Source: EASTERN REGIONAL RES CENTER submitted to
ADVANCED DEVELOPMENT OF INNOVATIVE TECHNOLOGIES AND SYSTEMATIC APPROACHES TO FOODBORNE HAZARD DETECTION AND CHARACTERIZATION FOR IMPROVING FOOD SAFETY
Sponsoring Institution
Agricultural Research Service/USDA
Project Status
TERMINATED
Funding Source
Reporting Frequency
Annual
Accession No.
0429773
Grant No.
(N/A)
Project No.
8072-42000-077-000D
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Jan 19, 2016
Project End Date
Jan 18, 2021
Grant Year
(N/A)
Project Director
PAOLI G
Recipient Organization
EASTERN REGIONAL RES CENTER
(N/A)
WYNDMOOR,PA 19118
Performing Department
(N/A)
Non Technical Summary
(N/A)
Animal Health Component
(N/A)
Research Effort Categories
Basic
25%
Applied
50%
Developmental
25%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
7123320110040%
7113260104050%
7113520200010%
Goals / Objectives
1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods
Project Methods
The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA-ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA-probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum.

Progress 01/19/16 to 01/18/21

Outputs
PROGRESS REPORT Objectives (from AD-416): 1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods Approach (from AD-416): The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA- ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA- probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum. The Center for Food Safety Engineering continues to develop novel methods for the sampling and detection of pathogens and other organisms that are indicators of contamination with pathogens, new methods to characterize microbial communities associated with food, and develop sensor technologies and bacterial growth models that will allow environmental data to be used to predict food safety risk. Despite the impact of COVID- 19, most of our projects were able to achieve their 60-month milestones. Work has continued on development of elastic light scattering (ELS) for the identification of both pathogenic and non-pathogenic indicator organisms. The hyperspectral version of ELS (HESPI) is undergoing testing with bacterial species that have different colony morphologies, with predictions from theoretical models being compared to scatter patterns achieved with different wavelengths of light. Work on using ELS to classify entire microbial communities (bacterial and fungal) is leading to improvements in software to allow the system to classify colonies as unknown (rather than having to be classified as something in the current library) and also to define the confidence with which an assignment is made. Our microbial community analyses have utilized next-gen sequencing to identify bacteria and fungi found within the endogenous community on romaine lettuce. Community analysis has been performed on conventionally and organically grown commercial lettuce. This work has identified a yeast species that appears to be ubiquitously found on lettuce and that we are working to modify into a living biosensor to detect human pathogens. We also conducted experiments to identify factors that promote or restrict the growth of E. coli O157:H7 when introduced to lettuce leaves. These experiments demonstrate that environmental factors (particularly relative humidity) play a major role in the ability of the pathogen to persist in the lettuce phylloplane community. These same environmental factors also affect the composition and density of the bacterial community and allow the identification of indicator species that are correlated with higher and lower growth of E. coli O157:H7. We continue to develop Laser Induced Breakdown Spectroscopy (LIBS) hardware and software to detect biological and chemical contaminants in food. This work has focused both on a lateral flow assay (LFA)-based system using lanthanide labeled antibodies and a handheld system that will produce ⿿food-fingerprints⿝ that can be used to detect adulteration and food fraud. The second system will take advantage of our work on machine- learning models, with a specific focus on the interpretation and classification of spectral data linking LIBS food fingerprinting with elemental analysis. We have expanded our machine learning (ML) models to include data from olive oil, balsamic vinegar, and coffee, in addition to alpine-style cheeses. Two routes of explainable ML have been pursued: 1) we continued using generalized linear models with various regularization approaches to select spatial features, and 2) we build a feature dependency map using a graph-theoretic approach paired with manifold learning to identify the spectral features that contribute most to the fingerprint distinguishability. We have developed a ⿿biphasic⿝ amplification method for E. coli detection in ground beef samples and demonstrated detection of E. coli O157:H7 DNA with a limit of 2.5 copies in 30 mg of meat sample (~83.3 copies/gram) as well as E. coli O157:H7 cells with a detection limit of 1 CFU/30 mg of meat sample (~33 CFU/g). The platform is based on a two-phase reaction in which the solid phase is dried food matrix that does not remix with the supernatant phase where the fluorescent amplicons are concentrated, allowing a high signal to noise ratio and greater fluorescence change. The advantage of drying the food matrix is that amplification reagents such as primers and polymerase can detect the target without inhibition from reaction inhibitory factors that are locked in the dried matrix. This system provides a culture independent method where the current sample to result time is 2.5 hours and minimal sample processing assures the integrity of DNA or bacteria. We have developed patterned microfluidic paper-based analytical devices (µ-PADS) that have combined the well-known advantages of paper strips with the functionality and utility of microfluidics. This technology holds great potential for instrument-free, portable and multiplexed detection. These devices take advantage of gold nanoparticle⿿s (AuNPs) decorated on polystyrene microparticles (Au-PS). Highly selective aptamers were immobilized on AuNPs and act as a protective support material that maintains the Au-PS particles in a non-aggregated state, giving off a pink color visible to the naked eye. In the presence of the analyte the aptamer coating is disrupted leading to a stronger aptamer- target bond compared to the aptamer-particle bond. Upon target binding the aptamer is lifted off of the particles⿿ surface, causing their immediate agglomeration, which triggers a change in color from pink to purple. After optimizing the synthesis and agglomeration conditions of the polystyrene-AuNPs-aptamer complex, we were able to obtain change in color that can be seen with the naked eye in concentrations as low as 1 ppm. Preliminary detection results performed in solution showed sensitivity as low as 0.5 ppm for mercury and selectivity against other heavy metals such as arsenic, cadmium, and lead. In order to move forward with even more tests at the same time, we would like to create new devices with six to eight test zones. Our fabrication method for the eight-channel devices is wax printing due to the fact that we wish to keep part costs low and fabrication easy. Calibration of the wax-based printing methods is now complete, and multi-target tests are underway. To facilitate quantitative measurements from µ-PAD devices we have designed a pipeline for image analysis using a mobile phone camera to estimate target concentration. In this approach, the metric that we use to characterize the responses in each circular detection zone is based on the average grayscale value. This base model shows an average prediction performance of 60%. Its effectiveness is restricted by the limited dataset and the insufficient utilization of the spatial information contained in the sensor pad images. The detection of lower contamination levels remains challenging due to the small number of data samples and large intra-class variance. To overcome this challenge and improve the prediction accuracy, we propose two more methods for image processing: (1) a Deep Learning Approach for Classifying Contamination Levels with Limited Samples, (2) a Spectral Imaging for Contaminant Levels Estimation. Our systems yield much higher classification accuracy (88%, and 87% average accuracy, respectively) than the base model, demonstrating the feasibility of our approaches. To facilitate development of our phage- based detection systems we characterized genes previously isolated in the project based on their expression in inducible genetic systems. The expression of the phage repressor protein reduced the induction of the lytic cycle in E. coli O157:H7, reducing phage titers by greater than 99. 9% and totally inhibiting plaque formation. Expression of the antirepressor showed the opposite effect by limiting the induction of the lysogenic cycle resulting in a primary lytic phenotype observed in plaques/titers even in our modified phage engineered to have a high frequency of lysogen formation. We have continued our work with the HEK293 reporter cell line expressing TLR-5 receptors. Interaction of flagellar antigen with TLR-5 leads to the activation of NF-kappaB, IL-8 and alkaline phosphatase which can be detected with an appropriate substrate to generate a blue color. The assay is highly specific and validated with the top 20 Salmonella enterica serovars and 10 non- Salmonella spp. The performance of the assay was also validated with spiked food samples. The total detection time including shortened pre- enrichment (4 h) and selective enrichment (4 h) set up with artificially inoculated outbreak-implicated food samples (chicken thighs, peanut kernel, peanut butter, black pepper, mayonnaise and peach) was 15 h when inoculated at 1⿿100 CFU/25 g sample. We have performed several analytical studies for the development of the battery-less time-temperature monitoring (TTM) system. This configuration will greatly simplify integration of the system into deli cases. It also does not depend on the power status of the deli cases, providing reports even during unscheduled power outages. The proposed scheme relies on energy harvesting of radiofrequency waves that are abundant in the environment or provided by internal oscillators that operate in the required frequency range. To estimate the required harvested power, a study of the power budget was performed for every essential module of our system. We also performed sensor resolution analysis for each excitation scheme finding that with internal oscillation the temperature resolution increases by 40% to a nominal value of 0.6 °C. Our next steps are to construct the new design and validate our results in the lab as well as in the field. Growth studies required the coupling of new sensors for routine monitoring of product temperature abuse over time and, unfortunately, sensor availability and testing was delayed due to Covid-19. Sensor performance was assessed by incorporating into in-lab incubators in parallel with built-in temperature monitoring. Growth curves of L. monocytogenes 10403S were completed in different brands of prepacked deli-style turkey breast containing celery powder as an antimicrobial. These data are being used to model the effect of temperature abuse on growth of L. monocytogenes and the utility of celery powder as a bacterial growth inhibitor. Record of Any Impact of Maximized Teleworking Requirement: Many of our research laboratories were affected by Covid-19 related policies at the universities that house the Center for Food Safety Engineering. The magnitude of these effects varied greatly from one laboratory to another since research was allowed to continue varied depending on whether the research was classified as ⿿essential⿝ or not. Rather than trying to generalize how big the impact was, we have included individual statements from the faculty that felt their work was impacted