In the realm of food safety, the standard practice involves collecting food product samples and sending them to a central laboratory for microbiological testing. However, this process introduces delays in obtaining the microbiological testing results and subsequently affects the timely delivery of food products to consumers. To further reduce the time-to-detection issue, we propose the development of a self-contained, battery-operated, high-sensitivity optical sensor that can be affixed to the cap of the typical food sample collection container. This device, called MPACT, offers real-time and in-transit monitoring of the contamination status of the food sample, specifically targeting E. coli O157:H7, through a bioluminescence assay. The assay exclusively targets the target pathogen and, when detected, produces minimal luminescence. As the sample is transported in the container, the number of bacterial cells multiplies, and once the luminescent signal reaches a predefined threshold, the sensor reports the results via Bluetooth. This study focuses on the design of the bottle cap sensor and examines its sensitivity by subjecting it to bioluminescence samples.
Salmonella ser. Typhimurium is notorious for causing serious foodborne illnesses and presenting considerable public health risks. The study introduces an innovative system based on a quartz crystal microbalance, designed to detect the target pathogen by integrating the system around a smartphone. The system operates through a dual-mode approach, relying on two distinct mechanisms: measuring frequency changes due to variations in bacterial mass and quantifying fluorescence intensities resulting from bacteria captured by FITC-labeled antibodies. Incorporating FITC-labeled antibodies not only enhances the resonance frequency shift but also offers visual confirmation through the fluorescence signal. The integration of the quartz crystal microbalance system with a smartphone enables real-time monitoring. This system displays both frequency and temperature data, while also capturing fluorescence intensities to estimate the concentration of the target analyte. The smartphone-based system successfully detected Salmonella Typhimurium within a concentration range of 105 CFU/mL after the application of FITC-labeled antibodies. This portable QCM system represents a promising advancement in pathogen detection, holding significant potential to improve food safety protocols and strengthen public health safeguards.
With the development and expansion of the internet of things, many scientific and engineering instruments are leaving the benchtop restriction and moving on to provide on-site detection. On-site detection requires a complete miniaturization of a benchtop system while maintaining a similar performance with respect to the analyte detection sensitivity. In addition, due to the mobile nature, utilizing a battery source is required. Here we present a portable loop-medicated isothermal amplification detection system for on-site detection and amplification of target analyte via fluorescence detection. The digital twin design incorporates three major components: an isothermal heating chamber, light-tight enclosure for sample insert, and fluorescence imaging system via micro-controllers. The isothermal heating chamber was designed with Peltier heater to provide small form factor accurate temperature control. For light-tight enclosure is a 3D printed device that allows DNA samples to be inserted and fluorescent images to be taken within the chamber. Lastly, fluorescent imaging system operates with a stand-alone camera connected to an Arduino micro-controller. Excitation is provided by blue colored LED and emission is detected via long-pass filter that matches the emission spectrum.
The consumption of mycotoxins generated by fungi can have severe effects on the health of both humans and animals. These toxins can exist at dangerous levels in food products made from crops that have been infected with mycotoxinproducing fungi. Numerous methods have been developed for detecting mycotoxins in order to divert contaminated commodities from the food supply, but only allow for reactive, not preventive approaches. Furthermore, under favorable conditions toxin-producing fungi can continue to produce mycotoxins during storage and throughout the crop processing stages. By identifying mycotoxin-producing fungal species on crops or commodities, remediation such as fungicide application can be carried out, preventing the spread of infection and potential contamination of healthy crops, reducing waste of resources and ultimately improving food safety. Loop-mediated isothermal amplification (LAMP) has advantages for portable DNA detection due to its isothermal nature, resistance to matrix inhibitors, and the possibility of a long shelflife when reagents are dried onto a matrix. The developed microfluidic device allows for the homogenized wheat sample input after DNA extraction. The microfluidic device functions as a disposable cassette and can be heated by an independent, portable, isothermal heating device. The LAMP assay is combined with calcein for fluorescence detection. In this experiment, Fusarium graminearum, a trichothecene mycotoxin producer, was used as a proof-of-concept for the device with a LAMP assay targeting the gaoA gene, which codes for the enzyme galactose oxidase (GO), a unique enzyme produced by only a few other fungal species. The presence of Fusarium graminearum was detected in contaminated wheat samples utilizing the described methods, indicating the potential detection of mycotoxin-producing fungi. In the future, the device will be expanded to test for multiple mycotoxin-producing genes.
