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 Characterization and Interventions for Foodborne Pathogens: Wyndmoor, PA
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ARS Home Northeast Area Wyndmoor, Pennsylvania Eastern Regional Research Center Characterization and Interventions for Foodborne Pathogens Research





- Paoli, George
- 2023
- 2022
- 2021
- Publications
- Food Safety, (Animal and Plant Products) (108)

Research Project: Development of Innovative Technologies and Strategies to Mitigate Biological, Chemical, Physical, and Environmental Threats to Food Safety **Location:
                            Characterization and Interventions for Foodborne Pathogens** 2023 Annual Report **Objectives**
                                

Objective 1:  	Development and evaluation of innovative sensor technologies for the detection and characterization of biological, chemical, and physical contaminants of concern in foods that can be implemented for improved food safety and/or assessment of food integrity and adulteration.
	Sub-objective 1A: 	Lysogenic phage-based detection of Shiga toxin producing E. coli and Salmonella serovars.
	1.A. Aim 1	Development of luminescent/colorimetric phage for detection of Salmonella serovars. (Applegate)
	1.A. Aim 2	Generate, validate, and transfer a field-portable and lab based luminescent phage-based method for quantitative detection of Shiga-toxin producing E. coli (STEC). (Applegate)
	Sub-objective 1B: 	Cell phone-based technologies for pathogen detection.
	1.B. Aim 1	Electrochemical and mass-based methods for smartphone-based instrumentation. (Bae, Robinson, Rajwa)
	1.B. Aim 2	Enhancing the cell phone-linked bioluminescence and lateral flow assay technology for food safety applications. (Bae, Applegate, Deering, Robinson, Rajwa)
	Sub-objective 1C: 	Portable laser-induced breakdown spectroscopy system for on-field multiplexed detection of pathogens.
	1.C. Aim 1	Design of LIBS-compatible immunoassays. (Robinson, Bae, Rajwa)
	1.C. Aim 2	Design and prototyping of portable LIBS-based system. (Robinson, Bae, Rajwa)
	1.C. Aim 3	Development and implementation of data acquisition and management software. (Robinson, Bae, Rajwa)
	Sub-objective 1D: 	Multiplexed detection platform technologies for food safety threats
	1.D. Aim 1	Design of a multiplex/multi-replicate dual modality detection platform for whole-cell foodborne pathogens. (Stanciu, Deering, Chiu, Allebach)
	1.D. Aim 2	Design and fabricate a portable multiplexed paper-based platform for quantifying live Shiga toxin-producing E. coli strains in the field. (Verma, Stanciu, Chiu, Allebach)
	Sub-objective 1E: 	Development of a novel yeast biosensor for continuous real-time monitoring of produce safety.
	1.E. Aim 1	Develop a transformation system for Sporobolomyces lactuca nom. prov. and identify transcripts that are differentially expressed in the presence of E. coli. (Aime, Solomon, Pruitt)
	1.E. Aim 2	Development and testing of S. lactuca nom. prov. as a living biosensor. (Solomon, Aime, Pruitt)
	Sub-objective 1F: 	Development of a handheld LIBS unit, assays, and analysis tools for use in label-free food fingerprinting and tracing to improve food defense and combat food adulteration, contamination, and fraud.
	1.F. Aim 1	Expansion and re-design of the benchtop LIBS instrument and associated measurement procedures to accommodate a variety of agricultural samples. (Rajwa, Robinson, Bae)
	1.F. Aim 2	Design and feasibility study of a portable LIBS-based food fingerprinting platform. (Rajwa, Robinson, Bae)
	1.F. Aim 3	Development of machine learning tools for LIBS food fingerprinting and classification. (Rajwa, Robinson, Bae) **Approach**
                                