by requirements to work remotely to the greatest degree possible. Aime: Teleworking greatly reduced our ability to conduct necessary wet-bench work (DNA sequencing, replicate NGS data collection, culturing). Additionally, the university hiring freeze meant that our postdoc, who returned to Europe during the pandemic, could not be replaced. Applegate: Although Purdue had a policy of teleworking when possible, our group stayed active as numerous longitudinal projects were being carried out at the time. However, students associated with the project were also able to use this time for other educational purposes: one completed their preliminary exam while the other completed their dissertation work and defended their CFSE associated research. Our undergraduate researchers also returned to work in the last 6 months and are progressing on their CFSE associated projects. We authored one peer-reviewed publication and one review article during this time as well. Bae, Robinson: Remote work requirements had an impact on the pace of progression of the research work. For example, each lab was forced to have only a certain number of students at a given time, and we had to arrange an online reservation system to get into the lab. So, the work and research were conducted but at a slower pace compared to the pre-COVID era. Bashir: The impact was minimal as research ramped back up quickly. Irudayaraj: These were challenging times and held us back. However, we focused our efforts on reviewing current food safety literature and methods for optimizing our technology. A possible extension of the technology to detect viruses was done. Further experiments will be needed to complete these experiments. We expect to publish 1-2 manuscripts from SARS-CoV2 detection. Mousoulis: With regards to the first milestone, ⿿TTM System Optimization,⿝ our laboratory where the TTM development takes place did not have a significant impact on its operations due to telework during the second half of the grant year. This is due to the adjustment of the timeline of the studies so that the analytical work would take place during the period of the lab space certification for compliance with the University⿿s COVID-related restrictions and limitations. Oliver: The teleworking requirement and research lab closures had a significant impact on the progress of planned experiments. Growth curves with new sensors were postponed as they demanded purchasing prepacked deli meats from grocery stores and physical presence in the lab to conduct experiments. However, we utilized this time for an expanded literature review, experimental design refinement, and selection of deli meat categories. Pruitt, Deering: The maximized telework had both positive and negative impacts. On one side, it slowed down the research activity, which requires physical presence in the lab, access to buildings, facilities, and equipment that were closed during the stay-at-home order, and the delay of processing purchase orders to get reagents and laboratory supplies. On the other hand, that research slowdown helped finish some writing activities (papers, reports, data organization, and analysis), which are always put aside due to the time being mainly consumed by the research activity. Stanciu, Chiu, Allebach, Deering: The laboratory work of foodborne pathogens (E. Coli O157:H7 and Listeria monocytogenes) has been halted to concentrate on heavy metal contamination, and progress has been slowed. However, the time has been used to optimize the new synthesis of the aptamer-based microparticles and obtain preliminary results on multiplexed detection of heavy metals. Using the same principle, we plan to start working on bacteria detection at the end of this summer. ACCOMPLISHMENTS 01 Long term storage of reporter bacteriophages. Bacteriophages (viruses that only infect bacteria) offer a unique opportunity to expand detection technologies targeting foodborne pathogens. These bacteriophage-based technologies can potentially be integrated into standardized food testing protocols with minimal disruption of workflow. While several research groups have expanded the uses for various bacteriophages, reliable means of translating these proof-of-principle systems to impactful are lacking. The research accomplished by ARS- funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, has developed technologies that allow bacteriophages for pathogen detection to be manufactured and stored at room temperature. Using bacteriophages reduces the time it takes to evaluate the safety of various food products, increasing the throughput of testing, reducing financial losses to food companies, and, most importantly, protecting consumers. 02 Cellphone-based portable fluorometer. A major limitation of handheld instruments for optical detection of foodborne pathogens is reliably and reproducibly of very weak signals. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed a smartphone-based fluorescence detection system that can detect an extremely low fluorescence signal level. The system combines an optimized hardware design and software algorithm that enhances the low-level fluorescence signal to overcome these limitations. The incorporation of these new technologies will potentially enable detection systems that inspectors can use to provide on-site detection results. 03 Culture-free approach detection of pathogens in complex food samples. Rapid detection of the pathogen E. coli O157:H7 is vital for public health and food safety worldwide. It is very important to detect pathogen presence in food products early, rapidly, and accurately to avoid potential outbreaks and economic loss. ARS-funded scientists at the Center for Food Safety Engineering at the University of Illinois, Urbana-Champaign, Illinois, have developed a diagnostic tool to detect pathogens from direct samples without the necessary steps of enrichment or purification for the pathogens. The protocol directly detects pathogen DNA from a dried food sample and reduces processing time which improves the overall time to results to only 2.5 hours, whereas standard protocols of plating take 1-5 days. Experimental results showed that the method is very sensitive (a detection limit of 1 cfu/ 30mg of dried food matrix for E. coli bacteria). This technique bypasses the need for conventional steps of culture or nucleic acid purification, and the platform can reduce the costs and resources required for food testing and safety. 04 Highly sensitive rapid detection technology for monitoring pathogens in large volume food samples. ARS-funded scientists at the Center for Food Safety Engineering, the University of Illinois at Urbana-Champaign, Urbana, Illinois, have developed sensor technologies utilizing polyester pads to detect E. coli O157:H7 in large sample volumes of a complex food matrix by monitoring the change in color of a sensor strip that the naked eye can see. The method requires only a simple syringe- based setup with an antibody-modified polyester pad to make the enrichment approach practical. The developed method could process a large volume of sample (50 ml of lettuce cocktail blended from a 5g of lettuce), required only 40 minutes of sample processing time, and was very sensitive and specific (100 CFU/ml of E. coli O157:H7 in the presence of ~3000 CFU/ml of non-target bacteria). The developed approach demonstrates a promising means to improve the detection of target bacteria with a high degree of sensitivity and specificity. It could be used in low-resource settings for the specific detection of pathogens. 05 Celery powder to control Listeria monocytogenes in prepacked deli-style turkey breast. Listeria monocytogenes can be a post-processing contaminant of ready-to-eat foods, including deli meats. Deli meats are among the highest risk foods resulting in listeriosis as they do not undergo additional cooking steps before consumption. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, conducted growth studies to evaluate the efficacy of commercial celery powder as an antimicrobial against L. monocytogenes in prepacked deli-style turkey breast stored under ideal and temperature abuse conditions. The results demonstrated a significant linear relationship between product temperature abuse over time and the growth of L. monocytogenes in deli-style turkey samples. The findings underscore the importance of maintaining refrigeration temperatures to complement the efficacy of commercial antimicrobials. 06 Better methods for sequencing the bacterial microbiome from plant samples. Characterization of the bacterial communities from plant tissue is a valuable tool in produce safety research. Methods to catalog these bacterial communities require high throughput DNA sequencing of a gene present in all bacteria, the 16S rRNA gene. Characterization of the bacterial communities from plant tissue using high throughput DNA sequencing of the 16S rDNA is a challenge due to the high similarity between the plant mitochondrial and plastid DNA and the bacterial 16S rDNA. This can result in a greatly reduced number of bacterial sequence reads, leading to loss of the entire bacterial diversity coverage. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed protocols for sample preparation, high throughput DNA sequencing, and data analysis to overcome this issue. These protocols will result in much more complete descriptions of plant bacterial communities at a lower cost than was previously possible. 07 Identification of environmental factors and biomarkers associated with E. coli O157:H7 growth on romaine lettuce leaves. Indoor farming industries grow leafy greens under controlled environmental conditions, potentially allowing them to restrict the growth of harmful bacteria on the leaf surface. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have found that relative humidity (R.H.) is the main factor modulating the fate of E. coli O157:H7 and the composition of the resident bacterial microbiome on romaine lettuce. Other minor contributing factors included the inoculum dose of the foodborne pathogen and the wettability of the leaf surface. Two bacterial species were also found to be potential biomarkers for higher and lower growth rates of E. coli O157:H7 on lettuce leaves. Application of these results could increase the safety of indoor lettuce growing systems. 08 Deep learning approach for classifying contamination levels with limited samples. Mercury (Hg) and Arsenic (As) ions have been recognized as chemical threats to human health and can be present in food samples in trace amounts. Still, the detection of low levels of contamination remains challenging due to the small number of available data samples and significant intra-class variance. To overcome this challenge, ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, explored techniques for synthesizing realistic colorimetric images and propose a Convolutional Neural Network (CNN) classifier for five-contamination-levels. The system was trained and evaluated on a limited dataset of 126 images captured with a cell phone camera representing five contamination levels. The system yields 88.1% classification accuracy and 91.9% precision, demonstrating the feasibility of this approach. The application of this system would allow the use of readily available cell phone cameras to capture images that can estimate the level of contamination with heavy metals.

Impacts
(N/A)

Publications

  • Kanach, A., Bottorff, T., Zhao, M., Wang, J., Chiu, G.T., Applegate, B. 2021. Evaluation of anhydrous processing and storage methods of the temperate bacteriophage V10 for integration into foodborne pathogen detection methodologies. PLoS ONE. 16(4):e0249473. https://doi.org/10.1371/ journal.pone.0249473.
  • Wang, J., Kanach, A., Han, R., Applegate, B. 2021. Application of bacteriophage in rapid detection of Escherichia coli in foods. Current Opinion in Food Science. 39:43⿿50. https://doi.org/10.1016/j.cofs.2020.12. 015.