Recently, the use of a Quartz Crystal Microbalance (QCM) as a biosensor for detecting foodborne pathogens by observing changes in resonant frequency has gained popularity. However, conventional detection methods are time-consuming and require expensive equipment and trained personnel. The current trend is toward detection approaches that are quick, portable, and easy to use. In order to address this need, a dual-modality QCM system combining a smartphone, an in-situ fluorescence imaging subsystem, and a flow injection component has been proposed. This system enables a smartphone to receive real-time frequency data via Bluetooth, while a camera detects the presence of bacteria on the quartz crystal surface using a fluorescence-tagged antibody. The fluorescence imaging subsystem utilizes a camera to capture the bacteria fluorescence signal, while the flow injection subsystem employs a mini peristaltic pump and controller to introduce biochemical solutions, antibodies, and bacteria. All components are contained in a 3D cartridge that is portable. FITC images were captured with 5 MHz quartz crystals when the prototype system was tested. The developed QCM biosensor has the potential to become a portable bacteria detection approach that outperforms existing techniques.
Due to the increasing complexity of the food supply chain, the likelihood of food adulteration or contamination is a significant problem for food safety. Agricultural products may be chemically, physically, or biologically manipulated at numerous points along the supply chain. To address this issue and improve food safety, it is necessary to implement innovative measurement technologies for biohazard detection. Currently, accessible food analysis techniques include vibrational spectroscopy and mass spectrometry. However, optical techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman systems are gaining popularity due to their real-time analysis capabilities and minimal requirements for sample preparation. In this study, we combined LIBS and Raman detection to analyze the elemental and molecular composition of various food matrices for the purpose of detecting food contamination in real time. We examined typical generic herbicides containing glyphosate, such as Roundup. The samples were spiked by spraying the chemical compound on the surface of fruits. The results revealed that it is possible to assess complex food matrices polluted with widespread organic contaminants by combining optical spectroscopies very rapidly.
Accumulating evidence suggests that cytokine storm syndrome (CSS) induced by the SARS-CoV-2 may be the ultimate cause of acute respiratory distress syndrome (ARDS), resulting in severe outcomes of COVID-19 infection and potentially death. Elevated levels of serum interleukin 6 (IL-6) correlate with the occurrence of respiratory failure, ARDS, and adverse clinical outcomes in many COVID-19 patients. The currently available clinical cytokine tests are costly, time-consuming, and require skilled technicians to execute. There is an unmet need for rapid, affordable, robust, and sensitive tests for cytokine levels. Therefore, this study aimed to develop a cost-effective system for quantitative detection of cytokines that can be used in the point-of-care (POC) format within a few minutes of blood collection. Our approach combines detection based on laser-induced breakdown spectroscopy with a lateral flow immunoassay (LIBS-LFIA) to deliver a quantitative clinical analysis platform with multiplexing capability. Lanthanide-complexed polymers (LCPs) were selected as the labels to provide optimal quantitative performance when sensing signals from the test lines of LFIAs. For a prototype implementation and a proof-of-concept, we targeted IL-6 as it is one of the most critical pro-inflammatory cytokines. Our initial LIBS-LFIA biosensor achieved a limit of detection (LOD) of 0.2298 μg/mL of IL-6 within 15 minutes and further sensitivity increase is possible with optimization. Regardless, since high levels of IL-6 are reported for patients in crisis, this is more than adequate to identify patients with highly elevated cytokine levels. Our research provides evidence that rapid and accurate detection of cytokines for clinical diagnosis and prognosis of COVID-19 and other pathogenic infections using LIBS is highly feasible and compatible with the POC format.
A portable bacterial colony classification tool based on colonies’ reflective elastic light-scatter (ELS) patterns has been developed using a smartphone, a green laser, and a projection screen material. As the collimated beam from the laser illuminates the bacterial colony, backscattered photons interfere and generate a unique pattern on the screen material determined by the unique morphology of the colony. The phone camera, which is located behind the screen, captures the pattern. The collected patterns are utilized to extract the distinctive scatter-related features across different organisms for the classification process. Unlike other tools that use transmitted ELS patterns, the novel device measures the reflective signal, and therefore this ELS technique can be applied to organisms that are grown on opaque media such as blood agar, chocolate agar, which normally prohibits the transmission of the light and generation of forward ELS patterns. The adaptation of the smartphone camera as an imaging device dramatically reduced the system to a palm-size instrument. This made it wholly portable and easy to carry. For validation of the instrument, two different bacteria species, E. coli and L. innocua were grown on opaque agar media and tested. The results showed over 90% of overall accuracy in differentiating the organisms.
