Label-free identification of bacterial microcolonies via elastic scattering

Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. KEY POINTS: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes.

Phenotypic characteristics of the mycelium of Pleurotus geesteranus using image recognition technology

Phenotypic analysis has significant potential for aiding breeding efforts. However, there is a notable lack of studies utilizing phenotypic analysis in the field of edible fungi. Pleurotus geesteranus is a lucrative edible fungus with significant market demand and substantial industrial output, and early-stage phenotypic analysis of Pleurotus geesteranus is imperative during its breeding process. This study utilizes image recognition technology to investigate the phenotypic features of the mycelium of P. geesteranus. We aim to establish the relations between these phenotypic characteristics and mycelial quality. Four groups of mycelia, namely, the non-degraded and degraded mycelium and the 5th and 14th subcultures, are used as image sources. Two categories of phenotypic metrics, outline and texture, are quantitatively calculated and analyzed. In the outline features of the mycelium, five indexes, namely, mycelial perimeter, radius, area, growth rate, and change speed, are proposed to demonstrate mycelial growth. In the texture features of the mycelium, five indexes, namely, mycelial coverage, roundness, groove depth, density, and density change, are studied to analyze the phenotypic characteristics of the mycelium. Moreover, we also compared the cellulase and laccase activities of the mycelium and found that cellulase level was consistent with the phenotypic indices of the mycelium, which further verified the accuracy of digital image processing technology in analyzing the phenotypic characteristics of the mycelium. The results indicate that there are significant differences in these 10 phenotypic characteristic indices ( P < 0.001 ), elucidating a close relationship between phenotypic characteristics and mycelial quality. This conclusion facilitates rapid and accurate strain selection in the early breeding stage of P. geesteranus.

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look Once version 4 (YOLOv4) can accurately identify the presence of E. coli at the microcolony stage after a 3-h cultivation. Integrating with phase-contrast microscopic imaging, YOLOv4 discriminated E. coli from seven other common foodborne bacterial species with an average precision of 94%. This approach also enabled the rapid quantification of E. coli concentrations over 3 orders of magnitude with an R² of 0.995. For romaine lettuce spiked with E. coli (10 to 10³ CFU/g), the trained YOLOv4 detector had a false-negative rate of less than 10%. This approach accelerates analysis and avoids manual result determination, which has the potential to be applied as a rapid and user-friendly bacterial sensing approach in food industries. IMPORTANCE A simple, cost-effective, and rapid method is desired to identify potential pathogen contamination in food products and thus prevent foodborne illnesses and outbreaks. This study combined artificial intelligence (AI) and optical imaging to detect bacteria at the microcolony stage within 3 h of inoculation. This approach eliminates the need for time-consuming culture-based colony isolation and resource-intensive molecular approaches for bacterial identification. The approach developed in this study is broadly applicable for the identification of diverse bacterial species. In addition, this approach can be implemented in resource-limited areas, as it does not require expensive instruments and significantly trained human resources. This AI-assisted detection not only achieves high accuracy in bacterial classification but also provides the potential for automated bacterial detection, reducing labor workloads in food industries, environmental monitoring, and clinical settings.

Petri-plate, bacteria, and laser optical scattering sensor

Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21^st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.

Elastic Light Scatter Pattern Analysis for the Expedited Detection of Yersinia Species in Pork Mince: Proof of Concept

Isolation of the pathogens Yersinia enterocolitica and Yersinia pseudotuberculosis from foods typically rely on slow (10–21 day) “cold enrichment” protocols before confirmed results are obtained. We describe an approach that yields results in 39 h that combines an alternative enrichment method with culture on a non-selective medium, and subsequent identification of suspect colonies using elastic light scatter (ELS) analysis. A prototype database of ELS profiles from five Yersinia species and six other bacterial genera found in pork mince was established, and used to compare similar profiles of colonies obtained from enrichment cultures from pork mince samples seeded with representative strains of Y. enterocolitica and Y. pseudotuberculosis. The presumptive identification by ELS using computerised or visual analyses of 83/90 colonies in these experiments as the target species was confirmed by partial 16S rDNA sequencing. In addition to seeded cultures, our method recovered two naturally occurring Yersinia strains. Our results indicate that modified enrichment combined with ELS is a promising new approach for expedited detection of foodborne pathogenic yersiniae.