Progress 10/01/19 to 09/30/20

Outputs
Progress Report Objectives (from AD-416): 1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods Approach (from AD-416): The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA- ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA- probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum. The Center for Food Safety Engineering continues to develop novel methods for the sampling and detection of pathogens and other organisms that are indicators of contamination with pathogens, new methods to characterize microbial communities associated with food, and develop sensor technologies and bacterial growth models that will allow environmental data to be used to predict food safety risk. Despite the complications that arose from the Covid-19 pandemic, all of our projects continue to move forward with the majority of our 48-month milestones being fully or substantially met. Development of our elastic light scattering (ELS ⿿ formerly Bardot and BEAM) technology continues with improvements to both hardware and software, as well as the development of methodologies for using the technology to characterize entire microbial communities. Hardware enhancements involve production and testing of a hyperspectral instrument (HESPI). This has been tested on pure cultures and used to develop new theoretical models of the relationship between light scattering and colony morphology. Work also continues on improved classification algorithms, driven in part by our efforts to develop methodologies that will allow the classification of entire microbial communities. We have worked to define new classification models which work with our existing (ELS, LIBS) and future systems (Raman, FTIR, HESPI) using methodologies that are described below. As a part of this project we have also extensively characterized both the bacterial and fungal microbial communities associated with romaine lettuce using ELS and DNA sequencing and the results are being made available online. A similar analysis of poultry samples is in progress with a strain collection that has been subjected to DNA sequence analysis and will be characterized by ELS. We also continue to develop cell phone-based technologies to be used in portable detection systems. We have developed a smartphone-based spectrometer that was validated by detection of protein concentrations from liquid samples and milk. We have also developed a cell phone linked fluorometer that has been validated with FITC fluorochrome. We were successful in designing and building a gold-standard LIBS instrument that was capable of metal-based detection and analysis of potential pathogens. The core of our progress was based on developing the assay component to target potential pathogens. This required the design and construction of a rapid assay platform and the design of the reagent complexes necessary for the successful detection of target molecules. We focused on lateral flow-based assays (LFA) because they are low cost, easy to transport and there is already a significant literature and commercial knowledge about the use of LFA tests. No previous work has been published using LFA testing with LIBS. We successfully designed and tested a LIBS-based LFA to detect the presence of E. coli from contaminated suspensions. In addition, we defined the limit of detection for four potential lanthanide elements using paper-based detection. We have also developed highly sensitive LFA sensors to detect pathogens in real food samples by just observing color changes with the naked eye. The technology is based on a lateral flow platform, but we utilize gold coated magnetic particles, so that the target pathogen captured by the particles can be concentrated at the signal generation zone of the sensor strip. The whole operation can be completed in 30-45 minutes and as low as 50-100 cfu/ml of pathogen can be detected. The sensor has been validated in fruit juices against E. coli O157:H7 and Salmonella spp. Recently we have extended our protocol to detect as few as 100 cfu/ml of pathogenic E. coli in blended lettuce samples in as much as 50 ml of sample volume in less than 60 minutes. Patterned microfluidic paper- based analytical devices (µ-PADS) have combined the well-known advantages of paper strips with the functionality and utility of microfluidics. This technology holds great potential for instrument-free, portable and multiplexed detection. To obtain quantitative measurements of food contaminant concentration, we developed an image analysis system to read the color response of the sensors by comparing different segmentation methods. We designed a pipeline for an image analysis system using a mobile phone camera to quantitatively correlate the response signal to target concentration, and also investigated variability of the response. Based on these results, we optimized our image analysis pipeline to produce a method which segments the test zones accurately and presents a quantitative estimate of color response in the test zones. Aptamers provide the high specificity for the target that antibodies offer, and at the same time are more robust towards environmental stressors and significantly cheaper. We were able to design aptasensors based on functionalization of gold nanoparticles with aptamers specific for mercury and arsenic and fabricated an effective multiplexed paper-based assay. We then tested the detection limit of µ-PADS, as well as reliability, repeatability, stability, and specificity. This work is, to the best of our knowledge, the first cell-phone integrated µ-PAD platform for multiplexed aptamer-based detection of analytical targets, which presents with a low standard deviation of analytical response and colorimetric signal quantification. We introduce the application of highly stable optical labels as a strategy for enhanced sensitivity of colorimetric detection. Finally, we present robust image analysis and processing, resulting in a limit of detection of 1 ppm for two targets, mercury and arsenic, and quantitative (analytical) response in an aqueous environment. Aptamers provide the high specificity for the target that antibodies offer, and at the same time are more robust towards environmental stressors and significantly cheaper. We were able to design aptasensors based on functionalization of gold nanoparticles with aptamers specific for mercury and arsenic and fabricated an effective multiplexed paper-based assay. We then tested the detection limit of µ- PADS, as well as reliability, repeatability, stability, and specificity. This work is, to the best of our knowledge, the first cell-phone integrated µ-PAD platform for multiplexed aptamer-based detection of analytical targets, which presents with a low standard deviation of analytical response and colorimetric signal quantification. We introduce the application of highly stable optical labels as a strategy for enhanced sensitivity of colorimetric detection. Finally, we present robust image analysis and processing, resulting in a limit of detection of 1 ppm for two targets, mercury and arsenic, and quantitative (analytical) response in an aqueous environment. We are also developing a plasmonic ELISA system (PES) that utilizes the growth of gold nanoparticles after the reduction of gold chloride solution with hydrogen peroxide to produce a visible color change that can be analyzed with the naked eye. This type of assay has been developed and tested for Salmonella enterica serovars and for Listeria monocytogenes. The PES assays developed have similar limits of detection to conventional ELISA assays, but are easier to score without the need for a spectrophotometer. We are also developing assays that are based on pathogen interactions with mammalian cell lines. We have developed Mammalian Cell-based ImmunoAssay (MaCIA) for the detection of viable Salmonella enterica serovar Enteritidis. In MaCIA, instead of a capture antibody, which is generally used in conventional ELISA, a mammalian cell line was used to capture bacteria. In MaCIA, natural adhesion ability (achieved by using virulence factors) of a pathogen can be exploited for the detection of the viable pathogenic organism from food. In this assay, a human intestinal cell line, HCT-8 was grown in monolayers in 24-well tissue culture plates and Salmonella at variable concentrations was added and incubated for 30 minutes. Anti-Salmonella antibody was added to interact with the captured bacteria and an enzyme-conjugated second antibody was used as a reporter. MaCIA could detect viable S. Enteritidis (1-10 CFU/ 25g) in ground chicken, shelled eggs, whole milk, and cake mix, using a traditional enrichment set up; however, the detection time was shortened to 10-12 h using direct on-cell (MaCIA) enrichment. Formalin-fixed cells in the MaCIA platform permits a longer shelf life (at least 14-weeks at 4°C), minimum on-site maintenance care, and a stable cell monolayer for point-of-need deployment. Furthermore, the 24-well plate configuration is also convenient for testing multiple samples in a single run thus reducing cost per sample testing. We have also developed an assay using HEK293 reporter cell line expressing TLR-5. Interaction of flagellar antigen with TLR-5 leads to the activation of NF-¿B, IL-8 and alkaline phosphatase which can be detected with an appropriate substrate to generate a blue color. Our preliminary results indicate that the assay is highly sensitive (102-103CFU/ml) to Salmonella enterica (top-20 Salmonella serovars have been tested). We are now optimizing conditions to improve assay specificity by using Salmonella specific immunomagnetic beads from inoculated poultry meats. During this project period we were able to identify key genetic parameters for the optimization of production of the PhiV10 based reporter phage platform in a nonpathogenic E. coli host. The expression of these genes in the E. coli lysogen will counteract the lytic cycle repressor allowing release of progeny phage. During this project period we also developed a protocol for the freeze drying the reporter phage to ensure long-term storage with minimal loss during drying. The developed protocol will allow phage to be integrated into detection assays and inclusion in dry media formulations. Accomplishments 01 Identification of fungal indicator species from romaine lettuce. A better understanding of the interactions between the organisms that make up the microbial community found on lettuce will help to understand how human pathogens become part of that community and what we can do to prevent it. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have identified a single, abundant and ubiquitous species of yeast present on healthy lettuce samples. The fact that this ubiquitous yeast is previously undescribed indicates how limited our knowledge of the fungal community on lettuce is. Characterization of this yeast and its interactions will help determine how its presence changes in response to invasion by human pathogens and/or food spoilage microbes. 02 Development of an enhanced elastic light scattering instrument for bacterial identification. Current practice for identification of bacterial species relies on microbiological methods to obtain individual bacterial colonies on an agar plate. Because visual examination of the isolated bacterial colony is not sufficient to confirm the identity of bacteria, complex and time-consuming assays are required following the isolation procedures. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have previously developed a single-color laser optic method to directly identify bacterial isolates on plates without the need for additional complex and time-consuming assays for bacterial identification. While the method has proved successful for many bacterial species, we have improved this technology through the use of a multi-color laser. The system was tested for the identification of bacterial species from lettuce samples and shown to provide higher accuracy using newly designed analysis methods. This system will help laboratory personnel and food safety engineers in government and industry sectors with fast and accurate identification of bacterial species. 03 Cellphone-based spectrometer. A major limitation of many food safety assays is the requirement to use expensive equipment that is found only in centralized laboratories. This requires samples obtained in the field to be sent to those labs rather than being analyzed on site. ARS- funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed a smartphone-based spectrometer that can resolve the visible range of spectrum in transmission mode and can be used to analyze many types of food safety assays. The overall cost of the spectrometer is only $200 and functions with an app that can visualize, record, and analyze the visible spectrum. This device could be incorporated into many types of assays with visual readouts to allow them to be utilized at the point the sample is taken, simplifying the assay process and reducing the time required to get a result. 04 Development of a novel detection assay for Salmonella based on binding to mammalian cells. Rapid detection of major foodborne pathogens is of paramount importance to ensure food safety. At present, nucleic acid and antibody-based assays are the methods of choice for rapid detection, but these are prone to interference from inhibitors and resident microbes and the assay formats may limit multisample testing in a single run. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed a mammalian cell- based assay that detects pathogen interaction with mammalian host cells and is responsive to only live bacterial pathogen cells. The assay has been formulated to use stabilized cell monolayers to provide longer shelf-life for possible point-of-need deployment and multi-sample testing in a single run. This assay provides a highly specific alternative to conventional assays that could be utilized either in testing labs or at remote locations. 05 Wireless, high-resolution, time-temperature measurement using low-cost tags. Continuous temperature monitoring is essential for the estimation of pathogen growth in ready-to-eat foods. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed a system that can be integrated in deli-cases and is capable of acquiring temperature measurements using low cost tags that can be attached to food packages. The system provides high-resolution temperature measurement that can be integrated into the ⿿Internet of Things⿝ (IoT) through Bluetooth communication capabilities. Integrating the system in retail deli-cases can enable real-time risk assessment of stored products with direct notification of the management when irregular storage conditions occur. 06 Development of paper-based assays for use with laser induced breakdown spectroscopy (LIBS). A major goal of food safety research is to provide rapid, sensitive, low cost assays that reliably detect human pathogens. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed new paper-based assays that take advantage of a previously developed LIBS instrument and metal tagged antibodies and have demonstrated that they can be used to detect pathogenic E. coli. The use of tagged antibodies provides the potential for detecting multiple pathogens at once in a single assay. This demonstration is a proof of concept that will facilitate the development of commercial assays based on this technology that could be used by the food industry and food safety regulatory agencies. 07 Affordable, reliable, sensitive, and repeatable multiplexed aptasensors for the detection of heavy metals. Mercury and Arsenic have been recognized as chemical threats for human health because of their atmospheric transport, environmental persistence, bioaccumulation in living tissue and detrimental effects for human health even at extremely low concentration. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, designed and demonstrated a novel cell-phone integrated µ-PAD platform for rapid and reliable multiplexed aptamer-based detection of mercury and arsenic in pristine and real environmental samples. This is a new technology that makes µ PAD sensitivity and specificity enhancement and colorimetric data quantification possible and opens the door to commercialization of this low cost and flexible assay technology. 08 Quantitative colorimetric detection of target molecules with low response variability. Quantitative detection of the level of a biological or chemical food contaminant is often limited by the reliability of the assay system. Response variations in test areas of lateral flow assay strips to the same concentrations of target prevent reliable quantitation. ARS- funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, developed of a robust image analysis system that provides rapid quantitative measurement of the µ-PADs based on the analysis of color composition. This device is fully integrated into a mobile phone to capture and analyze the sensor images on-site and the method can be customized for a wide variety of analytes including whole cell foodborne pathogens. This technology provides the basis for a portable and inexpensive system that could be deployed widely in the food industry and has the flexibility to detect a wide variety of food contaminants.