We developed a hyperspectral elastic light scatter (ELS) phenotyping instrument to explore the relationship between the wavelength of the incident beam and the elastic light-scatter patterns of a bacterial colony, and, ultimately, to enhance the classification efficiency of non-invasive ELS-based systems employed in microbiology. The new instrument consists of a supercontinuum (SC) laser and acousto-optic tunable filter (AOTF), which enables the selection of the wavelength of interest allowing multiple spectral patterns in a single measurement. An initial experiment with microflora found on green leafy vegetables derived an encouraging result, showing over 90% of average classification accuracy when classifying colonies from two different bacterial species utilizing 70 spectral bands from SC-laser. This observation suggested the notion that colonies of varied species may form distinguishable scatter features at separate different spectral bands. The increase in the number of employed bands consequently led to the rise in the number of scatter pattern features, potentially resulting in the classifier's overfitting and decrease in real-life accuracy. Therefore, the presented hyperspectral ELS system employs feature reduction and selection procedures to enhance the robustness and ultimately lessen the complexity of data collection.
Due to its ease of sample preparation and rapid processing speed, laser-induced breakdown spectroscopy (LIBS) has emerged as a promising new technique for food analysis. Food adulteration detection is critical for fair trade and protecting customers from food fraud. As a result, there is a high demand for a rapid and portable detection method for authenticating and evaluating the safety of marketed food and beverage products. An increased prevalence of food fraud which frequently entails the substitution of inferior ingredients for high-quality products has necessitated the development of innovative measures for detecting and preventing fraud. In this report, we describe an authentication approach utilizing a custom designed benchtop LIBS system. We focused on high-value regional food products such as European alpine-style cheeses and Italian balsamic vinegars. Liquid samples were measured on paper without any pretreatment, and solid samples were ablated directly on the sample surface by LIBS. The pre-processed LIBS spectra were utilized for training and validating various classifiers for sample categorization and validation. The development of an elastic net (ENET) classification model is also reported in the study. In summary, our research highlighted the potential of the LIBS technique combined with chemometric methods for solid and liquid high-value food authenticity certification. The results show that LIBS enables rapid analysis and accurate food sample classification without the requirement for sample pretreatment.
Infection with foodborne pathogens such as Salmonella spp. is of high risk for people with a weakened immune system. Microbiological culture method has been used in general for detection of pathogens from the food matrix; however, it is time consuming and requires experience and good level of laboratory skills. In the food safety field, various techniques which allows the rapid and simple detection have been developed at the level of a user-friendly tool for detecting the foodborne pathogens. Quartz crystal microbalance (QCM) are mass-based biosensor which measures the microgram level mass changes, enabling a user to observe the presence of the pathogen simply and rapidly. When the pathogens are bound on vibrating quartz surface, the resonant frequency of a quartz crystal will be changed due to the mass of the pathogens. In this study, the QCM detected killed Salmonella Typhimurium in the range of 〖10〗^5-〖10〗^9 CFU/mL, correlating to the averaged frequency shifts. The actual concentrations of Salmonella from the culture method were compared to the difference in the resonant frequency. The QCM sensor were treated with 11-Mercaptoundecanoic acid (11-MUDA), and EDC-NHS following by antibodies and bovine serum albumin (BSA) to utilize the antibody-antigen reaction. With a usage of peristaltic pump, the solutions could be introduced to the surface while frequencies could be monitored for each step in real-time. To acquire the evidence of Salmonella, the surfaces of the quartz crystal with the fluoresce labeled antibody were captured by the fluorescence microscope. The QCM biosensor showed the possibility of detection of Salmonella in less time, compared with the conventional method.
Rapid detection and disinfection of microbial contamination is an ongoing concern across various food processing industries. Numerous methods exist for detection, including nucleic acid-based, fluorescence microscopy, and immunological-based tests. There is an emerging interest in using optical techniques to perform detection and disinfection simultaneously. This study reports on experimental results of energy density effects on disinfection of gram-negative organisms using a commercially available portable device. Three different gram-negative organisms were cultured and diluted over a four-log range. Samples of different concentrations were plated and exposed to UVC with increasing energy densities. A summary of the disinfection rate is presented. We identified an appropriate energy density condition that was required depending upon the concentration and type of microorganisms. The results showed that the tested portable device could serve be a valuable alternative for in-field screening and disinfection.