Optical multi-channel interrogation instrument for bacterial colony characterization

A single instrument that includes multiple optical channels was developed to simultaneously measure various optical and associated biophysical characteristics of a bacterial colony. The multi-channel device can provide five distinct optical features without the need to transfer the sample to multiple locations or instruments. The available measurement channels are bright-field light microscopy, 3-D colony-morphology map, 2-D spatial optical-density distribution, spectral forward-scattering pattern, and spectral optical density. The series of multiple morphological interrogations is beneficial in understanding the bio-optical features of a bacterial colony and the correlations among them, resulting in an enhanced power of phenotypic bacterial discrimination. To enable a one-shot interrogation, a confocal laser scanning module was built as an add-on to an upright microscope. Three different-wavelength diode lasers were used for the spectral analysis, and high-speed pin photodiodes and CMOS sensors were utilized as detectors to measure the spectral OD and light-scatter pattern. The proposed instrument and algorithms were evaluated with four bacterial genera, Escherichia coli, Listeria innocua, Salmonella Typhimurium, and Staphylococcus aureus; their resulting data provided a more complete picture of the optical characterization of bacterial colonies.

Food Microstructure and Fat Content Affect Growth Morphology, Growth Kinetics, and Preferred Phase for Cell Growth of Listeria monocytogenes in Fish-Based Model Systems

Food microstructure significantly affects microbial growth dynamics, but knowledge concerning the exact influencing mechanisms at a microscopic scale is limited. The food microstructural influence on Listeria monocytogenes (green fluorescent protein strain) growth at 10°C in fish-based food model systems was investigated by confocal laser scanning microscopy. The model systems had different microstructures, i.e., liquid, xanthan (high-viscosity liquid), aqueous gel, and emulsion and gelled emulsion systems varying in fat content. Bacteria grew as single cells, small aggregates, and microcolonies of different sizes (based on colony radii [size I, 1.5 to 5.0 μm; size II, 5.0 to 10.0 μm; size III, 10.0 to 15.0 μm; and size IV, ≥15 μm]). In the liquid, small aggregates and size I microcolonies were predominantly present, while size II and III microcolonies were predominant in the xanthan and aqueous gel. Cells in the emulsions and gelled emulsions grew in the aqueous phase and on the fat-water interface. A microbial adhesion to solvent assay demonstrated limited bacterial nonpolar solvent affinities, implying that this behavior was probably not caused by cell surface hydrophobicity. In systems containing 1 and 5% fat, the largest cell volume was mainly represented by size I and II microcolonies, while at 10 and 20% fat a few size IV microcolonies comprised nearly the total cell volume. Microscopic results (concerning, e.g., growth morphology, microcolony size, intercolony distances, and the preferred phase for growth) were related to previously obtained macroscopic growth dynamics in the model systems for an L. monocytogenes strain cocktail, leading to more substantiated explanations for the influence of food microstructural aspects on lag phase duration and growth rate.IMPORTANCEListeria monocytogenes is one of the most hazardous foodborne pathogens due to the high fatality rate of the disease (i.e., listeriosis). In this study, the growth behavior of L. monocytogenes was investigated at a microscopic scale in food model systems that mimic processed fish products (e.g., fish paté and fish soup), and the results were related to macroscopic growth parameters. Many studies have previously focused on the food microstructural influence on microbial growth. The novelty of this work lies in (i) the microscopic investigation of products with a complex composition and/or structure using confocal laser scanning microscopy and (ii) the direct link to the macroscopic level. Growth behavior (i.e., concerning bacterial growth morphology and preferred phase for growth) was more complex than assumed in common macroscopic studies. Consequently, the effectiveness of industrial antimicrobial food preservation technologies (e.g., thermal processing) might be overestimated for certain products, which may have critical food safety implications.

Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches

Detection and discrimination of bacteria are crucial in a wide range of industries, including clinical testing, and food and beverage production. Staphylococcus species cause various diseases, and are frequently detected in clinical specimens and food products. In particular, S. aureus is well known to be the most pathogenic species. Conventional phenotypic and genotypic methods for discrimination of Staphylococcus spp. are time-consuming and labor-intensive. To address this issue, in the present study, we applied a novel discrimination methodology called colony fingerprinting. Colony fingerprinting discriminates bacterial species based on the multivariate analysis of the images of microcolonies (referred to as colony fingerprints) with a size of up to 250 μm in diameter. The colony fingerprints were obtained via a lens-less imaging system. Profiling of the colony fingerprints of five Staphylococcus spp. (S. aureus, S. epidermidis, S. haemolyticus, S. saprophyticus, and S. simulans) revealed that the central regions of the colony fingerprints showed species-specific patterns. We developed 14 discriminative parameters, some of which highlight the features of the central regions, and analyzed them by several machine learning approaches. As a result, artificial neural network (ANN), support vector machine (SVM), and random forest (RF) showed high performance for discrimination of theses bacteria. Bacterial discrimination by colony fingerprinting can be performed within 11 h, on average, and therefore can cut discrimination time in half compared to conventional methods. Moreover, we also successfully demonstrated discrimination of S. aureus in a mixed culture with Pseudomonas aeruginosa. These results suggest that colony fingerprinting is useful for discrimination of Staphylococcus spp.

Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies

Detection and identification of microbial species are crucial in a wide range of industries, including production of beverages, foods, cosmetics, and pharmaceuticals. Traditionally, colony formation and its morphological analysis (e.g., size, shape, and color) with a naked eye have been employed for this purpose. However, such a conventional method is time consuming, labor intensive, and not very reproducible. To overcome these problems, we propose a novel method that detects microcolonies (diameter 10–500 μm) using a lensless imaging system. When comparing colony images of five microorganisms from different genera (Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans), the images showed obvious different features. Being closely related species, St. aureus and St. epidermidis resembled each other, but the imaging analysis could extract substantial information (colony fingerprints) including the morphological and physiological features, and linear discriminant analysis of the colony fingerprints distinguished these two species with 100% of accuracy. Because this system may offer many advantages such as high-throughput testing, lower costs, more compact equipment, and ease of automation, it holds promise for microbial detection and identification in various academic and industrial areas.

Virulence Gene-Associated Mutant Bacterial Colonies Generate Differentiating Two-Dimensional Laser Scatter Fingerprints

In this study, we investigated whether a laser scatterometer designated BARDOT (bacterial rapid detection using optical scattering technology) could be used to directly screen colonies of Listeria monocytogenes, a model pathogen, with mutations in several known virulence genes, including the genes encoding Listeria adhesion protein (LAP; lap mutant), internalin A (ΔinlA strain), and an accessory secretory protein (ΔsecA2 strain). Here we show that the scatter patterns of lap mutant, ΔinlA, and ΔsecA2 colonies were markedly different from that of the wild type (WT), with >95% positive predictive values (PPVs), whereas for the complemented mutant strains, scatter patterns were restored to that of the WT. The scatter image library successfully distinguished the lap mutant and ΔinlA mutant strains from the WT in mixed-culture experiments, including a coinfection study using the Caco-2 cell line. Among the biophysical parameters examined, the colony height and optical density did not reveal any discernible differences between the mutant and WT strains. We also found that differential LAP expression in L. monocytogenes serotype 4b strains also affected the scatter patterns of the colonies. The results from this study suggest that BARDOT can be used to screen and enumerate mutant strains separately from the WT based on differential colony scatter patterns.

IMPORTANCE

In studies of microbial pathogenesis, virulence-encoding genes are routinely disrupted by deletion or insertion to create mutant strains. Screening of mutant strains is an arduous process involving plating on selective growth media, replica plating, colony hybridization, DNA isolation, and PCR or immunoassays. We applied a noninvasive laser scatterometer to differentiate mutant bacterial colonies from WT colonies based on forward optical scatter patterns. This study demonstrates that BARDOT can be used as a novel, label-free, real-time tool to aid researchers in screening virulence gene-associated mutant colonies during microbial pathogenesis, coinfection, and genetic manipulation studies.

Bacterial Colonies in Solid Media and Foods: A Review on Their Growth and Interactions with the Micro-Environment

Bacteria, either indigenous or added, are immobilized in solid foods where they grow as colonies. Since the 80's, relatively few research groups have explored the implications of bacteria growing as colonies and mostly focused on pathogens in large colonies on agar/gelatine media. It is only recently that high resolution imaging techniques and biophysical characterization techniques increased the understanding of the growth of bacterial colonies, for different sizes of colonies, at the microscopic level and even down to the molecular level. This review covers the studies on bacterial colony growth in agar or gelatine media mimicking the food environment and in model cheese. The following conclusions have been brought to light. Firstly, under unfavorable conditions, mimicking food conditions, the immobilization of bacteria always constrains their growth in comparison with planktonic growth and increases the sensibility of bacteria to environmental stresses. Secondly, the spatial distribution describes both the distance between colonies and the size of the colonies as a function of the initial level of population. By studying the literature, we concluded that there systematically exists a threshold that distinguishes micro-colonies (radius < 100-200 μm) from macro-colonies (radius >200 μm). Micro-colonies growth resembles planktonic growth and no pH microgradients could be observed. Macro-colonies growth is slower than planktonic growth and pH microgradients could be observed in and around them due to diffusion limitations which occur around, but also inside the macro-colonies. Diffusion limitations of milk proteins have been demonstrated in a model cheese around and in the bacterial colonies. In conclusion, the impact of immobilization is predominant for macro-colonies in comparison with micro-colonies. However, the interaction between the colonies and the food matrix itself remains to be further investigated at the microscopic scale.