Impacts
(N/A)

Publications

  • Bhunia, A.K. 2019. Microbes as a tool to defend against antibiotic resistance in food animal production. Indian Journal of Animal Sciences. 58(2).
  • Doh, J., Gondhalekar, C., Patsekin, V., Rajwa, B., Hernandez, K., Bae, E., Robinson, J.P. 2019. A portable spark-induced breakdown spectroscopic (SIBS) instrument and its analytical performance. Applied Spectroscopy. 73(6):698-708.
  • Gondhalekar, C., Biela, E., Rajwa, B.B., Patsekin, V., Sturgis, J., Reynolds, C., Doh, I.J., Diwakar, P., Stanker, L., Zorba, V., Mao, X., Russo, R., Robinson, J.P. 2020. Detection of E. coli labeled with metal- conjugated antibodies using lateral-flow assay and laser-induced breakdown spectroscopy. Analytical and Bioanalytical Chemistry. 412:1291-1301.
  • Mathipa, M.G., Thantsha, M.S., Bhunia, A.K. 2019. Lactobacillus casei expressing internalins A and B reduces Listeria monocytogenes interaction with Caco-2 cells in vitro. Microbial Biotechnology. 12(4):715-729.
  • Rodriquez-Lorenzo, L., Garrido-Maestu, A., Bhunia, J.K., Espina, B., Prado, M., Diéguez, L., Abaldecela, S. 2019. Gold nanostars for the detection of foodborne pathogens via surface-enhanced raman scattering combined with microfluidics. ACS Nano. 2(10):6081-6086.