Fungal species such as Aspergillus, Fusarium and Alternaria can contaminate agricultural commodities in the field or during storage and produce mycotoxins. They usually pose threats to human and animal health and can result in significant economic loss. Specifically, Fusarium graminearum, the major causative agent of Fusarium head blight (FHB) of small cereals produces mycotoxins including deoxynivalenol, nivalenol, and zearalenone. Conventional detection methods are time-consuming, expensive and require large-scale instruments and skilled technicians. Furthermore, detection of the toxins in post-harvested grain is a process that can only be accomplished after the grain is harvested. Therefore, our goal was to develop a molecular point-of-detection (POD) platform which was sensitive and specific to detect low levels of toxin-producing fungi within agricultural products in the field and could also be used directly in food products. Herein, we investigated a rapid molecular POD assay called loop mediated isothermal amplification (LAMP) to detect low levels of genomic DNA extracted from Fusarium graminearum, which is often associated with toxicological potential and food safety issues. Both fluorescent and colorimetric LAMP assays were characterized and optimized to detect low-level of pathogens within 70 and 50 minutes respectively. In summary, LAMP offers an efficient assay format for rapid and specific nucleic acid-based detection of mycotoxins in-field use. Coupled with our custom-designed microchip, our platform provides a proof-of-principle to achieve low-cost and widespread foodborne pathogens testing at the POD which is highly desirable to keep analysis time and costs low, but more importantly be a field use application.
Quartz crystal microbalance (QCM) sensors have been applied to detect foodborne pathogens such as Salmonella Typhimurium, E.coli O157:H7, and Campylobacter jejuni. As pathogens are placed on vibrating quartz surface, the change in mass of the pathogens affects the characteristic of a QCM. The presence of pathogens that antibody captures can be correlated to the shift in resonance frequency. Thus, theoretical description is necessary to understand the relationship between the change in frequency and mass. In this work, the relationship between theoretical and experimental results is examined by comparing the frequency shift caused by different type of liquids. In general, a QCM can be represented by a Butterworth-Van-Dyke (BVD) circuit made up of resistance R, inductance L, and capacitance C. With physical properties of quartz, viscosity-density product of the liquid has an effect on inductance as well as resistance. As a preliminary experiment, measurements of mixtures of water and glycerol were conducted to evaluate results from the different levels of viscosity and density. The results of the experiments showed that higher viscosity and density resulted in lower resonant frequencies. With regard to theoretical calculation, increase of R and L resulted in a proportional increase in the square root of the viscosity-density product. Increased lumped parameters explains the decreased resonant frequency. Therefore, the shift of the resonant frequency of the load and unloaded QCM could be calculated based on the admittance from circuit components. Blank (air) sample, water, glycerol and water mixture have shown proportional shift in the resonant frequencies. The experiments and theoretical model were matched within reasonable range. The average difference between the theory and the experiments (Matlab/FEM model) was 7.04 %.
Arcobacter (formerly classified as Campylobacter spp.) are curved-to helical, Gram-negative, aerobic/microaerobic bacteria increasingly recognized as human and animal pathogens. In collaboration with Lincoln and Purdue University, we report the first experimental result of laser-based classification method of bacterial colonies of these species. This technology is based on elastic light scatter (ELS) phenomena where incident laser interacts with the whole volume of the colony and generates a unique fingerprint laser pattern. Here we report a novel development and application of deep learning algorithm to classify the scatter patterns of Arcobacter species using variational autoencoders (VAE). VAE creates set of normal distributions. Each of these distributions are responsible for certain properties of the original images. We used VAE to identify features in the features space for several hundred images which includes size of the colony based on scatter size, intensity of the image, and, the number of rings within the image, and so on. Thus each sample within our image database can be coded with sets of features that facilitates fast preliminary search for similar images allowing clustering of similar patterns in feature space. In addition, such initial selection could assist in identifying non-bacterial scatter patterns (i.e. bubbles or dust spots in the agar), or doublets where two colonies are overlapping during the acquisition time thus removing non-biological artifacts prior to analysis. An interesting result was that while VAE created far more realistic synthetic images closer to the original image, a simple autonencoder resulted in better cluster separation.
Laser-induced breakdown spectroscopy (LIBS) is a technique developed in the last few decades for simultaneous multi-element characterization of various materials. Multiplexed detection of analytes is particularly useful in the realm of food contaminant detection, where the contaminant can be one or a combination of adulterants. Paper-based assays are an emerging platform for food-contaminant detection. However, most paper-based assays do not perform multiplexed detection. For food contaminant outbreak prevention and remediation, rapid multiplexed detection could make a difference in response speed. This study applies LIBS to the concept of multi-analyte detection on paper-based bioassays. In the envisioned bioassay, a variety of analytes are labeled with unique lanthanides, a technique common to the well-established field of mass cytometry. The presence of single or multiple lanthanide labels indicates the presence of single or multiple types of contaminants. We aim to implement LIBS for multiplexed detection of lanthanide labels. To investigate data analysis approaches for multi-lanthanide detection, we evaluate univariate data analysis and spectral unmixing approaches on samples containing combinations of europium, dysprosium, gadolinium, praseodymium, and neodymium. We find that the intense signal generated by Eu, matrix effects, selfabsorption, and spectral overlap affect the outcome of the results. Future studies will continue the investigation to identify the most appropriate approach.