Current status and future prospects of using advanced computer-based methods to study bacterial colonial morphology

Despite the advancement of recent molecular technologies, culturing is still considered the gold standard for microbial sample analysis. Here we review three different bacterial colony-based screening modalities that provide significant information beyond the simple shape and color of the colony. The plate imaging technique provides numeration and quantitative spectral reflectance information for each colony, while Raman spectroscopic analysis of bacteria colonies relates the Raman-shifted peaks to specific chemical bonding. Finally, the elastic-light-scatter technique provides a volumetric interaction of the whole colony through laser-bacteria interactions, instantly capturing the morphological traits of the colony and allowing quantitative classifications.

Recent trends in non-invasive in situ techniques to monitor bacterial colonies in solid (model) food

Planktonic cells typically found in liquid systems, are routinely used for building predictive models or assessing the efficacy of food preserving technologies. However, freely suspended cells often show different susceptibility to environmental hurdles than colony cells in solid matrices. Limited oxygen, water and nutrient availability, metabolite accumulation and physical constraints due to cell immobilization in the matrix, are main factors affecting cell growth. Moreover, intra- and inter-colony interactions, as a consequence of the initial microbial load in solid systems, may affect microbial physiology. Predictive food microbiology approaches are moving toward a more realistic resemblance to food products, performing studies in structured solid systems instead of liquids. Since structured systems promote microbial cells to become immobilized and grow as colonies, it is essential to study the colony behavior, not only for food safety assurance systems, but also for understanding cell physiology and optimizing food production processes in solid matrices. Traditionally, microbial dynamics in solid systems have been assessed with a macroscopic approach by applying invasive analytical techniques; for instance, viable plate counting, which yield information about overall population. In the last years, this approach is being substituted by more mechanistically inspired ones at mesoscopic (colony) and microscopic (cell) levels. Therefore, non-invasive and in situ monitoring is mandatory for a deeper insight into bacterial colony dynamics. Several methodologies that enable high-throughput data collection have been developed, such as microscopy-based techniques coupled with image analysis and OD-based measurements in microplate readers. This research paper provides an overview of non-invasive in situ techniques to monitor bacterial colonies in solid (model) food and emphasizes their advantages and inconveniences in terms of accuracy, performance and output information.

On the application of optical forward-scattering to bacterial identification in an automated clinical analysis perspective

The Optical Forward Scattering (OFS) technique can be used to identify pathogens by direct observation of bacteria colonies growing on a culture plate. The identification is based on the acquisition of scattering images from isolated colonies and their subsequent comparison with reference images acquired from known bacteria. The technique has been mainly studied for the identification of pathogens in the food-safety field. This paper focuses on the possibility of extending the applicability of the technique to the field of clinical laboratory automation. This scenario requires that the paradigm of image acquisition at fixed colony-dimension, well established in the food-safety applications, should be substituted by an acquisition at fixed incubation time. As a consequence, the scatterometer must be adjustable in real-time for adapting to the actual features of the bacterial colony. The paper describes an OFS system prototype qualified by the possibility to tune both the laser beam diameter and the acquisition camera field of view. Preliminary experiments on bacteria cultures from pathogens causing infections of the urinary tract show that the proposed approach is promising for the development of an automated bacteria identification station. The new OFS approach also involves an alternative method for building a reference image database for subsequent image analysis.

Three-dimensional characterization of bacterial microcolonies on solid agar-based culture media

For the last century, in vitro diagnostic process in microbiology has mainly relied on the growth of bacteria on the surface of a solid agar medium. Nevertheless, few studies focused in the past on the dynamics of microcolonies growth on agar surface before 8 to 10h of incubation. In this article, chromatic confocal microscopy has been applied to characterize the early development of a bacterial colony. This technology relies on a differential focusing depth of the white light. It allows one to fully measure the tridimensional shape of microcolonies more quickly than classical confocal microscopy but with the same spatial resolution. Placing the device in an incubator, the method was able to individually track colonies growing on an agar plate, and to follow the evolution of their surface or volume. Using an appropriate statistical modeling framework, for a given microorganism, the doubling time has been estimated for each individual colony, as well as its variability between colonies, both within and between agar plates. A proof of concept led on four bacterial strains of four distinct species demonstrated the feasibility and the interest of the approach. It showed in particular that doubling times derived from early tri-dimensional measurements on microcolonies differed from classical measurements in micro-dilutions based on optical diffusion. Such a precise characterization of the tri-dimensional shape of microcolonies in their late-lag to early-exponential phase could be beneficial in terms of in vitro diagnostics. Indeed, real-time monitoring of the biomass available in a colony could allow to run well established microbial identification workflows like, for instance, MALDI-TOF mass-spectrometry, as soon as a sufficient quantity of material is available, thereby reducing the time needed to provide a diagnostic. Moreover, as done for pre-identification of macro-colonies, morphological indicators such as three-dimensional growth profiles derived from microcolonies could be used to perform a first pre-identification step, but in a shorten time.

