Location: Characterization and Interventions for Foodborne Pathogens
2024 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 the majority of our milestones were completed on schedule.
Work continues on the use of bacteriophage as a sensitive and specific detection method and was focused on 2 areas: 1) Construction of recombinant Salmonella phage P22 for colorimetric and luminescent detection assays. The recombinant phage modification tools previously developed for Escherichia coli (E. coli) O157:H7 phage PhiV10 are currently being used to generate a Salmonella reporter. Presently we are evaluating P22 lysogen frequency and strain specificity to determine the efficacy of this detection platform using the current regulatory protocols for selective enrichment of Salmonella. 2) The modification of E. coli O157:H7-specific reporter phage PhiV10nluc to expand its detection for other STEC serotype strains. Further investigation was continued to identify the mode of infection of the phage, specifically the necessity for both the O157 (attachment) and H7 (infection) antigens. A protocol was developed exploiting expression of the H7 antigen in non H7 stains to allow positive selection of mutants which can attach to other relevant STEC strains.
The CFSE also continued work to develop new and improve existing cell phone-based detection technologies. A mass-based sensor (quartz crystal microbalance) was successfully integrated with a smartphone-based system to operate as a portable and field-deployable instrument. This included integration of a micro-controller, fluidic controller, and fluorescence detection module into a single unit. The detection limit was tested and was approximately 103-104 CFU/mL. In addition to those new sensors, we continued work on our existing cell phone-based detector for lateral flow assays (LFAs), focusing on validation of the bench top LFA analysis system using thermo-speckle detection principles. Calibration and food spike testing were performed for detection limit analysis, which yielded results of approximately 104 CFU/mL. In another project, a smart lid-based real-time detection system and drop-in biosensor prototype were designed and fabricated to improve the utility of our existing bioluminescence detection system. Sensitivity analysis was performed with a calibrated light source and bioluminescence samples to refine the design.
We achieved significant advancements in food product evaluation using LIBS (laser-induced breakdown spectroscopy) technology. The accomplishments include the comprehensive characterization of a benchtop LIBS instrument, providing detailed insights into its sensitivity, accuracy, and detection limits. Optimized LIBS measurement protocols were developed, enhancing efficiency and precision in food analysis with paper-based substrates for liquid samples. Extensive testing on various food products, such as hard cheeses, coffee beans, spices, olive oil, and balsamic vinegar, refined the system's capabilities. Algorithmic advancements for food group classification led to standardized spectral feature selection algorithms, significantly improving classification accuracy. In addition, the first prototype of a hybrid LIBS/Raman spectroscopy instrument was completed, combining the strengths of both spectroscopic methods; achieving about a 10% improvement in classification accuracy over individual methods. A data fusion system was created, integrating data from different spectroscopic techniques to enhance the accuracy and reliability of food authentication. A strategic collaboration with SciAps Inc. played a crucial role in adapting portable alloy analyzers for food fingerprinting. This partnership drastically reduced hardware prototyping time and expedited the development, testing, and validation processes.
Work also continued on two microfluidic paper-based systems to detect bacterial pathogens in a multiplexed assay. The first system has been enhanced by integrating image analysis protocols and detection in real food samples (e.g., lettuce) to make pathogen detection quantifiable by, 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 Salmonella Typhimurium and Listeria monocytogenes in real food samples (lettuce). We've designed a colorimetric system for detecting these pathogens, and for pesticides as well, both of which are relevant for food safety. Early results suggest our devices are efficient, selective, cost-effective, and suitable for rapid on-site screening of these hazards in water or food.
Another method is being developed to use nucleic acid sequence-based amplification (NASBA) method to detect Shiga toxin-producing E. coli (STEC) strains in the field targeting pathogen-specific RNA. We have demonstrated that an assay using NASBA can be used to specifically detect RNA and experiments are ongoing to determine if this method can be used to discriminate between live and dead bacterial cells. Such an assay can be helpful for preliminarily assessing food safety coupled with other enrichment methods such as filtration-based concentration.