Luminescence based detection has been widely used in diverse science and engineering applications. The recent development of the smartphone has enabled end users to utilize this communication device as a portable detector and instruments such as a microscope, fluorimeter, colorimeter, and spectrometer. To transform the smartphone into a bioluminescence detector, our group developed an advanced signal processing algorithm and an optical chamber designed for efficient photon capture. This solution was required to overcome the typical sensitivity of the CMOS-based smartphone camera such that sub-nano to pico Watt levels of power can be measured with conventional smartphones. Preliminary experiments conducted with the bioluminescent Pseudomonas fluorescens M3A shows a detection limit of approximately 106 CFU/ml. To achieve sensitive detection while maintaining the portability, we explored using the recently developed silicon photomultiplier (SiPM), and designed a portable bioluminescence sensor which shows a 2-3 order higher sensitivity on calibration sample testing. Finally, for live sample testing, Escherichia coli O157:H7 was inoculated on a ground beef sample and subjected to luminescence phage based detection and a luminescence signal was generated from the bacteriophage infection and detected within 8-10 h after spiking.
KEYWORDS: Cameras, Computer aided design, Bacteria, RGB color model, Imaging systems, Camera shutters, Chemical analysis, Agriculture, Matrices, 3D modeling
We report an application of the smartphone as an accurate and unbiased reading platform of lateral flow assay. In particular, this report focuses on detection of food-borne bacteria from samples extracted from various food matrices. Lateral flow assay is widely accepted methodology due to its on-site result and low-cost analysis even though sensitivity is not as good as standard laboratory equipment. Antibody-antigen relationship is translated into a color change on the nitrocellulose pad and interpretation of this color change causes obscurity, particularly around the detection limit of the assay. Based on its integrated camera and computing power, we provide an objective and accurate method to determine the bacterial cell concentration from the food matrix based on the regression model based on the bacterial concentration and RGB channel color changes. 3-D printed sample holder was designed for one of the representative commercial lateral flow assay and in-house application was developed in Android studio that solves the inverse problem instantly to provide cell concentration to the user.
Based on its integrated camera, new optical attachment, and inherent computing power, we propose an instrument design and validation that can potentially provide an objective and accurate method to determine surface meat color change and myoglobin redox forms using a smartphone-based spectrometer. System is designed to be used as a reflection spectrometer which mimics the conventional spectrometry commonly used for meat color assessment. We utilize a 3D printing technique to make an optical cradle which holds all of the optical components for light collection, collimation, dispersion, and a suitable chamber. A light, which reflects a sample, enters a pinhole and is subsequently collimated by a convex lens. A diffraction grating spreads the wavelength over the camera’s pixels to display a high resolution of spectrum. Pixel values in the smartphone image are translated to calibrate the wavelength values through three laser pointers which have different wavelength; 405, 532, 650 nm. Using an in-house app, the camera images are converted into a spectrum in the visible wavelength range based on the exterior light source. A controlled experiment simulating the refrigeration and shelving of the meat has been conducted and the results showed the capability to accurately measure the color change in quantitative and spectroscopic manner. We expect that this technology can be adapted to any smartphone and used to conduct a field-deployable color spectrum assay as a more practical application tool for various food sectors.
A phenotyping of bacterial colonies on agar plates using forward-scattering diffraction-pattern analysis provided promising classification of several different bacteria such as Salmonella, Vibrio, Listeria, and E. coli. Since the technique is based on forward-scattering phenomena, light transmittance of both the colony and the medium is critical to ensure quality data. However, numerous microorganisms and their growth media allow only limited light penetration and render the forward-scattering measurement a challenging task. For example, yeast, Lactobacillus, mold, and several soil bacteria form colorful and dense colonies that obstruct most of the incoming light passing through them. Moreover, blood agar, which is widely utilized in the clinical field, completely blocks the incident coherent light source used in forward scatterometry. We present a newly designed reflection scatterometer and validation of the resolving power of the instrument. The reflectance-type instrument can acquire backward elastic scatter patterns for both highly opaque media and colonies and has been tested with three different bacterial genera grown on blood agar plates. Cross-validation results show a classification rate above 90% for four genera.
