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.

Surface Environment and Energy Density Effects on the Detection and Disinfection of Microorganisms Using a Portable Instrument.

Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a portable instrument to validate its simultaneous detection and disinfection capability in typical laboratory situations. In this challenging study, three gram-negative and two gram-positive microorganisms were used. For the detection of contamination, inoculations of various concentrations were dispensed on three different surface types to estimate the performance for minimum-detectable cell concentration. Inoculations higher than 103~104 CFU/mm2 and 0.15 mm of detectable contaminant size were estimated to generate a sufficient level of fluorescence signal. The evaluation of disinfection efficacy was conducted on three distinct types of surfaces, with the energy density of UVC light (275-nm) ranging from 4.5 to 22.5 mJ/cm2 and the exposure time varying from 1 to 5 s. The study determined the optimal energy dose for each of the microorganisms species. In addition, surface characteristics may also be an important factor that results in different inactivation efficacy. These results demonstrate that the proposed portable device could serve as an in-field detection and disinfection unit in various environments, and provide a more efficient and user-friendly way of performing disinfection on large surface areas.

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns.

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications.

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification.

We present a smartphone-based bacterial colony phenotyping instrument using a reflective elastic light scattering (ELS) pattern and the resolving power of the new instrument. The reflectance-type device can acquire ELS patterns of colonies on highly opaque media as well as optically dense colonies. The novel instrument was built using a smartphone interface and a 532 nm diode laser, and these essential optical components made it a cost-effective and portable device. When a coherent and collimated light source illuminated a bacterial colony, a reflective ELS pattern was created on the screen and captured by the smartphone camera. The collected patterns whose shapes were determined by the colony morphology were then processed and analyzed to extract distinctive features for bacterial identification. For validation purposes, the reflective ELS patterns of five bacteria grown on opaque growth media were measured with the proposed instrument and utilized for the classification. Cross-validation was performed to evaluate the classification, and the result showed an accuracy above 94% for differentiating colonies of E. coli, K. pneumoniae, L. innocua, S. enteritidis, and S. aureus.

Detection of Salmonella Typhimurium with Gold Nanoparticles Using Quartz Crystal Microbalance Biosensor.

Demonstration of the Salmonella Typhimurium detection system was shown utilizing a quartz crystal microbalance (QCM) biosensor and signal enhancement by gold nanoparticles. In this study, a benchtop system of a QCM biosensor was utilized for the detection of Salmonella Typhimurium. It was designed with a peristaltic pump system to achieve immobilization of antibodies, detection of Salmonella, and the addition of gold nanoparticles to the sensor. As a series of biochemical solutions were introduced to the surface, the proposed system was able to track the changes in the resonant frequency which were proportional to the variations of mass on the sensor. For antibody immobilization, polyclonal antibodies were immobilized via self-assembled monolayers to detect Salmonella O-antigen. Subsequently, Salmonella Typhimurium was detected by antibodies and the average frequency before and after detecting Salmonella was compared. The highest frequency shifts were −26.91 Hz for 109 CFU/mL while the smallest frequency shift was −3.65 Hz corresponding to 103 CFU/mL. For the specificity tests, non-Salmonella samples such as E. coli, Listeria, and Staphylococcus resulted in low cross-reactivity. For signal amplification, biotinylated antibodies reacted to Salmonella followed by streptavidin­100 nm AuNPs through biotin-avidin interaction. The frequency shifts of 103 CFU/mL showed −28.04 Hz, and consequently improved the limit of detection.

Design and Validation of a Portable Machine Learning-Based Electronic Nose.

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography-mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

Detection of E. coli labeled with metal-conjugated antibodies using lateral-flow assay and laser-induced breakdown spectroscopy.