Optical forward-scattering for identification of bacteria within microcolonies

The development of methods for the rapid identification of pathogenic bacteria is a major step towards accelerated clinical diagnosis of infectious diseases and efficient food and water safety control. Methods for identification of bacterial colonies on gelified nutrient broth have the potential to bring an attractive solution, combining simple optical instrumentation, no need for sample preparation or labelling, in a non-destructive process. Here, we studied the possibility of discriminating different bacterial species at a very early stage of growth (6 h of incubation at 37 °C), on thin layers of agar media (1 mm of Tryptic Soy Agar), using light forward-scattering and learning algorithms (Bayes Network, Continuous Naive Bayes, Sequential Minimal Optimisation). A first database of more than 1,000 scatterograms acquired on 7 gram-negative strains yielded a recognition rate of nearly 80%, after only 6 h of incubation. We investigated also the prospect of identifying different strains from a same species through forward scattering. We discriminated, thus, four strains of Escherichia coli with a recognition rate reaching 82%. Finally, we show the discrimination of two species of coagulase-negative Staphylococci (S. haemolyticus and S. cohnii), on a commercial selective pre-poured medium used in clinical diagnosis (ChromID MRSA, bioMérieux), without opening lids during the scatterogram acquisition. This shows the potential of this method--non-invasive, preventing cross-contaminations and requiring minimal dish handling--to provide early clinically-relevant information in the context of fully automated microbiology labs.

Development of an integrated optical analyzer for characterization of growth dynamics of bacterial colonies

In order to understand the biophysics behind collective behavior of a bacterial colony, a confocal displacement meter was used to measure the profiles of the bacterial colonies, together with a custom built optical density circuits. The system delivered essential information related to the quantitative growth dynamics (height, diameter, aspect ratio, optical density) of the bacterial colony. For example, the aspect ratio of S. aureus was approximately two times higher than that of E. coli O157 : H7, while the OD of S. aureus was approximately 1/3 higher than that of E. coli O157 : H7.

Determine the quality of human embryonic stem colonies with laser light scattering patterns

Background

With the prompt developments of regenerative medicine, the potential clinical applications of human embryonic stem cells have attracted intense attention. However, the labor-intensive and complex manual cell selection processes required during embryonic stem cell culturing have seriously limited large-scale production and broad applications. Thus, availability of a label-free, non-invasive platform to replace the current cumbersome manual selection has become a critical need.

Results

A non-invasive, label-free, and time-efficient optical platform for determining the quality of human embryonic stem cell colonies was developed by analyzing the scattering signals from those stem cell colonies. Additionally, confocal microscopy revealed that the cell colony morphology and surface structures were correlated with the resulting characteristic light scattering patterns. Standard immunostaining assay (Oct-4) was also utilized to validate the quality-determination from this light scattering protocol. The platform developed here can therefore provide identification accuracy of up to 87% for colony determination.

Conclusions

Our study here demonstrated that light scattering patterns can serve as a feasible alternative approach to replace conventional manual selection for human embryonic stem cell cultures.

Noise-free accurate count of microbial colonies by time-lapse shadow image analysis

Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance.

Portable bacterial identification system based on elastic light scatter patterns

Background

Conventional diagnosis and identification of bacteria requires shipment of samples to a laboratory for genetic and biochemical analysis. This process can take days and imposes significant delay to action in situations where timely intervention can save lives and reduce associated costs. To enable faster response to an outbreak, a low-cost, small-footprint, portable microbial-identification instrument using forward scatterometry has been developed.

Results

This device, weighing 9 lb and measuring 12 × 6 × 10.5 in., utilizes elastic light scatter (ELS) patterns to accurately capture bacterial colony characteristics and delivers the classification results via wireless access. The overall system consists of two CCD cameras, one rotational and one translational stage, and a 635-nm laser diode. Various software algorithms such as Hough transform, 2-D geometric moments, and the traveling salesman problem (TSP) have been implemented to provide colony count and circularity, centering process, and minimized travel time among colonies.

Conclusions

Experiments were conducted with four bacteria genera using pure and mixed plate and as proof of principle a field test was conducted in four different locations where the average classification rate ranged between 95 and 100%.