support discrimination. In this research, we analyzed the forward scattering patterns of individual Escherichia coli K12 colonies when agar hardness and nutrition levels were varied from the control sample. As the agar concentration increased from 1.2% to 1.8%, the diameter of the forward scattering patterns also increased for the same experimental condition which reflects that the colony thickness at the apex is greater for increased agar concentrations. Regarding nutrition, increasing dextrose resulted in smaller mean colony diameters while the mean diameters of the colonies were proportional to the yeast extract concentration up to 0.5%. The result reveals that ±0.3% agar concentration from the control sample is sufficient to create variations in the scattering patterns. For nutrition -0.25% of yeast extract showed significant variations while +0.25% from control sample showed minimal variations.

Analysis of time-resolved scattering from macroscale bacterial colonies

We investigate the relationship of incubation time and forward-scattering signature for bacterial colonies grown on solid nutrient surfaces. The aim of this research is to understand the colony growth characteristics and the corresponding evolution of the scattering patterns for a variety of pathogenic bacteria relevant to food safety. In particular, we characterized time-varying macroscopic and microscopic morphological properties of the growing colonies and modeled their optical properties in terms of two-dimensional (2-D) amplitude and phase modulation distributions. These distributions, in turn, serve as input to scalar diffraction theory, which is, in turn, used to predict forward-scattering signatures. For the present work, three different species of Listeria were considered: Listeria innocua, Listeria ivanovii, and Listeria monocytogenes. The baseline experiments involved the growth of cultures on brain heart infusion (BHI) agar and the capture of scatter images every 6 h over a total incubation period of 42 h. The micro- and macroscopic morphologies of the colonies were studied by phase contrast microscopy. Growth curves, represented by colony diameter as a function of time, were compared with the measured time-evolution of the scattering signatures.

Light Scattering Sensor for Direct Identification of Colonies of Escherichia coli Serogroups O26, O45, O103, O111, O121, O145 and O157

Background Shiga-toxin producing Escherichia coli (STEC) have emerged as important foodborne pathogens, among which seven serogroups (O26, O45, O103, O111, O121, O145, O157) are most frequently implicated in human infection. The aim was to determine if a light scattering sensor can be used to rapidly identify the colonies of STEC serogroups on selective agar plates. Methodology/Principal Findings Initially, a total of 37 STEC strains representing seven serovars were grown on four different selective agar media, including sorbitol MacConkey (SMAC), Rainbow Agar O157, BBL CHROMagarO157, and R&F E. coli O157:H7, as well as nonselective Brain Heart Infusion agar. The colonies were scanned by an automated light scattering sensor, known as BARDOT (BActerial Rapid Detection using Optical scattering Technology), to acquire scatter patterns of STEC serogroups, and the scatter patterns were analyzed using an image classifier. Among all of the selective media tested, both SMAC and Rainbow provided the best differentiation results allowing multi-class classification of all serovars with an average accuracy of more than 90% after 10–12 h of growth, even though the colony appearance was indistinguishable at that early stage of growth. SMAC was chosen for exhaustive scatter image library development, and 36 additional strains of O157:H7 and 11 non-O157 serovars were examined, with each serogroup producing unique differential scatter patterns. Colony scatter images were also tested with samples derived from pure and mixed cultures, as well as experimentally inoculated food samples. BARDOT accurately detected O157 and O26 serovars from a mixed culture and also from inoculated lettuce and ground beef (10-h broth enrichment +12-h on-plate incubation) in the presence of natural background microbiota in less than 24 h. Conclusions BARDOT could potentially be used as a screening tool during isolation of the most important STEC serovars on selective agar plates from food samples in less than 24 h.