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 Report**
                                

The Center for Food Safety Engineering (CFSE) continues to develop novel methods for the detection of biological, chemical, and physical contaminants in food that could pose food safety threats. Research moved forward on all projects and with the lifting of COVID research restrictions all the milestones were fully or substantially met.
Work continues on the use of bacteriophage as a sensitive and specific detection method and focused on 2 areas: 1) Continued construction of molecular tools for using Salmonella phage P22 for colorimetric and luminescent detection assays. The genetic complementation vector allowing phage modification without antibiotic markers was completed and is being used to generate both luminescent and colorimetric P22 reporter phage. The antibiotic marker system was also completed and is being used to generate P22 carrying a tetracycline resistance marker for expanding the P22 host range. 2) Development of protocols for implementing the previously developed Shiga toxin-producing Escherichia coli luminescent reporter phage during selective enrichment protocols. Key parameters were evaluated for using the previously developed reporter phage in enrichment protocols for both produce and meat. It was shown that in the standard FDA protocol for leafy greens that the reporter phage could be added at 5 hours (after the pre-enrichment) when the selective supplement was added at high concentrations. Similar observations have been made for meat assays however there is more modification of the current workflow required.
The CFSE also continues work to develop new and improve existing cell phone-based detection technologies. This year’s effort was primarily focused on moving forward with a mass-based sensor (quartz crystal microbalance) and electrochemical sensor. The mass-based sensor was successfully tested as a benchtop model using Salmonella samples. With the addition of gold nanoparticles, a 10^3 CFU/ml detection limit was achieved on the benchtop system. For the electrochemical sensor, design work was completed and a prototype was built and tested using nonpathogenic bacteria. The first design had a detection limit of 10^6 CFU/ml, which will be improved by optimization of the detection process and sensor design. In addition to those new sensors, we continued work on our existing cell phone-based detector for lateral flow assays. Improvements were based on developing algorithms to analyze the speckle patterns and correlate them with the gold nanoparticle concentrations to provide improved sensitivity.
Work to develop an assay system based on laser-induced breakdown spectroscopy (LIBS) has focused on building a prototype unit that incorporated Raman spectroscopy in addition to LIBS. This task was achieved, although the unit is not hand-held at this stage and still requires an external power supply. The prototype is capable of generating both LIBS and Raman signals and represents a significant improvement to the LIBS technology. Project scientists also significantly improved the control and analysis software for the system. Currently, the software developed is driven entirely by an external computer and requires extensive user interaction, but it demonstrates proof of principle and is a significant step forward.
We achieved significant advancements in food product evaluation using LIBS technology. We optimized measurement protocols, fully characterized the benchtop instrument, and developed algorithmic enhancements. We also initiated the development of a hybrid LIBS/Raman instrument and a data fusion system for improved food authentication. We optimized the LIBS measurement protocols using paper-based substrates for liquids. The benchtop LIBS instrument was further characterized for sensitivity, accuracy, and detection limits, and compared to a LIBS handheld device. Algorithmic advancements involved standardized spectral feature selection algorithms for the accurate classification of food groups.
Work also continued on two microfluidic paper-based systems to detect bacterial pathogens in a multiplexed assay. The first system has been enhanced through the addition of electrochemical sensing. Our focus is on colorimetric and electrochemical techniques to make detection quantifiable, integrating image analysis for colorimetric sensors and testing image processing software. Challenges like accurate recognition and controlled signal variation are being addressed by quantifying variations from multiple tests. Despite hurdles, we've developed devices that can detect two different targets simultaneously. We've designed a colorimetric system for detecting Escherichia coli (E. coli) O157:H7 and Salmonella Typhimurium, a dual sensing system for mercury, and have promising preliminary results with Listeria monocytogenes detection. Early results suggest our devices are efficient, selective, cost-effective, and suitable for rapid on-site screening of threats in water or food.