Progress 10/01/18 to 09/30/19

Outputs
Progress Report Objectives (from AD-416): 1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods Approach (from AD-416): The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA- ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA- probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum. We are developing advanced technologies for sample handling and the detection of foodborne pathogens and toxins, and demonstrating their applications for meats, poultry, ready-to-eat (RTE) products, and produce. All of the individual projects continue to move forward with most of our milestones either fully or substantially met. Our elastic light scatter ⿿BARDOT⿝ (Bacterial Rapid Detection using Optical Scattering Technology) system uses light scattering techniques to differentiate and classify bacterial colonies grown on Petri-dishes. BARDOT analysis has successfully differentiated bacteria at genus, species, and serovar/ serotype levels, with correct classification often above 90% for foodborne pathogens (Listeria monocytogenes, Salmonella, Shiga toxin- producing E. coli (STEC), Vibrio, Staphylococcus, and Bacillus species). The BARDOT systems use a single-wavelength red laser to interrogate the bacterial colonies to generate a scatter image, an enhanced version of the BARDOT technology (Hyperspectral Elastic Scatter Phenotyping Instrument, or HESPI) is being developed using a newly introduced supercontinuum laser. The HESPI system is capable of using 50-100 different laser lines to generate scatter images using multiple wavelengths of light, providing significantly more information for bacterial differentiation and identification. One of the critical issues in dealing with a wide range of wavelengths is that the imaging spot laterally translates due to diffraction phenomena from the laser filters. This year an optical relay system was designed and tested that can minimize this spot movement so that hyperspectral imaging from a single colony can be achieved within 40-100 ms. Efforts are ongoing to validate pathogen identification by BARDOT/HESPI in real-world samples, emphasizing leafy greens and poultry, by coupling laser scatter patterns with DNA sequence verification and macro- and micro-morphology of the colonies. Recent work has also demonstrated the feasibility of using a single culture system with BARDOT to simultaneously detect multiple different pathogens. An additional 1100 samples were collected from poultry farms in the past year and this yielded a collection of more than 800 isolates of Salmonella. These isolates are being confirmed via PCR and serotyping and will be used to enhance our optical scatter pattern libraries. Work is also ongoing to extend the use of BARDOT/HESPI to the characterization of entire microbial communities found on food samples. We have used our previous culture collection to construct BARDOT libraries that can classify most of the culturable naturally occurring bacterial genera from foods with 90% accuracy in mixed populations. Some genera generate scatter patterns that are so similar that it is difficult for BARDOT to distinguish them under these conditions and we are working with both HESPI and improvements to the software to see if we can develop methods to properly differentiate all of the genera. Characterization of the bacterial and fungal communities on lettuce using DNA sequencing methods is also ongoing. Culture-dependent methods were used to characterize the bacterial and fungal communities, and culture independent methods (next- generation DNA sequencing) are being optimized so that we can examine how these communities vary spatially and with differing growth conditions. Analysis of fungal communities has identified several types of yeast (Sporidiobolales) on the surface of lettuce that have also been reported from contrasting ecosystems, including marine, soil, polar ice, and many others. An analysis of niche preferences in Sporidiobolales shows they are ubiquitous on plant surfaces and in commercial crops and food products. The dominant yeast on the surface of lettuce is a new species (Sporobolomyces cf. roseus) that is ubiquitous on plant surfaces, but had not been previously isolated or characterized. We are currently collecting full assimilation profiles to fully characterize this species. We have created and started to populate an online fungal catalog that will eventually provide all data on the panmicrobiome of Romaine lettuce including numerous tools, spanning from classical to innovative (including molecular barcodes, scatter patterns, biochemical, and morphological) to expedite the identification of fungal colonies directly by food safety inspectors and members of the fresh produce industry. A better understanding of the ecology of the microorganisms that inhabit the surface of fresh produce will provide useful new approaches to increase product shelf-life and control human pathogens that exploit this environment. A ⿿bi-phasic⿝ DNA amplification assay system is also being developed. This involves the direct drying of food samples into a solid phase followed by the release of bacterial DNA into a liquid phase by thermal lysis of bacterial cells in the solid phase. This DNA can then be amplified and specific sequences detected in a loop mediated isothermal amplification (LAMP) reaction. The system has been shown to be capable of very sensitive detection in bacterial cultures (~1 cell in a 4 microliter sample). Further development of methods and testing of the system with environmental samples is in progress. The development of a metal enhanced plasma spectroscopy (MEPS) system was slowed this year to achieve the goal of having a more robust instrument in hand for the remaining development work. This system has now been used to demonstrate the detection of metals in a paper-based assay as well as detection of metals conjugated to antibody molecules. This has also been used to demonstrate the ability to detect E. coli O157:H7 in a paper-based assay and preliminary steps were achieved to demonstrate the ability to do multiplex assays using different metal ions. In addition, a Notice of Allowance was issued this year by the U.S. Patent and Trademark Office for a patent application (15/510,319) covering intellectual property related to the metal-antibody tagging and plasma-based detection technology. Several lateral flow immunochromatography assays (LFIA) and DNA-based aptamer approaches are in development. Work on magnetically focused lateral flow assays continued with real world samples including fruit juices and fresh produce. Detection limits of 50-100 cells per milliliter have been achieved. A cell phone-based sensor for reading the lateral flow assays has also been developed. Methods for optimized printing of DNA aptamers for use in detecting foodborne pathogens have also been developed and these assays can also be read with a sensor attached to a mobile phone. Detection limits of 100 cells/mL in ground beef enrichment cultures have also been achieved with this DNA-based system. Our plasmonic ELISA system (PES) has been tested for sensitivity and specificity when used with anti-Salmonella antibodies. All Salmonella enterica serovars including Typhimurium, Enteritidis, Choleraesuis and several others showed strong species-specific reaction while Listeria monocytogenes, Bacillus cereus, and Staphylococcus aureus did not show any reaction. Appropriate antibodies to adapt this technology to the detection of Listeria have been developed and are in the process of being tested. Both traditional and plasmonic ELISA have a limit of detection of about 10^8 cells/mL, however, PES yields a clear change in color which can be detected with naked eyes, while the color change in conventional ELISA is subtle and requires a spectrophotometer for quantification. We are now developing an In-Cell ELISA (ICE) for the detection of viable foodborne pathogenic bacteria. In ICE, instead of a capture antibody, which is used in conventional ELISA, a mammalian cells are used to capture the pathogenic bacteria. In ICE, the natural adhesion ability (achieved by using virulence factors) of a pathogen can be exploited for detection of the viable pathogenic organism from food. A secondary antibody and colorimetric detection is then used in a manner similar to conventional ELISA assays. Initial experiments have demonstrated the efficacy of ICE for detection of Listeria monocytogenes and Salmonella enterica. For monitoring the temperature of food products through distribution and retail, a high-resolution (1°C), low-powered, networked sensor for time- temperature monitoring was developed that can transmit measurements in real time to a base station. Modifications have been made that optimize data storage and transmission as well as providing a rigid housing for the sensor. These sensors have been tested in a real-world refrigerated environment and have provided temperature data for a period of more than two months. Work is continuing on battery-less sensors that can be inductively coupled to a receiving unit to provide a lower cost sensor. Experimentally determined bacterial growth curves in deli meats maintained under various temperature regimes will be used to model growth under a wide range of conditions. Integrating the TTM sensor data with the bacterial growth models could allow for real-time estimates of product shelf-life or potential increases food safety risks. A better understanding of the specificity of bacteriophage PhiV10 for E. coli O157:H7 was obtained by testing the phage on numerous strains of E. coli bearing serogroup O157 O-antigen and variable flagellar (H) antigens and other strains bearing the H7 flagellar antigen and various O antigens. The results demonstrate that PhiV10 requires both of these antigens to successfully infect, although it can bind to the bacterial cell if only the O157 antigen is present. Accomplishments 01 Cellphone-based technology for pathogen detection. Portability of new pathogen detection systems enables them to be used in a variety of critical real-world scenarios and smartphone-based detectors provide an easy access platform for portability. ARS-funded researchers at the Purdue University Center for Food Safety Engineering have developed a sensitive bioluminescence detector using a smartphone that was able to detect E. coli O157:H7 on spiked ground beef samples after 10-12 hours culture using a previously developed bacteriophage-based luminescent detection system. Purdue researchers also developed a smartphone-based imaging system for quantitative detection of pathogens via lateral flow immunoassays (akin to a home pregnancy test). Integration of existing foodborne pathogen detection systems with smartphone hardware and applications will provide a tremendous benefit to the food industry and food testing laboratories by increasing portability of testing methods and testing data. U.S. Patent Application (16/175752) has been submitted to cover the developed technology. 