We previously reported the isolation, characterization, and broad geographic distribution of the newly discovered lettuce phylloplane yeast, Sporobolomyces lactucae (S. lactucae). We are currently designing experiments to test whether S. lactucae may be suitable as a prophylactic control method, to limit the introduction of human pathogens on leafy green vegetable leaf surfaces. In addition, efforts to develop a novel foodborne pathogen sensor from S. lactucae are well underway. Our goal is to develop a living yeast biosensor that can detect pathogens in situ without the need for sampling, destructive testing or compicated assays. The approach involves integrating a reporter gene into the S. lactucae genome under the control of an endogenous promoter that responds to the presence of a pathogen. To this end, the genome sequence of S. lactucae was determined at the Purdue Genomics Core Facility. The sequence data was cleaned, subjected to quality control check, assembled, and annotated. Analysis of the annotated genome revealed several pathways for potential signal amplification in the presence of a pathogen. Confrontation experiments between S. lactucae and E. coli were designed and carried out under a variety of conditions to identify E. coli responsive genes. To date, RNA amplification from these trials has not been successful. Refinements to the experimental procedure are being planned and additional experiments will be conducted in the fall of 2024. Once identified, E. coli-responsive promoters will be used to drive the expression of an indicator gene to generate a detectable signal. The bpsA and sfp genes, whose products convert the food-safe amino acid glutamine to the blue reporter molecule indigoidine, have been cloned and validated as potential reporters. Current studies are underway to determine optimal production levels of these genes to reduce false positives/negatives. New genetic tools will be needed to deliver and integrate the reporter construct into S. lactucae. We have optimized electroporation methods to deliverDNA to the yeast, and are evaluating several methods to integrate that DNA into the S. lactucae chromosome. We have evaluated random integration (non-homologous end joining) and are now pivoting to directed integration to specific sites within the genome (homologous recombination). Another challenge is providing DNA that is properly read by our platform. We have evaluated validated sequences from genetic relatives (Rhodospiridium toruloides), which do not work under the conditions tested. We are now evaluating sequences from the S. lactucae genome for function and potential reprogramming the cell as a sensor. Finally, we are optimizing the expression of key enzymes to generate a clear sensor output.
In another project, work has also been done to improve the design of a high-resolution, low-cost, wireless, and battery-less temperature sensor system for food safety assurance. The system has temperature resolution of 0.2 °C between -20 °C and 20 °C and distance resolution of 8 cm between the designed flex antennas. An improved system was designed to reduce power dissipation and size, and a simplified approach to integrate temperature sensing capabilities. Initially, the transmitter and receiver had continuous power dissipation of 1.7974 W. The improved system is integrated to have a lower profile with continuous power dissipation of 0.4684 W which is roughly a 4x improvement. This adaptation easily integrates with a commercially available product and improves the system's performance. With pulse operations, this adaptation has the potential to dissipate 4.075 mW. Work was also targeted towards further reduction in the cost of the disposable flex antenna using paper electronics. A temperature sensor and antenna made of paper with aluminum foil as electrodes would cost about 5 cents. This cost is further expected to go down to about a cent when mass produced. The sensor can also be integrated with the existing barcode labels on food packages, making this approach even more attractive and the most cost-effective option. A patch antenna was fabricated using a paper label sandwiched between aluminum foil electrodes. The stack was patterned using a low-cost, low-maintenance paper cutter. Measured performance of the patch antenna agreed with the values expected from simulation, indicating this is a promising approach. Production and deployment of this type of sensor could detect foods that have been temperature abused in transit and potentially reduce food waste.
Accomplishments
1. Detection and confirmation of Salmonella Typhimurium using a dual modality portable device. Detection of Salmonella contamination in foods 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 dual modality and portable mass- and optical-platform for detecting Salmonella. A smartphone-based device incorporating a mass-based quartz crystal microbalance (QCM) and a fluorescence imaging system was designed and constructed. This device allows for the detection of Salmonella-induced frequency shifts and captures fluorescence images of the cells for verification. The detection limit and specificity of the system were determined, as was the ability of the device to detect Salmonella in spiked samples using cantaloupe as a model system. This portable device could be deployed at low cost in just about any setting where pathogen detection is required.