The second system is designed to use loop-mediated isothermal amplification (LAMP) of target DNA to specifically detect Shiga toxin-producing E. coli (STEC) strains in the field. We have demonstrated that the LAMP assay can produce a colorimetric difference between live and dead cells of E. coli O157. However, a key observation was that the assay demonstrates a “hook-effect” in response to concentration of reagents used and thus, is not appropriate for quantification. The team is currently reviewing alternative methods for quantifying the distinction between live and dead STEC.
Work also continues to develop and determine the suitability of the phylloplane yeast Sporobolomyces lactucae (S. lactucae) as a novel biosensor for the detection of human pathogens in situ. We have continued work to determine the geographic distribution of this novel yeast to determine in what regions it might be successfully deployed. DNA sequences consistent with S. lactucae were detected from environmental DNA sequence datasets from parts of the world with Mediterranean climates. Environmental strains of this fungus were also isolated from various crop plants and natural phylloplanes in the San Francisco Bay area; this was the only world region that has been sampled for phylloplane yeasts from which this species has been recovered. The majority of U.S.-grown lettuce also originates from this region, making it likely that all produce carrying S. lactucae was originally colonized at the point of origin, rather than during transport. Additional experiments were designed to determine whether S. lactucae is also present in other lettuce growing regions of the United States. Data from this study will help to determine the suitability range of deploying S. lactucae as a biosensor. 
Both DNA and RNA sequencing studies are currently ongoing with S. lactucae. The genome of S. lactucae was sequenced and assembled. A transcriptome has been generated and genome annotations are nearly complete. Confrontation experiments between S. lactucae and E. coli were designed in replicate and carried out under a variety of conditions in vitro. RNA from these experiments has been extracted and is presently being sequenced. Data from these experiments will be analyzed to identify genes that are differentially expressed during co-growth with E. coli. These genes will then form the basis targets for biosensor development.
In the previous reporting period, we evaluated and optimized methods to deliver DNA to S. lactucae to enable cellular reprogramming as a biosensor. We also identified antibiotic and potential auxotrophic markers to select for transformants that would retain any introduced programming. In this period, we have focused on implementing these markers to reprogram these cells with new phenotypes. We have developed a uracil auxotrophic mutant enabling transformation and selection with URA3 bearing constructs. We have created a URA3 selection cassette with a strong, constitutively expressed promoter, which is able to integrate into the genome and complement uracil-deficient media enabling growth. Due to the source and design of these sequences, the resulting organism is not classified as a genetically modified organism (GMO). We have also validated URA3 plasmids that can be transformed into the yeast. These two platforms position us to modify S. lactucae to generate a colorimetric signal as a biosensor.
Work was done to develop a high-resolution, low-cost, wireless, and battery-less temperature sensor system for food safety assurance. The designed sensor is able to reach high-resolution through down-conversion methods. A Kapton MT+ based flexible antenna was also designed to directly capture temperature changes. Analytical, simulation, and experimental models were developed and used to design and implement these flexible antennae. The resolution and the measurement distance of the time temperature monitoring (TTM) sensor system to accurately measure temperature variations have been determined at a maximum of 0.6 °C between -20 °C and 20 °C and 12 cm between the designed flex antennae, respectively. The flexible antennae used in the system cost approximately 30 cents and conform to shapes and sizes typically found in packaged food products. The results of simulations have been confirmed experimentally for the prototype.
The CFSE continues research to develop methods to create experimental biofilms that can be used for testing sanitizing procedures. Current efforts are involved in creating model dry biofilms that can occur in low moisture food processing environments. An increasing number of foodborne illness outbreaks are associated with low moisture foods due to the presence of dry surface biofilms (DSB) that can be difficult to detect and eliminate. We created a DSB mono- and mixed-culture model that can mimic these challenges in food production and processing systems.  
This work is expanding to include Cronobacter sakazakii, which is a major food security threat. **Accomplishments**
                                