02 Nanoparticles for targeted bacterial killing. The presence of human pathogens in biofilms in foods and food production settings remains a major challenge for food safety. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, synthesized and assessed a novel functionalized gold nanoparticle (fGNP) that kills and prevents biofilm formation by the deadly foodborne pathogen Listeria monocytogenes. The fGNP is coated with a molecule that directs the nanoparticle to bind to L. monocytogenes and an antimicrobial molecule that kills the bacterium. In addition to providing an effective mechanism to killing L. monocytogenes, the fGNP provides a platform for the development of reagents to reduce or eliminate other foodborne pathogens on surfaces in food processing settings. 03 Simultaneous detection of three major pathogens from food samples using optical scattering technology. The detection of human pathogens in food remains a major challenge for the food industry. Application of novel, rapid technologies increases food safety while reducing costs. ARS- funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, employed a laser light scattering technology (BARDOT) coupled with a multi-pathogen selective medium for concurrent detection of three major foodborne pathogens in a single assay. BARDOT was used to detect and distinguish Salmonella enterica, Shiga-toxin producing Escherichia coli (STEC) and Listeria monocytogenes from food based on colony scatter signature patterns. This innovative BARDOT multi-pathogen detection platform can reduce turn-around-time and economic burden on food industries by offering a label-free, non- invasive on-plate multi-pathogen screening technology to reduce microbial food safety and public health concerns. 04 A super-sensitive lateral flow immunosensor for rapid detection of pathogenic bacteria from foods. Rapid and inexpensive detection of human pathogens in food samples is critical to maintaining a safe food supply. ARS-funded scientists at the Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, have developed an enhanced immunoassay that is about 1000-times more sensitive than current commercial tests. The ability to detect pathogens at this low level is due to the fact that we direct pathogens towards the signal generation region using a simple magnet integrated within the sensor. The entire procedure can be performed in under 45 minutes and the signal can be visualized by the naked eye. Tests have been developed for three foodborne pathogens: E. coli O157:H7, Salmonella, and Listeria monocytogenes. A provisional patent application has been filed (2019-IRUD-68420) related to magnetic focusing technology that leads to the dramatically improved sensitivity of this lateral flow assay. 05 Portable time-temperature monitors (TTMs) for food safety. Temperature is a critical control point to extend shelf-life and ensure safety of many foods. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, developed and deployed low-cost, portable time-temperature monitoring (TTM) sensors that provided continuous temperature data for over two months in a deli case of a commercial store. A smartphone-based application that interfaces with the TTM sensors for the acquisition, storage, and cloud-upload of measurements was also developed as part of this study. The interface is simple and allows simultaneous monitoring of multiple sensors with a single handheld device. Accurate TTM throughout food transport and storage would allow the modeling of bacterial growth in food products, which in turn could predict changes in shelf-life or risk of pathogen growth. 06 A large collection and characterization of bacterial strains associated with organic and conventional Romaine lettuce. Bacteria pathogenic to humans have been found at an increasing rate as contaminants in fresh produce. Reducing the frequency of illness associated with the consumption of fresh produce will require an understanding of both the events leading to contamination and how the human pathogens integrate into the native microbial communities. ARS-funded scientists at the Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, have created a representative collection of 645 bacterial strains from organic and conventional romaine lettuce. The 9 most abundant types (representing about 77% of the romaine lettuce- associated culturable bacterial genera) were used to build image identification libraries using the light scattering bacterial identification technology called BEAM. Three BEAM identification libraries were tested using an additional 215 lettuce isolates; demonstrating an average error per class of less than 10%. These studies demonstrate the efficacy of the BEAM for identification of bacteria from food and environmental samples and the libraries developed will aid in the rapid characterization of the native bacterial community found on lettuce. 07 Comparison of Salmonella prevalence on conventional and no antibiotics ever (NAE) poultry farms. Salmonella is an important human foodborne pathogen often associated with poultry. With changing practices in the poultry industry, such as no antibiotics ever (NAE) on-farm production, it is important to determine the impact these changing practices have on Salmonella prevalence in poultry. ARS-funded researchers at the University of Georgia worked with poultry producers to test for the prevalence of Salmonella throughout the growth of broiler chickens on these different types of farms. Results from these samplings showed that there is a higher prevalence of Salmonella on the conventional farms than in NAE farms, suggesting that NAE poultry production does not pose an elevated risk of Salmonella prevalence. 08 A new bench top laser-based instrument for foodborne pathogen detection. Rapid and sensitive detection of human pathogens in food samples is a major challenge for food safety researchers. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed a new laser-induced breakdown spectroscopy (LIBS) instrument and demonstrated that it can be used to detect metal-labeled antibodies in paper-based immunoassays, an assay platform commonly used for foodborne pathogen detection. These studies provide the basis for the use of this technology for the development of commercial food safety test kits. A Notice of Allowance was issued this year by the U.S. Patent and Trademark Office for a patent (application 15/510,319) covering intellectual property related to various aspects of this work. 09 Ink-jet printing of test strips for detection of E. coli O157:H7. It is known that the foodborne pathogen E. coli O157:H7 can cause severe disease and even death. Therefore, the detection of foodborne pathogens is crucial for global public health. The work performed by ARS-funded scientists at the Center for Food Safety Engineering, Purdue University, West Lafayette, Indiana, designed an inkjet printing-based, cost- efficient, reliable, and repeatable approach for the detection of foodborne bacterial pathogens in real food samples. Paper test strips that were printed with novel bio-inks containing DNA molecules (aptamers) that specifically bind to E. coli O157:H7 provided quantitative detection of the pathogenic bacteria from ground beef with an extremely low limit of detection (100 CFU/ml). This aptamer-based platform achieves highly sensitive and specific detection of E. coli O157:H7 using a cost-efficient and scalable printable bio-ink that has the potential to significantly reduce the price for rapid testing of foodborne pathogens. 10 Artificial intelligence for recognition of emerging foodborne pathogens. A major issue in any type of human disease prevention (including foodborne illness) is the ability to recognize new and emerging threats from a vast and complex sea of data. ARS-funded scientists at the Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, have employed AI-enhanced data processing to enumerate classes of microorganisms and recognize the presence of new (i.e., previously unknown or emerging) types of microbes. The result demonstrates the tremendous capacity for machine learning technologies in the areas of biosurveillance; biothreat detection and agricultural biosafety. The developed software has applicability far exceeding the field of agriculture and food safety, and it could be potentially used for visualization and interaction with clinical samples processed using advanced genomic technologies.