Typical bioterrorism prevention scenarios assume well-known and well-characterized pathogens like anthrax or
tularemia, which are serious public concerns if released into food and/or water supplies or distributed using other
vectors. Common governmental contingencies include rapid response to these biological threats with predefined
treatments and management operations. However, bioterrorist attacks may follow a far more sophisticated route. With
the widely known and immense progress in genetics and the availability of molecular biology tools worldwide, the
potential for malicious modification of pathogenic genomes is very high. Common non-pathogenic microorganisms
could be transformed into dangerous, debilitating pathogens. Known pathogens could also be modified to avoid
detection, because organisms are traditionally identified on the basis of their known physiological or genetic properties.
In the absence of defined primers a laboratory using genetic biodetection methods such as PCR might be unable to
quickly identify a modified microorganism. Our concept includes developing a nationwide database of signatures based
on biophysical (such as elastic light scattering (ELS) properties and/or Raman spectra) rather than genetic properties of
bacteria. When paired with a machine-learning system for emerging pathogen detection these data become an effective
detection system. The approach emphasizes ease of implementation using a standardized collection of phenotypic
information and extraction of biophysical features of pathogens. Owing to the label-free nature of the detection
modalities ELS is significantly less costly than any genotypic or mass spectrometry approach.
The majority of tools for pathogen sensing and recognition are based on physiological or genetic properties
of microorganisms. However, there is enormous interest in devising label-free and reagentless biosensors that
would operate utilizing the biophysical signatures of samples without the need for labeling and reporting biochemistry.
Optical biosensors are closest to realizing this goal and vibrational spectroscopies are examples of
well-established optical label-free biosensing techniques. A recently introduced forward-scatter phenotyping
(FSP) also belongs to the broad class of optical sensors. However, in contrast to spectroscopies, the remarkable
specificity of FSP derives from the morphological information that bacterial material encodes on a coherent
optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given
a constant environment, are unique to each bacterial species and/or serovar. Both FSP technology and spectroscopies
rely on statistical machine learning to perform recognition and classification. However, the commonly
used methods utilize either simplistic unsupervised learning or traditional supervised techniques that assume
completeness of training libraries. This restrictive assumption is known to be false for real-life conditions, resulting
in unsatisfactory levels of accuracy, and consequently limited overall performance for biodetection and
classification tasks. The presented work demonstrates preliminary studies on the use of FSP system to classify
selected serotypes of non-O157 Shiga toxin-producing E. coli in a nonexhaustive framework, that is, without
full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to
learning with a nonexhaustive training dataset to allow for the automated and distributed detection of unknown
bacterial classes.
To experimentally analyze the morphological characteristics and to predict the resulting scattering patterns of different
bacterial colonies, an optical morphology analyzer was constructed based on a laser confocal displacement meter to
simultaneously obtain the optical properties of colonies. The profile data was accurately captured using the confocal
laser triangulation technology and the transmitted light was collected by a photodiode circuit. The analog signals were
read into a data acquisition board in parallel for off-line signal processing. This approach showed promising results for
differentiation of micro-colonies in the range of 100~300 μm based on the morphological differences among different
species using light scattering.
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.
Light scattering is one of the most fundamental optical processes whereby electromagnetic waves are forced to deviate from
a straight trajectory by non-uniformities in the medium that they traverse. This presentation summarizes our recent research
on application of light-scatter measurements paired with machine learning and pattern recognition methodologies for label-free
classification of bioparticles. Two separate examples of light scatter-based techniques are discussed: forward-scatter
measurements of bacterial colonies in an imaging system, and flow cytometry measurements of scatter signals formed by
individual bacterial particles.
Recently, we have reported a first practical implementation of a system capable of label-free classification and recognition
of pathogenic species of Listeria, Salmonella, Vibrio, Staphylococcus, and E. coli using forward-scatter patterns
produced by bacterial colonies irradiated with laser light. Individual bacteria in flow also form complex patterns dependent
on particle size, shape, refraction index, density, and morphology. Although commercial flow cytometers allow scatter
measurement at two angles this rudimentary approach cannot be used to separate populations of bioparticles of similar
shape, size, or structure. The custom-built system used in the presented work collects axial light-loss and scatter signals
at five carefully chosen angles. Experimental results obtained from colony scanner, as well from the extended cytometry
instrument, were used to train the pattern-recognition algorithm. The results demonstrate that information provided by
scatter alone may be sufficient to recognize various bioparticles with 90-99% success rate, both in flow and in imaging
systems.
Early detection and classification of pathogenic bacteria species is crucial to food safety. The previous BARDOT
(BActeria Rapid Detection by using Optical light scattering Technology) system is capable of classifying the bacterial
colonies of around 1~1.5mm diameter within 24~36 hours of incubation. However, in order to further reduce the
detection time and synchronize the detection operation with the bacterial cultivation, a micro-incubator is developed that
not only grows bacteria at 37°C but also enables forward scatterometry. This new design feature enables us to
continuously characterize the light scattering patterns of the bacterial colonies throughout their growing stages. Some
experimental results from this new system are demonstrated and compared with the images obtained from phase contrast
microscopy and a confocal displacement meter to show the possibility of earlier identification of bacteria species.