2. Lateral flow-based pathogen detection by laser thermo-speckle imaging. Current lateral flow assay (LFA) systems (similar to the now ubiquitous COVID-19 home tests) are limited in their application for the detection of foodborne bacterial pathogens due to relatively high limits of detection. ARS-funded engineers at the Center for Food Safety Engineering in West Lafayette, Indiana, collaborated to create a portable LFA enhancement device to lower the detection limit. By applying the principle of laser thermo-speckle imaging, an imaging device captures speckle movements caused by changes in refractive index. Benchtop system algorithms were further challenged with a modified system design, temperature variability, and finally testing on cantaloupe contaminated with Salmonella as a model food system to increase the sensitivity by about 10-fold. This device could increase the utility of LFA assays in food safety and create a broader market for these types of tests.
3. Luminescence-based drop-in-biosensor (DIBS) for foodborne pathogen detection. Rapid detection of human pathogens in food samples remains a major challenge for food testing laboratories. Collaboration between ARS-funded scientists at the Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, led to the design and testing of in transit enrichment and monitoring devices that would speed detection. Two possible concepts were explored with one of them immersing the sensor module into the liquid sample while the other explored the possibility of a bottle-cap design where a bioluminescent signal was optically detected through protective windows on the lid. Feasibility studies were performed, and a bottle-cap design was chosen for improved user experience and field deployability. Deployment of such a device could lead to the ability to detect pathogens in samples while they are enroute to a testing facility, reducing overall time to detection.
4. Advancement in rapid detection of pesticides. The excessive use of pesticides in agriculture poses significant environmental and health risks globally. To help address the issue of pesticide residues on foods, a method was developed that could swiftly and accurately detect these chemicals. ARS-funded scientists at the Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, have developed a user-friendly paper-based device that identifies multiple pesticides simultaneously. The method employs nucleic acid molecules, called aptamers, that bind to different pesticides with high specificity, and an innovative color-changing system that reads pesticide concentration, enhancing its value as a quantitative tool. The results demonstrate linearity over a wide range of concentration ranges, with negligible changes in response for non-target chemicals. This new device is capable of detecting pesticides such as imidacloprid and carbendazim in real world samples like water from soybean crop irrigation. The development of this tool and its potential for on-site rapid screening is part of a broader effort to protect public health by ensuring the safety of water and food products.
5. Application of an optimized colorimetric analysis to the lateral flow detection of Salmonella Typhimurium. The rising occurrence of infectious outbreaks from foodborne pathogens like Salmonella Typhimurium presents significant economic, healthcare, and public health burden. To address this issue ARS-funded scientists at the Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, developed a rapid, reliable, and portable device capable of detecting Salmonella Typhimurium in real food samples, such as lettuce. Although the quantitative lateral flow immunoassay (LFIA, analogous to the home COVID-19 test) has a wide market presence, it sometimes experiences sensitivity issues. ARS scientists developed an optimized color-based readout method to address the sensitivity issues in LFIA quantification. By employing both color- and grayscale intensity-differences, the device provides accurate and quantitative signals. The developed device maintains high stability and performance for over eight weeks at room temperature, ensuring its reliability in real-world settings.
6. Development of a hybrid analytical instrument for food authentication. Food fraud and food adulteration are two major problems facing the food industry. To develop a robust system to help combat these problems, we have developed a hybrid instrument employing two spectroscopic methods, laser induced breakdown spectroscopy (LIBS) and Raman spectroscopy, for improved accuracy and reliability of food authentication. ARS-funded scientists at Purdue University Center for Food Safety Engineering in West Lafayette, Indiana, have tested the system on a wide variety of food products and developed standardized spectral feature selection algorithms and a data processing pipeline that integrates data from the different spectroscopic techniques. Integration of both types of spectroscopy into a single handheld unit will greatly facilitate the authentication of foods as well as detection of adulteration on site.