Impacts
(N/A)

Publications

  • Urbina, H., Aime, M. 2018. A closer look at Sporidiobolales: Ubiquitous microbial community members of plant and food biospheres. Mycologia. 110(1) :79-92.
  • Duarte-Guevara, C., Swaminathan, V., Reddy Jr, B., Wen, C., Huang, Y., Huang, J., Liu, Y., Bashir, R. 2017. Characterization of a 1024 ÿ 1024 DG- BioFET platform. Sensors and Actuators B: Chemical. 250:100-110.
  • Wang, R., Vega, P., Xu, Y., Chen, C., Irudayaraj, J. 2018. Exploring the anti-quorum sensing activity of a D-limonene nanoemulsion for Escherichia coli O157:H7. Biomedical Materials Research.
  • Diaz Amaya, S., Zhao, M., Lin, L., Ostos, C., Allebach, J.P., Chiu, G.T., Deering, A., Stanciu, L.A. 2019. Inkjet printed nanopatterned aptamer- based sensors for improved optical detection of foodborne pathogens. Nano Micro Small. 15(24):e1805342.
  • Diaz-Amaya, S., Lin, L., Dinino, R.E., Ostos, C., Stanciu, L.A. 2019. Inkjet printed electrochemical aptasensor for detection of Hg2+ in organic solvents. Electrochemical Acta. 316:33-42.
  • Bailey, M.A., Taylor, R.M., Brar, J.S., Corkran, S., Velasquez, C., Rama, E., Oliver, H., Singh, M. 2018. Prevalence and antimicrobial resistance of Campylobacter from antibiotic-free broilers during organic and conventional processing. Poultry Science. 98:1447-1454.
  • Corkran, S.C., Bailey, M., Brar, J., Velasquez, C., Waddell, J., Oliver, H. , Bratcher, C.L., Wang, L., Kumar, S., Singh, M. 2018. Comparison of processing parameters in small and very small beef processing plants and their impact on Escherichia coli prevalence. LWT - Food Science and Technology. 95:92-98.


Progress 10/01/17 to 09/30/18

Outputs
Progress Report Objectives (from AD-416): 1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods Approach (from AD-416): The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA- ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA- probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum. We are developing advanced technologies for sample handling and the detection of foodborne pathogens and toxins, and demonstrating their applications for meats, poultry, ready to eat (RTE) products, and produce. Progress was made on all objectives and subobjectives. Our elastic light scatter ¿BARDOT¿ (Bacterial Rapid Detection using Optical Scattering Technology) system uses light scattering techniques to differentiate and classify bacterial colonies grown on Petri-dishes. BARDOT analysis has successfully differentiated bacteria at genus, species, and serovar/ serotype levels, with correct classification often above 90% for foodborne pathogens (Listeria monocytogenes, Salmonella, Shiga toxin- producing E. coli (STEC), Vibrio, Staphylococcus, and Bacillus species). BARDOT has also been used to study the effects of subinhibitory levels of antibiotic exposure on bacterial cell morphology and scatter signatures. Numerous hardware and software upgrades have been made for improved data collection and analysis, including replacing the single-wavelength laser currently used in the BARDOT with a supercontinuum laser in our new hyperspectral elastic scatter phenotyping instrument (HESPI). Efforts are ongoing to validate pathogen identification by BARDOT/HESPI in real-world samples, emphasizing leafy greens and poultry, by coupling laser scatter patterns with DNA sequence verification and macro- and micro-morphology of the colonies. Data collected on over 1300 samples from poultry farms and 1000 samples from poultry processing facilities found 450 isolates of Salmonella and are being used for both risk-assessment and developing mitigation strategies. Additionally, expansion of BARDOT/HESPI to include analysis of fungal species continued. Within the 645 bacterial strains isolated from conventionally grown and organic romaine lettuce and identified using DNA sequencing methods, 44 different bacterial genera were found, and BARDOT analysis was able to classify all but one of the genera with >90% accuracy. More than 250 fungal strains were also isolated from these lettuce samples, identified to 49 fungal species. A photographic library of the colony morphology of these identified bacteria and fungi, along with the micro-morphology, ITS GenBank sequence accession number, and BARDOT scatter pattern images, was created to be used as a web-based identification guide for the fresh produce industry. Benchtop laser induced breakdown spectroscopy (LIBS) methods along with spark-induced breakdown spectroscopy (SIBS) technologies were applied to our labeled heavy metal probes to capture and identify foodborne pathogens and toxins, and preliminary data look promising. Significant effort this year went in to developing a SIBs instrument that does provide a low cost technical solution for using our lanthanide-conjugated antibodies to detect foodborne pathogens and toxins. This technology is based on use of a high voltage spark that disrupts the sample into a plasma that is subsequently measured using a spectrometer. Several lateral flow immunochromatography assay (LFIA) approaches are in development. Advances this year in the LFIA method that utilizes magnetic- focusing reduced the detection limit from 50 CFU/ml to 25 CFU/ml in pure culture, with a repeatable detection limit of 100 CFU/ml E.coli O157:H7 and Salmonella spp. in fruit juices within 45 minutes. Printing processes in development successfully jetted drop of biologically active materials (including antibodies) onto substrates in predesigned geometric patterns, which will better facilitate pathogen capture for detection. Printed DNA and antibodies remained functional on natural cellulose paper, and lateral flow analysis using these printed test strips was able to detect 100 CFU/ml E.coli O157:H7. Further developments in the chemistry of our plasmonic ELISA system (PES) led to use of liposome encapsulating chemicals which trigger aggregation of the gold nanoparticles to generate an enhanced colorimetric signal and lower detection threshold, which is currently 100 CFU/ml E.coli O157:H7. Additional PES developments utilized Listeria adhesion protein as a ligand for detection of Listeria monocytogenes. For the cell-based assay for rapid high-throughput detection (CARD) technology for detecting Shiga-toxins (Stx), phage induction enrichment methods were used, vero cells were successfully grown in 3D format, the optimal mammalian cell concentration needed to show a maximum cytotoxicity towards a membrane active detergent or STEC cell was determined, and the current detection limit is 106 CFU/ml with a 12-16 hour assay time. The CARD was tested for specificity against a panel of pathogenic and commensal bacteria in a buffer and in 27 raw ground beef samples (32 ± 3.2 g) that were inoculated at 4 CFU/g. For monitoring the temperature of food products through distribution and retail, a high-resolution (1°C), low-powered, networked sensor for time- temperature monitoring was developed that can transmit the obtained measurements in real time to a base station. The original rigid circuit board was replaced with a flexible polyethylene terephthalate substrate (a plastic approved for food contact), and conductive traces and pads were formed via inkjet printed silver ink. Extensive work was also done to improve integrity of data storage when the connection with the base station is lost for long periods of time, as could happen during transport. Work is ongoing to correlate microbial growth profiles to temperature histories: efforts this year documented the growth of Listeria monocytogenes in different ready-to-eat products under various time-temperature profiles. The potential for using the luminescent phage-based detection assay during sample shipment was explored using different luminescent reporter phage concentrations, temperatures, and media:sample ratios, and it was possible to detect 1 cell of E.coli O157:H7 in 325 grams of ground beef within 15 hours. These results suggest this approach is feasible and could allow the rapid detection of a presumptive positive upon arrival, assuming the sample was shipped at 5pm and arrived at 8am the following day (15h) and the shipping container was maintained near 37 degrees. Developments in cellphone-based miniature optical attachments for pathogen detection enabled colorimetric detection of inkjet printed ink patterns and colorimetric assay responses, as well as bioluminescence detection, for which a portable silicon photomultiplier sensor reduced the detection from 106 CFU/ml surrogate bioluminescent bacteria to 103 CFU/ml. Accomplishments 01 Insight into the low infectious dose and highly invasive nature of Listeria monocytogenes. Listeria monocytogenes is an important foodborne pathogenic bacterium. ARS-funded scientists in the Center for Food Safety Engineering at Purdue University are developing methods to detect L. monocytogenes in foods while improving our understanding of the pathogenicity of this deadly bacterium. They previously discovered a protein they called Listeria adhesion protein (LAP) the helps L. monocytogenes bind to intestinal cells and leveraged the unique properties of LAP to develop methods to detect L. monocytogenes from foods. Here they report studies on how LAP is involved in helping L. monocytogenes cause infection. Intestinal cells are the first line of defense against gut pathogens, yet some bacterial pathogens, such as L. monocytogenes, can cross the intestinal barrier during foodborne infection. Not only does LAP allow L. monocytogenes to bind to intestinal cells it induces a unique physiological change that allows L. monocytogenes to bypass the intestinal barrier and cause serious illness, often leading to death. This may explain the low infectious dose and highly invasive nature of L. monocytogenes. This important discovery about L. monocytogenes¿ mechanisms of infection provides researchers valuable details that could lead to the development of therapeutic interventions to prevent or treat human illness. 02 A net fishing enrichment strategy for pathogen detection. The relatively low numbers and uneven distribution of pathogenic microorganisms in foods create challenges for reliable and rapid pathogen detection. ARS-funded scientists in the Center for Food Safety Engineering have developed a unique strategy for the enrichment of pathogens based on a concept that captures target bacterial using a net fishing approach. The functionalized ¿net¿ is immersed into a contaminated liquid food sample and captures the target pathogenic bacteria. The net fishing enrichment requires 2 hours for pathogen capture and 30 minutes for detection of the foodborne pathogen (such as E. coli O157:H7). Lower numbers of pathogens can be detected sooner using this approach because they are captured by the ¿net¿ and then detected. Rapid and more sensitive detection of foodborne pathogens could lead to enhanced food safety and improve public health outcomes. 03 New species of yeast commonly associated with romaine lettuce. Yeast and molds are commonly associated with plants and foods of plant origin. Yeast in the order Sporidiobolales, commonly called red yeast because they produce a red carotenoid pigment, have been reported from numerous ecosystems, including marine, soil, leaves, polar ice, and many others. ARS-funded scientists in the Center for Food Safety Engineering at Purdue University present several analyses drawing on nearly 600 new isolates collected from various substrates around the globe. An analysis of niche preferences in Sporidiobolales shows they are ubiquitous on plant surfaces and in commercial crops and food products. The study revealed numerous interesting results. For example, the red yeast species Sporobolomyces cf. roseus was recovered from every Romaine lettuce sample tested, and cross analyses with isolates recovered from other foods show that this new species is ubiquitous on plant surfaces, yet had not previously been scientifically characterized. Researchers will use these data to study and better understand the interactions between microorganisms on common crops and how these may impact produce shelf-life and human health. 04 A robust system for molecular printing of biosensing test strips. While many rapid and sensitive biosensing technologies for foodborne pathogen detection have been developed at the laboratory level, the transition to the market has been difficult to accomplish. ARS-funded scientists in the Center for Food Safety Engineering at Purdue University are overcoming obstacles in the way of mass production of foodborne pathogen sensors by designing inkjet patterning methodologies for the mass fabrication of test strips comprising biologically active nanomaterials that can perform sensing functions. Some sensing molecules (e.g., antibodies and DNA aptamers) remain active for pathogen detection after being inkjet printed in targeted bioink quantities in controlled design printed patterns. The developed procedure was proven to fabricate both antibody-based and aptamer-based color-changing biosensing test strips reproducibly and reliably, detecting E. coli O157:H7 at low concentrations. The development of printable test strips for foodborne pathogen detection should be broadly applicable and will allow the rapid and reproducible production of inexpensive detection devices. 05 Recognition of emerging food-pathogens using the tools of artificial intelligence. Pathogen detection and data analysis are often limited to the types of samples present in a database, and problems are often encountered when new bacteria not present in the database are encountered. ARS-funded scientists at the Center for Food Safety Engineering, at Purdue University in West Lafayette, Indiana, explored the application of an artificial intelligence (AI) system to phenotypic characteristics of various food-borne pathogens to determine the ability of the AI to identify the number of present pathogenic classes, and recognition of new, unknown classes of food-borne pathogens that were not present in the databases. The scientists were able to develop a functional prototype of an emerging pathogen detection system using AI methodology primarily based on the pattern-recognition neural network created by data scientists at Google initially for the goal of natural images classification. The technology developed at Purdue integrates the cutting-edge machine learning tools with a unique optical phenotypic biosensing device created at Purdue using ARS funding. The result demonstrates tremendous potential of the AI technology in the areas of biosurveillance, biothreat detection, and agricultural biosafety. Additionally, it must be emphasized that leveraging the existing state-of-art informatics tools employed by the leading U.S. data management companies will lower the cost of adoption of the new AI technologies by food producers and regulatory agencies.