Moreover, this paper also explains the updated optical and mechanical modules for the beam waist control to
accommodate the smaller bacteria colony detection.
In order to maximize the utility of the optical scattering technology in the area of bacterial colony identification, it is
necessary to have a thorough understanding of how bacteria species grow into different morphological aggregation and
subsequently function as distinctive optical amplitude and phase modulators to alter the incoming Gaussian laser beam.
In this paper, a 2-dimentional reaction-diffusion (RD) model with nutrient concentration, diffusion coefficient, and agar
hardness as variables is investigated to explain the correlation between the various environmental parameters and the
distinctive morphological aggregations formed by different bacteria species. More importantly, the morphological
change of the bacterial colony against time is demonstrated by this model, which is able to characterize the spatio-temporal
patterns formed by the bacteria colonies over their entire growth curve. The bacteria population density
information obtained from the RD model is mathematically converted to the amplitude/phase modulation factor used in
the scalar diffraction theory which predicts the light scattering patterns for bacterial colonies. The conclusions drawn
from the RD model combined with the scalar diffraction theory are useful in guiding the design of the optical scattering
instrument aiming at bacteria colony detection and classification.
The formation of bacterial colonies and biofilms requires coordinated gene expression, regulated cell differentiation,
autoaggregation, and intercellular communication. Therefore colonies of bacteria have been recognized as multicellular
organisms or "superorganisms." It has consequently been postulated that the phenotype of colonies formed by
microorganisms can be automatically recognized and classified using optical systems capable of collecting information
related to cellular pattern formation and morphology of colonies. Recently we have reported a first practical
implementation of such a system, capable of noninvasive, label-free classification and recognition of pathogenic Listeria
species. The design employed computer-vision and pattern-recognition techniques to classify scatter patterns produced
by bacterial colonies irradiated with laser light. Herein we report our efforts to extend this system to other genera of
bacteria such as Salmonella, Vibrio, Staphylococcus, and E. coli. Application of orthogonal moments, as well as texture
descriptors for image feature extraction, provides high robustness in the presence of noise. An improved pattern
classification scheme based on an SVM algorithm provides better results than the previously employed neural network
system. Low error rates determined by cross-validation, reproducibility of the measurements, and overall robustness of
the recognition system prove that the proposed technology can be implemented in automated devices for bacterial
detection.
Time needed for detection and identification of bacteria can be much shortened using the unique light-scattering pattern
after being exposed to the laser source from the new platform named BARDOT (Bacteria Rapid Detection using Optical
scattering Technology). The resulting pattern is compared to the compiled pattern library to search for similarity, hence
determine the types of bacteria. The system consists of a laser source, an imaging camera, a scattering camera, and a
two-dimensional stage. First the imaging camera captures the image of the sample on Petri-dish and locates the center
coordinate locations of each cluster. Then the two-dimensional stage translates the Petri-dish such that the incident laser
beam is upon the individual sample cluster and performs a centering process which is an fine-adjustment to capture a
concentric scattering pattern. The displacement of the platform during this process is determined from the difference of
the centroid of the laser beam without sample and that of scattered laser beam with sample. Using MATLAB to design
and test the centering algorithm, the time taken for the centering algorithm can be minimized by generating a linear
relationship between the lateral distance of the sample movement and the difference of the centroid. The initial
algorithm utilized the non-linear relationship without any compensation of the difference of the centroid value. Thus it
took multiple steps of motions to reach the center location if the difference of colony center to the laser center is larger
than the radius of the sample cluster. With the help of newly designed algorithm, a linear relationship is achieved via
identifying the specific location of the starting point of centering algorithm and compensating the corresponding centroid
difference to match the actual displacement. Therefore the total time needed to satisfy the centeredness of the scattering
pattern is minimized.
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.
Bacterial contamination of food products puts the public at risk and also generates a substantial cost for the food-processing industry. One of the greatest challenges in the response to these incidents is rapid recognition of the bacterial agents involved. Only a few currently available technologies allow testing to be performed outside of specialized microbiological laboratories. Most current systems are based on the use of expensive PCR or antibody-based techniques, and require complicated sample preparation for reliable results. Herein, we report our efforts to develop a noninvasive optical forward-scattering system for rapid, automated identification of bacterial colonies grown on solid surfaces. The presented system employs computer-vision and pattern-recognition techniques to classify scatter patterns produced by bacterial colonies irradiated with laser light. Application of Zernike and Chebyshev moments, as well as Haralick texture descriptors for image feature extraction, allows for a very high recognition rate. An SVM algorithm was used for classification of patterns. Low error rates determined by cross-validation, reproducibility of the measurements, and robustness of the system prove that the proposed technology can be implemented in automated devices for bacterial detection.