This study explores the adoption of laser-induced breakdown spectroscopy (LIBS) for the analysis of lateral-flow immunoassays (LFIAs). Gold (Au) nanoparticles are standard biomolecular labels among LFIAs, typically detected via colorimetric means. A wide diversity of lanthanide-complexed polymers (LCPs) are also used as immunoassay labels but are inapt for LFIAs due to lab-bound detection instrumentation. This is the first study to show the capability of LIBS to transition LCPs into the realm of LFIAs, and one of the few to apply LIBS to biomolecular label detection in complete immunoassays. Initially, an in-house LIBS system was optimized to detect an Au standard through a process of line selection across acquisition delay times, followed by determining limit of detection (LOD). The optimized LIBS system was applied to Au-labeled Escherichia coli detection on a commercial LFIA; comparison with colorimetric detection yielded similar LODs (1.03E4 and 8.890E3 CFU/mL respectively). Optimization was repeated with lanthanide standards to determine if they were viable alternatives to Au labels. It was found that europium (Eu) and ytterbium (Yb) may be more favorable biomolecular labels than Au. To test whether Eu-complexed polymers conjugated to antibodies could be used as labels in LFIAs, the conjugates were successfully applied to E. coli detection in a modified commercial LFIA. The results suggest interesting opportunities for creating highly multiplexed LFIAs. Multiplexed, sensitive, portable, and rapid LIBS detection of biomolecules concentrated and labeled on LFIAs is highly relevant for applications like food safety, where in-field food contaminant detection is critical. Graphical abstract.

Design and application of a portable luminometer for bioluminescence detection.

The silicon photomultiplier (SiPM) for low light detection has many advantages when compared to existing photon counting detectors, such as high sensitivity, low cost, robustness, and compact hardware. To facilitate the use of SiPM as a portable, field deployable device, an electrical circuit was designed consisting of an amplifier, comparator, and microcontroller. In addition, a 3D printing was used to create a portable cradle for housing the SiPM. To evaluate its detection ability, a laser experiment and bioluminescent experiments, including Pseudomonas fluorescens M3A detection, E. coli O157:H7 PhiV10nluc lysogen detection, and a luminescence-based detection of E. coli O157:H7 in ground meat using the engineered luminescent-based reporter phage PhiV10nluc, were conducted. In the same experimental setting, our previously developed smartphone-based luminometer called the bioluminescent-based analyte quantitation by smartphone and a conventional photomultiplier tube-based benchtop luminometer were used to compare detection levels and applicability for supporting luminescent phage-based pathogen detection. Results showed that the SiPM provides better performance in terms of time to detection and SNR and could be used as the light detection component of the PhiV10nluc phage-based detection format.

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

UNLABELLED: 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.

Scalar diffraction modeling of multispectral forward scatter patterns from bacterial colonies.

A theoretical model for spectral forward scatter patterns from a bacterial colony based on elastic light scatter is presented. The spectral forward scatter patterns are computed by scalar diffraction theory, and compared with experimental results of three discrete wavelengths (405 nm, 635 nm, and 904 nm). To provide quantitative analysis, spectral dependence of diffraction ring width, gap, maxima, minima, and the first deflection point are monitored. Both model and experiment results show an excellent agreement; a longer wavelength induces a wider ring width, a wider ring gap, a smaller pattern size, and smaller numbers of rings. Further analysis using spatial fast Fourier transform (SFFT) shows a good agreement; the spatial frequencies are increasing towards the inward direction, and the slope is inversely proportional to the incoming wavelength.

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.

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System.

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Label-free identification of bacterial microcolonies via elastic scattering.

Label-free microcolony identification via elastic light scattering was investigated for three different genera: Salmonella enterica serovar Montevideo, Listeria monocytogenes F4244, and Escherichia coli DH5α. Microcolonies were defined as bacterial colonies in their late-lag phase to early-exponential phase with the diameter range of 100-200 µm. To link biophysical characteristics with corresponding scattering patterns, a phase contrast microscope and a confocal displacement meter were used to measure the colony diameter and its 3D height profile. The results indicated that the growth characteristics of microcolonies were encoded in their morphologies which correlated to the characteristic diffraction patterns. Proposed methodology was able to classify three genera based on comprehensive phenotypic map which incorporated growth speed, ring count, and colony diameter. While the proposed method illustrated the possibility of discriminating microcolonies in their early growth stage, more thorough biophysical understanding is needed to expand the technology to other species.

