Progress 10/01/20 to 09/30/21
Outputs
PROGRESS REPORT Objectives (from AD-416): The goal of the research is to develop and validate early, rapid, sensitive and/or high-throughput methods and techniques for detecting biological and physical hazards in poultry (food) products with optical sensing methods and instruments. Thus, the nature of the research is to combine chemistry and engineering disciplines (optical, agricultural, and food) with microbiological techniques to solve food safety detection problems in poultry (food). Specific objectives are: Objective 1: Develop high-speed imaging methods for rapid detection of pathogens in live poultry flocks, and foodborne hazards, including foreign materials, in processed poultry products. Sub-objective 1A: Develop Salmonella surveillance system for early detection of diseased birds. Sub-objective 1B: Develop high-speed hyperspectral imaging methods and system for foreign material detection. Objective 2: Develop rapid methods and protocols for early detection, identification, and quantification of pathogens in poultry products (foods) using imaging spectroscopy. Sub-objective 2A: Develop hyperspectral microscope imaging (HMI) methods and system for early detection and identification of pathogen at the cellular level. Sub-objective 2B: Develop fluorescence in-situ hybridization (FISH) imaging methods to identify pathogenic bacteria at the cellular level. Sub-objective 2C: Develop nanobiosensor for pathogen detection with surface enhanced Raman spectroscopy (SERS) at the cellular level. Sub-objective 2D: Develop methods for intervention carryover for Salmonella detection. Sub-objective 2E: Develop methods for plate detection with optimized agar media at the colony level. Objective 3: Develop methods to detect biofilms in poultry processing facilities with optical technologies. In developing the methods assess if any biomarkers can be identified to enhance or improve the detection sensitivity or specificity. Approach (from AD-416): Ensuring poultry meat is safe to eat is of utmost importance to producers and consumers alike and rapid and early detection of foodborne pathogenic bacteria and foreign material in poultry products is needed. This research, which is divided into three objectives, primarily investigates optical sensors for rapid or improved detection of pathogenic bacteria with imaging and spectroscopic methods. Obj. 1A: an early-warning imaging surveillance system will be developed to detect bile in poultry droppings from laying hens in their cages. These higher levels of bile have been linked to birds with very high levels of Salmonella. Spectra will be collected to optimize key wavelengths and then a color-imaging system will be optimized for wireless real-time monitoring. Obj. 1B: Building on the success of a high speed hyperspectral imaging system developed within the unit, research will be expanded to detect foreign materials in various processed poultry products. Spectral libraries of normal meat features (muscle, fat, skin) and foreign material (rubber, metal, plastics, bone) will be used to develop algorithms suitable for high- speed use and then tested in real time. Obj. 2A: Hyperspectral microscope imaging (HMI) will be used to classify and quantify pathogens commonly found in poultry and other meats. The focus will be on identifying single bacteria cells from chicken rinsate by combining cell morphology and spectral profiles into an automated method for counting and classifying pathogenic bacteria. Additionally, markers will be used to enhance detection or means to separate and concentrate the bacteria will be implemented (immunomagnetic beads). Obj. 2B: Multiplex fluorescence in- situ hybridization (m-FISH) will be combined with HMI to further enhance detection with new protocols that will combine multiplexed probes and enhanced HMI detection resulting in broader, more robust methods of identification. Obj. 2C: Surface enhanced Raman spectroscopy (SERS), utilizing aptamers or antibodies and nano-enhanced surfaces, will be studied for Salmonella detection in broiler meat. Both labeled and label- free SERS will be evaluated. Obj. 2D: At FSIS⿿s request, research to neutralize sanitizers, frequently used to reduce pathogens while processing poultry meat, will be conducted to prevent interference of those sanitizers on bacterial analysis. Four potential neutralizing agents (quaternary ammonium, peroxyacetic acid, acidified sodium chlorite, acid solution, and dibromodimethylhydantoin sanitizers) will be screened for efficacy. Obj. 2E: Hyperspectral imaging (HI) systems will be used to classify pathogenic serovars growing in agar plates and collaborations will explore additional agar additives (both chromogenic and non- chromogenic) that will help differentiate serovars of E. coli O157:H7 and other shiga-toxin producing E. coli (STEC). Obj. 3: First in the lab, and then in processing plants, HI systems will be used and paired with spray- on markers to enhance the detection of biofilms on