Bacterial contamination by Listeria monocytogenes puts the public at risk and is also costly for the food-processing
industry. Traditional methods for pathogen identification require complicated sample preparation for reliable results.
Previously, we have reported development of a noninvasive optical forward-scattering system for rapid identification of
Listeria colonies grown on solid surfaces. The presented system included application of computer-vision and patternrecognition
techniques to classify scatter pattern formed by bacterial colonies irradiated with laser light. This report
shows an extension of the proposed method. A new scatterometer equipped with a high-resolution CCD chip and
application of two additional sets of image features for classification allow for higher accuracy and lower error rates.
Features based on Zernike moments are supplemented by Tchebichef moments, and Haralick texture descriptors in the
new version of the algorithm. Fisher's criterion has been used for feature selection to decrease the training time of
machine learning systems. An algorithm based on support vector machines was used for classification of patterns. Low
error rates determined by cross-validation, reproducibility of the measurements, and robustness of the system prove that
the proposed technology can be implemented in automated devices for detection and classification of pathogenic bacteria.
We have developed a detection system and associated protocol based on optical forward scattering where the bacterial colonies of various species and strains growing on solid nutrient surfaces produced unique scatter signatures. The aim of the present investigation was to develop a bio-physical model for the relevant phenomena. In particular, we considered time-varying macroscopic morphological properties of the growing colonies and modeled the scattering using scalar diffraction theory. For the present work we performed detailed studies with three species of Listeria; L. innocua, L. monocytogenes, and L. ivanovii. The baseline experiments involved cultures grown on brain heart infusion (BHI) agar and the scatter images were captured every six hours for an incubation period of 42 hours. The morphologies of the colonies were studied by phase contrast microscopy, including measurement of the diameter of the colony. Growth curves, represented by colony diameter as a function of time, were compared with the time-evolution of scattering signatures. Similar studies were carried out with L. monocytogenes grown on different substrates. Non-dimensionalizing incubation time in terms of the time to reach stationary phase was effective in reducing the dimensionality of the model. Bio-physical properties of the colony such as diameter, bacteria density variation, surface curvature/profile, and transmission coefficient are important
parameters in predicting the features of the forward scattering signatures. These parameters are included in a baseline model that treats the colony as a concentric structure with radial variations in phase modulation. In some cases azimuthal variations and random phase inclusions were included as well. The end result is a protocol (growth media, incubation time and conditions) that produces reproducible and distinguishable scatter patterns for a variety of
harmful food borne pathogens in a short period of time. Further, the bio-physical model we developed is very effective in predicting the dominant features of the scattering signatures required by the identification process and will be effective for informing further improvements in the instrumentation.
12 Current technological development toward miniaturization requires smaller components. These components usually generate complex multi-DOF motions other than simple 1-DOF mission. Therefore it is essential to develop measurement methodology for 6-DOF motions. In this paper, a new 6-DOF measurement system for milli-structure is presented. This methodology basically employs the Optical Beam Deflection Method with a diffraction grating. A laser beam is emitted toward the diffraction grating which could be attached on the surface of a milli-structure and the incident ray is diffracted in several directions. Among these diffracted beams, 0th and +/- 1th order diffracted rays are detected by 4 Quadrant Photodiodes. From coordinate values from each detector, we can get information for 6-DOF motions with linearization method. Required resolutions for milli- structure measurement are sub-micrometer in translation and arcsec in rotation. Experimental results indicate that proposed system has possibility to satisfy this requirement. This method can be applied to measurement of various applications such as arm head of HDD, micro positioning stages.
Multi-degree-of-freedom (MDOF) displacement measurement systems are needed in many application fields; precision machine control, precision assembly, vibration analysis, and so on. This paper presents a new MDOF displacement measurement system that is composed of a laser diode (LD), two position- sensitive detectors (PSDs), and a conventional diffraction grating. It utilizes typical features of a diffraction grating to obtain the information of MDOF displacement. MDOF displacement is calculated from the independent coordinate values of the diffracted ray spots on the PSDs. Forward and inverse kinematic problems were solved to compute the MDOF displacement of an object. Experimental results show maximum absolute errors of less than plus or minus 10 micrometers in translation and plus or minus 30 arcsecs in rotation.
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