Application of sampling criterion on numerical diffraction from bacterial colonies.

Numerical diffraction from a bacterial colony was investigated from the viewpoint of applying the sampling criterion for both spatial and frequency domains. Once the morphology information of a bacterial colony was given, the maximum diffraction angle was estimated to reveal the minimum and maximum length of both the imaging and aperture domains. Scalar diffraction modeling was applied to estimate the diffraction pattern, which provided that two phase functions were contributing to the phase modulation: chirp and Gaussian phase functions. Optimal sampling intervals for both phase functions were investigated, and the effect of violating these conditions was demonstrated. Finally, the Fresnel approximation was compared to the angular spectrum method for accuracy and applicability, which then revealed that the Fresnel approximation was valid for both large imaging distances and longer wavelengths.

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp.

Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 104 CFU/mL commercially available lateral-flow assays.

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.

Smartphone-based lateral flow imaging system for detection of food-borne bacteria E.coli O157:H7.

We report an application for the smartphone as an accurate and unbiased reading platform of a lateral flow immunoassays for food safety application. In particular, this report focuses on detection of food-borne bacteria in samples extracted from food matrices such as ground beef and spinach. The lateral flow assay is a widely accepted methodology owing to its on-site results, low-cost analysis, and ease of use with minimum user inputs, even though sensitivity is not quite equivalent to that of standard laboratory equipment. An antibody-antigen relationship is transduced into a color change on a nitrocellulose pad while visual interpretation of this color change can result in uncertainty, particularly near the detection limit of the assay. Employing the high resolution integrated camera, constant illumination from light source, and computing power of a smartphone, we provide an objective and accurate method to determine the bacterial cell concentration in a food matrix based on the regression model from the color intensity of test lines. A 3D-printed sample holder was designed for representative commercial lateral flow assays and an in-house application was developed in Android Studio to solve the inverse problem to provide cell concentration information from the color intensity. Test results with E.coli O157:H7 as a model organism suggests that smartphone-based reader can detect 104-105 CFU/ml from ground beef and spinach food matrices.

Generalized spectral light scatter models of diverse bacterial colony morphologies.

An optical forward-scatter model was generalized to encompass the diverse nature of bacterial colony morphologies and the spectral information. According to the model, the colony shape and the wavelength of incident light significantly affect the characteristics of a forward elastic-light-scattering pattern. To study the relationship between the colony morphology and the scattering pattern, three-dimensional colony models were generated in various morphologies. The propagation of light passing through the colony model was then simulated. In validation of the theoretical modeling, the scattering patterns of three bacterial genera, Staphylococcus, Exiguobacterium and Bacillus, which grow into colonies having convex, crateriform and flat elevations, respectively, were qualitatively compared to the simulated scattering patterns. The strong correlations observed between simulated and experimental patterns validated the scatter model. In addition, spectral effect on the scattering pattern was studied using the scatter model, and experimentally investigated using Staphylococcus, whose colony has circular form and convex elevation. Both simulation and experiment showed that changes in wavelength affected the overall pattern size and the number of rings.

A Portable Spark-Induced Breakdown Spectroscopic (SIBS) Instrument and its Analytical Performance.

A compact spark-induced plasma spectroscopic device was developed to detect elements used in a variety of applications. The system consists of a spark generator connected to tungsten electrodes, a custom-built delay generator, and two spectrometers that together cover the ultraviolet visible (UV-Vis) range (214-631 nm). The system was evaluated by qualitatively and quantitatively sampling copper standards. Prominent spectral peaks were identified using the NIST database for atomic emissions. The effectiveness of the proposed system was also tested with a lanthanide sample (gadolinium) and provided qualitative identification of the characteristic peaks. A semi-quantitative measurement for silicon and gold was performed using variable amounts of each particulate. Silica microbeads in solution were applied to paper wafers, while gold nanoparticles were sputter-coated onto silicon wafers. Results showed a positive correlation between the intensity of the signal and the concentration of each type of particulate. The variation of signal intensity was investigated to determine the repeatability, and the coefficient of variation was lowered from 60% to 25% after averaging measurements of multiple ablations per observation.