Impacts
(N/A)

Publications

  • Alsulami, T.S., Zhu, X., Abdelhaseib, M.U., Singh, A.K., Bhunia, A.K. 2018. Rapid detection and differentiation of Staphylococcus colonies using an optical scattering technology. Analytical and Bioanalytical Chemistry.
  • Redemann, M.A., Brar, J., Niebuhr, S.E., Lucia, L.M., Acuff, G.R., Dickson, J.S., Singh, M. 2017. Evaluation of thermal process lethality for non- pathogenic Escherichia coli as a surrogate for Salmonella in ground beef. LWT - Food Science and Technology. 90:290-296.


Progress 10/01/16 to 09/30/17

Outputs
Progress Report Objectives (from AD-416): 1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods Approach (from AD-416): The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA- ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA- probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum. Progress was made on all objectives, all of which fall under National Program 108 � Food Safety, Component 1 Foodborne Contaminants; and Problem Statement 3, Microbial Contaminants: Technologies for Detection and Characterization and Problem Statement 4, Chemical and Biological Contaminants: Detection and Characterization Methodology, Toxicology and Toxinology. We are developing advanced technologies for sample handling and the detection of foodborne pathogens and toxins, and demonstrating their applications for meats, poultry, ready to eat (RTE) products, and produce. Progress was made on all objectives and subobjectives. Our elastic light scatter �BARDOT� (Bacterial Rapid Detection using Optical Scattering Technology) system uses light scattering techniques to differentiate and classify bacterial colonies grown on Petri-dishes. The BARDOT successfully differentiated bacteria at genus, species, and serovar/ serotype levels. BARDOT analysis resulted in correct classification often above 90% for foodborne pathogens (Listeria monocytogenes, Salmonella, Shiga toxin-producing E. coli (STEC), Vibrio, and Bacillus species). A light scattering image library of indicator bacteria representing the Enterobacteriaceae family continues to be expanded (containing over 1600 scatter images from 14 genera and 31 species), and a library of Staphylococcus species was created and shown to have a high positive predictive value (>87.5%) and low false-positive rate (6.6%) for detecting Staphylococcus. Numerous hardware and software upgrades have been made for improved data collection and analysis, including replacing the single-wavelength laser currently used in the BARDOT with a supercontinuum laser in our new hyperspectral elastic scatter phenotyping instrument (HESPI). Efforts are ongoing to validate pathogen identification by BARDOT/HESPI in real-world samples, emphasizing leafy greens and poultry, by coupling laser scatter patterns with DNA sequence verification and macro- and micro-morphology of the colonies. Additionally, the application of BARDOT/HESPI was expanded from bacteria to also include analysis of fungal species. We have cataloged 645 bacterial strains and 48 fungal species found in conventional and organic grown lettuces. The best BARDOT/HESPI patterns for yeasts were collected from yeast species grown on corn meal agar. Benchtop laser induced breakdown spectroscopy (LIBS) methods along with spark-induced breakdown spectroscopy (SIBS) technologies were applied to our labeled heavy metal probes to capture and identify foodborne pathogens and toxins, and preliminary data look promising. Several lateral flow immunochromatography assay (LFIA) approaches are in development. One LFIA that combines magnetic-focusing has been demonstrated to have a detection limit of <50 CFU/ml and an analysis time of 30 minutes. Printing processes in development successfully jetted drop of biologically active materials (including antibodies) onto substrates in predesigned geometric patterns, which will better facilitate pathogen capture for detection. The chemistry of our plasmonic ELISA system (PES) was optimized by conjugating streptavidin with catalase and observing the kinetics of color development in the presence of variable concentrations of hydrogen peroxide. For the cell-based assay for rapid high-throughput detection (CARD) technology for detecting Shiga-toxins (Stx), phage induction enrichment methods were used, Vero cells were successfully grown in 3D format, and the current detection limit is 10X6 CFU/ml with a 12-16 hour assay time. For monitoring the temperature of food products through distribution and retail, a high-resolution (1 deg C), low-powered, networked sensor for time-temperature monitoring was developed that can transmit the obtained measurements in real time to a base station. Work is ongoing to correlate microbial growth profiles to temperature histories. Advances in bacteriophage luminescence-based detection of E. coli O157:H7 were tied to developments in cellphone-based miniature optical attachments, and the proof of principle conducted with a surrogate organism had a 10X6 CFU/ml detection limit using a smartphone for the assay. Accomplishments 01 Cellphone based technology for bioluminescence detection. Bacteriophage are viruses that infect specific bacteria, and they have been used to detect foodborne pathogens. One such approach has added a light-producing bioluminescent indicator into the bacteriophage that becomes detectable once the bacteriophage has infected its target pathogen. A challenge for this bacteriophage bioluminescence detection is that it requires sensitive and costly instruments to detect the extremely dim light produced. ARS-funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, have developed a smartphone-based bioluminescence detection system called BAQS (Bioluminiscent-based Analyte Quantification by Smartphone). The proof of concept was demonstrated with an indicator organism and resulted in the detection of the organism at levels comparable to some currently employed on-site detection methods. 02 Comparison of Salmonella prevalence between organic and conventional chicken. Salmonella is an important pathogen to control in poultry products, and organic poultry products are becoming more common. Therefore, it is important to determine the effectiveness of current processing methods on Salmonella prevalence in these products. ARS- funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, collected samples from organic and conventional chicken during processing at a commercial facility and determined the prevalence of Salmonella at various processing steps. Results showed that Salmonella prevalence was higher in organic chickens compared to conventional chickens during early processing steps, but prevalence was the same for both processing methods after the final chill step. This indicates that organic raised chickens are similar to conventional raised ones. 03 BARDOT-based screening of pathogens and indicator bacteria from foods. In order to minimize disease outbreaks and financial losses, the food industry requires methods to rapidly and accurately detect disease- causing bacteria (pathogens) and indicator bacteria that are used to monitor industrial hygiene, sanitization practices, process verification, and indirectly reflect the likely presence pathogenic bacteria. Using the elastic light scatter �BARDOT� (Bacterial Rapid Detection using Optical Scattering Technology) technique developed by ARS-funded scientists at the Center for Food Safety Engineering (CFSE) at Purdue University in West Lafayette, Indiana, CFSE scientists have developed methods to identify harmful Staphylococcus bacteria and indicator organisms belonging a large bacterial family, the Enterobacteriaceae (EB). Numerous culture media were tested to identify a medium that allows highly accurate classification [positive predictive values (PPV) of 87-100%] for six different Staphylococcus species, and the developed Staphylococcus scatter pattern library was successfully used for the identification of Staphylococcus isolates from chicken salad and bovine raw milk. After comparing the numerous BARDOT scatter images for the various EB (31 different species from 14 genera) grown on several different culture media, one culture medium (Rapid EB) was found to yield the best results. The medium was used to identify EB with PPVs of 83-100% using individual cultures and 33%-96. 5% using mixed cultures or artificially and naturally contaminated food samples. This novel label-free on-plate colony BARDOT screening technology has the potential for adoption by the food industry and public health laboratories for screening samples for the presence of both bacterial pathogens, such as Staphylococcus, and indicator organisms, such as EB, for hygiene monitory during food processing. 04 Improved method for detection of Shiga-toxin from pathogenic E. coli. Shiga-toxin producing Escherichia coli (STEC) are of major concern, since they are responsible for about 176,000 foodborne illness with more than 2,500 hospitalizations and 20 deaths annually in the US. STEC infection is characterized by severe intestinal illness sometimes leading to deadly kidney infections. Kidney toxicity is correlated with the production of the Shiga toxin (Stx). Immunological or molecular approaches, although rapid, cannot provide a correlation with the disease or presence of active Stx. The Vero cell (cultured mammalian kidney cells) cytotoxicity assay is the gold standard for Stx detection. ARS-funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, modified this Vero cell assay for improved Stx detection. A variety of treatments [chloroform. ultraviolet light, and antibiotics (ciprofloxacin, polymyxin B, and mitocysin C)] that increase Stx production by E. coli cells were tested. Using a modified method that incorporated Vero cells after antibiotic and chloroform treatment, it was possible to detect active Stx from test samples within 16-18 hours. This reduced detection time for the active toxin can help ensure food safety, reduce outbreaks, and lessen the economic burden of human illness. 05 Development of high-resolution, low-powered, networked, time- temperature monitoring sensor. Temperature abuse of food products has been established as one of the most important causes for foodborne disease outbreaks. Regulatory efforts are focused on food production but there is a lack of control measures outside the production plant. Therefore, distribution and retail are considered the weakest links in the food safety management system with regard to temperature abuses, creating a pressing need for new management tools. A high-resolution, low-powered, networked sensor for time-temperature monitoring (TTM) was developed. Correlation of microbial growth profiles to temperature history requires a sensor that can collect and transmit measurements without interfering with the conditions of the sample under study. ARS- funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, developed time-temperature monitoring (TTM) sensors that provide temperature measurements with high resolution and transfer them wirelessly to a basestation. The sensors have been calibrated in a wide range of temperatures (-40 to 140 deg F) and have exhibited resolution better than 2 deg F. These TTM sensors are ready for application in different environments and have the potential to assist in efforts to monitor and manage the environmental conditions to which food products are exposed during distribution and retail, providing crucial information on the quality and handling of the product.

Impacts
(N/A)

Publications

  • Dak, P., Ebrahimi, A., Swaminathan, V., Duarte-Guevara, C., Bashir, R., Alam, M.A. 2016. Droplet-based biosensing for lab-on-a-chip, open microfluidics platforms. Biosensors. doi: 10.3390/bios6020014.
  • Bashir, R. 2016. Microcantilevers track single-cell mass. Nature Biotechnology. 34(11):1125-1126.
  • Duarte-Guevara, P., Duarte-Guevara, C., Ornob, A., Bashir, R. 2016. On- chip PMA labeling of foodborne pathogenic bacteria for viable qPCR and qLAMP detection. Microfluidics and Nanofluidics. doi: 10.1007/s104016-1778- 2.
  • Mendon�a, M., Moreira, G., Concei��o, F., Hust, M., Mendon�a, K., Moreira, �., Fran�a, R., Padilha Da Silva, W., Bhunia, A.K., Aleixo, J. 2016. Fructose 1,6-Bisphosphate aldolase, a novel immunogenic surface protein on Listeria species. PLoS One. doi: 10.1371/journal.pone.0160544.
  • Kim, H., Doh, I., Sturgis, J., Bhunia, A.K., Robinson, J.P., Bae, E. 2016. Reflected scatterometry for noninvasive interrogation of bacterial colonies. Journal of Biomedical Optics. doi: 10.1117/1.JBO.21.10.107004.
  • Kim, H., Jung, Y., Doh, I., Lozano-Mahecha, R.A., Applegate, B., Bae, E. 2017. Smartphone-based low light detection for bioluminescence application. Scientific Reports. doi: 10.1038/srep40203.
  • Kim, H., Rajwa, B., Bhunia, A.K., Robinson, J.P., Bae, E. 2016. Development of a multispectral light-scatter sensor for bacterial colonies. Journal of Biophotonics. 10:634�644.
  • Ku, S., Ximenes, E., Kreke, T., Foster, K., Deering, A.J., Ladisch, M.R. 2016. Microfiltration of enzyme treated egg whites for accelerated detection of viable Salmonella. Biotechnology Progress. 32(6):1464-1471.
  • Rajwa, B. 2017. Effect-size measures as descriptors of assay quality in high-content screening: A brief review of some available methodologies. Assay and Drug Development Technologies. 15(1):15-29.
  • Shenoy, A.G., Oliver, H.F., Deering, A.J. 2017. Listeria monocytogenes internalizes in Romaine Lettuce grown in greenhouse conditions. Journal of Food Protection. 80(4):573�581.
  • Wang, R., Xu, Y., Liu, H., Peng, J., Irudayaraj, J., Cui, F. 2017. An integrated microsystem with dielectrophoresis enrichment and impedance detection for detection of Escherichia coli. Biomedical Microdevices. 19(2) :34.
  • Ximenes, E., Hoagland, L., Ku, S., Ladisch, M. 2017. Human pathogens in plant biofilms: Formation, physiology, and detection. Biotechnology and Bioengineering. 114:1403�1418.
  • Ren, W., Liu, W., Irudayaraj, J. 2017. A net fishing enrichment strategy for colorimetric detection of E. coli O157:H7. Sensors and Actuators B: Chemical. 247:923-929.
  • Kim, H., Awofeso, O., Choi, S., Jung, Y., Bae, E. 2016. Colorimetric analysis of saliva�alcohol test strips by smartphone-based instruments using machine-learning algorithms. Applied Optics. doi: org/10.1364/AO.56. 000084.
  • Reddy, B., Salm, E., Bashir, R. 2016. Electrical chips for biological point-of-care detection. Annals of Biomedical Engineering. 18:329-355.


Progress 10/01/15 to 09/30/16

Outputs
Progress Report Objectives (from AD-416): 1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense. 1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants. 1B. Antibody-based detection of foodborne pathogens and toxins. 1C. DNA - based detection of foodborne pathogens. 1D. Phage-based detection of foodborne pathogens. 1E. Cell-based detection of foodborne pathogens and toxins. 1F. Enabling technologies for pathogen detection. 2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing. 2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms) 2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods Approach (from AD-416): The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA- ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA- probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum. This new Project Plan was recently certified through ARS Office of Scientific Quality Review (OSQR), For further details on current work see the 2016 annual report for project 8072-42000-072-00D.

Impacts
(N/A)

Publications