1. Phage-based pathogen detection in leafy greens. Leafy greens continue to be a source of food illness caused by pathogenic bacteria, particularly E. coli O157:H7. Thus food regulatory agencies and food producers required, fast, sensitive and low cost methods to detect E. coli in Romaine lettuce and other leafy greens. The standard technique for detecting one cell requires a time consuming culture enrichment step in which the one cell grows into millions of cells, and after which the pathogen can be detected. To optimize the use of the hours spent in culture enrichment, ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, developed an assay that uses a modified bacterial virus to allow light-based detection both during and after the enrichment without interfering with the current workflow. The method also allows the recovery of the pathogen for trace back by simply plating the cells and finding the glowing colony. The developed assay is also cost effective and will allow more testing to reduce foodborne illness. 2. Detection of Salmonella Typhimurium using a portable mass-based device. Detection of the foodborne pathogenic bacterium Salmonella is crucial for global public health. ARS-funded engineers at the Center for Food Safety Engineering in West Lafayette, Indiana, have collaborated on the development of a portable mass-based device for detecting Salmonella. Improvements to the method for Salmonella detection using a benchtop mass-based device improved the sensitivity of the assay by 100-times. Subsequently, a smartphone-based device incorporating a mass sensor (a quartz crystal microbalance) and a fluorescence imaging system was engineered. This device allows the detection of Salmonella via mass-induced frequency shifts and captures fluorescence images of the cells for verification. By deploying this portable mass-based device, there is a strong potential for a notable decrease in Salmonella infections, thereby making a substantial impact on public health. 3. Advancement in rapid detection of foodborne bacteria. Foodborne bacteria pose significant health risks globally. Dealing with this problem depends on swift and effective detection methods to ensure food safety. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have developed a user-friendly paper-based device that identifies multiple foodborne pathogens simultaneously and provides quantitative results through image analysis. The device utilizes DNA molecules (aptamers) that specifiically bind the bacteria and an innovative color-based system to determine bacterial concentration, enhancing its value as a quantitative tool. This new device is currently capable of detecting two foodborne pathogens, E. coli O157:H7 and Salmonella Typhimurium, at low levels. The development of this versatile, cost-effective, and efficient tool represents a significant accomplishment in enhancing food safety measures. Its potential for on-site rapid screening makes it a valuable tool in our broader effort to protect public health by ensuring the safety of water and food products. 4. Novel aptasensor for the simultaneous electrochemical and colorimetric detection of mercury. Mercury is a harmful element that can contaminate water sources, contaminate food products and pose severe health risks. The challenge we was to create a rapid, reliable, and portable device capable of detecting mercury effectively. This task is critical to our partners and stakeholders who are concerned about ensuring safe food and water supplies. ARS-funded scientists at the Center for Food Safety Engineering in West Lafayette, Indiana, have innovated a device that can concurrently detect mercury via changes in color and electical properties of the sensor. Development of this device involved integrating a paper-based component into a miniaturized system, fabricated through advanced technology. This system was tested for selectivity against other heavy metals and showed outstanding performance. The device detects mercury at extremely low concentrations (as low as 0.01 ppm) and demonstrates a high level of stability. Moreover, it proved its capability in real-world settings by accurately detecting mercury in river samples with high recovery rates. Our device offers a versatile detection platform with multiplexing, multi-replication, and quantitative color analysis, minimizing false results. This development is a substantial accomplishment, enhancing our ability to safeguard water sources from mercury contamination and can have further applicability to detection of the same metal in food products. It contributes to broader efforts to protect public health from the hazards of heavy metal contamination. **Review Publications**
                            *Wu, X., Shin, S., Gondhalekar, C., Patsekin, V., Bae, E., Robinson, J.P., Rajwa, B. 2023. Rapid food authentication using a portable laser-induced breakdown spectroscopy system. Foods. 12(2). https://doi.org/10.3390/foods12020402.*
                                *Doh, I., Zuniga, D.V., Shin, S., Pruitt, R.E., Rajwa, B., Robinson, J.P., Bae, E. 2023. Bacterial colony phenotyping with hyperspectral elastic light scattering patterns. Sensors. 23(7):3485. https://doi.org/10.3390/s23073485.*
                                *Chaggar, G.K., Nkemngong, C.A., Li, X., Teska, P.J., Oliver, H.F. 2022. Hydrogen peroxide, sodium dichloro-s-triazinetriones and quaternary alcohols significantly inactivate the dry-surface biofilms of Staphylococcus aureus and Pseudomonas aeruginosa more than quaternary ammoniums. Microbiology. 168(3):001140. https://doi.org/10.1099/mic.0.001140.*
                                *Shin, S., Dowden, B., Doh, I., Rajwa, B., Bae, E., Robinson, J.P. 2023. Surface environment and energy density effects on the detection and disinfection of microorganisms using a portable instrument. Sensors. 23(4):2135. https://doi.org/10.3390/s23042135.*
                                *Somvanshi, S.B., Ulloa, A.M., Zhao, M., Liang, Q., Barui, A.K., Lucas, A., Jadhav, K.M., Allebach, J.P., Stanciu, A. 2022. Microfluidic paper-based aptasensor devices for multiplexed detection of pathogenic bacteria. Biosensors and Bioelectronics. 207(1):114214. https://doi.org/10.1016/j.bios.2022.114214.*
                                *Chen, C., Coronel-Aguilera, C., Applegate, B.M., Gehring, A.G., Bhunia, A.K., Paoli, G. 2022. Studies on simultaneous enrichment and detection of Escherichia coli O157:H7 during sample shipment. Foods. 11(22):3653. https://doi.org/10.3390/foods11223653.*
                                *Al-Hindi, R.R., Teklemariam, A.D., Alharbi, M.G., Alotibi, I., Azhari, S.A., Qadri, I., Alamri, T., Harahek, S., Applegate, B.M., Bhunia, A.K. 2022. Bacteriophage-based biosensors: A platform for detection of foodborne bacterial pathogens from food and environment. Biosensors. 12(10):905. https://doi.org/10.3390/bios12100905.*
                                *Lee, J.J., Aime, M.C., Rajwa, B., Bae, E. 2022. Machine learning-based classification of mushrooms using a smartphone application. Applied Sciences. 12(22):11685. https://doi.org/10.3390/app122211685.*


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