equipment surfaces. This research is potentially collaborative with ARS Beltsville and will help to discriminate biofilms from other organic material. This is the final report for the project 6040-42000-044-000D which ended in March 2021 and was replaced by project 6040-42440-001-000D, "Smart Optical Sensing of Food Hazards and Elimination of Non-Nitrofurazone Semicarbazide in Poultry," please refer to new project for additional information. Substantial results were realized over the 5 years of this project. In this research, we developed real-time hyperspectral imaging systems which can detect disease and contaminants on poultry carcasses with a common platform. A hyperspectral imaging system was also developed to automatically identify and count pathogenic bacterial colonies (Salmonella and E. coli.) growing on agar plates. Test results obtained from this research showed the potential of the imaging technique for rapid screening of pathogenic colonies which ensures the correct colonies were selected for further confirmation tests. Also, hyperspectral microscope imaging methods were developed for early and rapid detection of Salmonella serotypes which can speed up detection time by reducing the time needed for incubation. We also developed substrates and protocols for nanotechnology to detect and identify foodborne pathogens and toxins that can be further developed into a biosensor for commercial use with higher detection accuracies. Further research for the new project includes: integration of a real-time universal hyperspectral machine vision sensor with industrial picking device, such as a robot, to eliminate foreign materials during food processing; rapid and automated screening of food samples contaminated by pathogens; portable imaging system to identify foodborne pathogen with selective agar plates; fabricate devices with developed nanobiosensing methods; development of systems for early detection of Salmonella with hyperspectral microscope imaging; simulation, design and fabrication of microfluidics device for Salmonella detection in a food matrix; and development of methods to eliminate the production of semicarbazide during poultry processing. Record of Any Impact of Maximized Teleworking Requirement: Significant progress has been made on most of our research plan. However, maximized teleworking requirement hindered and delayed research progress that required data collection from experiments in laboratory. For sampling foodborne pathogens in chicken rinse, no experiment has been conducted since maximized teleworking requirement. Due to COVID-19 and vacancies and limited access to laboratory and in-plant poultry production lines for on-site experiments, we were not able to accomplish certain milestones as planned. Thus, we need to adjust and reprioritize our research plan to accomplish the goal as much as we can under COVID-19 maximized teleworking circumstances.
Impacts
(N/A)
Publications
- Sanchez-Hernandez, Juan C., Ro, Kyoung S., Szogi, Ariel A., Chang, Sechin, Park, Bosoon. 2021. Earthworms increase the potential for enzymatic bio- activation of biochars made from co-pyrolyzing animal manures and plastic wastes. Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat. 2020.124405.
- Wang, B., Park, B. 2020. Immunoassay biosensing of foodborne pathogen with surface plasmon resonance imaging: A review. Journal of Agricultural and Food Chemistry. https://dx.doi.org/10.1021/acs.jafc.0c02295.
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Progress 10/01/19 to 09/30/20
Outputs
Progress Report Objectives (from AD-416): The goal of the research is to develop and validate early, rapid, sensitive and/or high-throughput methods and techniques for detecting biological and physical hazards in poultry (food) products with optical sensing methods and instruments. Thus, the nature of the research is to combine chemistry and engineering disciplines (optical, agricultural, and food) with microbiological techniques to solve food safety detection problems in poultry (food). Specific objectives are: Objective 1: Develop high-speed imaging methods for rapid detection of pathogens in live poultry flocks, and foodborne hazards, including foreign materials, in processed poultry products. Sub-objective 1A: Develop Salmonella surveillance system for early detection of diseased birds. Sub-objective 1B: Develop high-speed hyperspectral imaging methods and system for foreign material detection. Objective 2: Develop rapid methods and protocols for early detection, identification, and quantification of pathogens in poultry products (foods) using imaging spectroscopy. Sub-objective 2A: Develop hyperspectral microscope imaging (HMI) methods and system for early detection and identification of pathogen at the cellular level. Sub-objective 2B: Develop fluorescence in-situ hybridization (FISH) imaging methods to identify pathogenic bacteria at the cellular level. Sub-objective 2C: Develop nanobiosensor for pathogen detection with surface enhanced Raman spectroscopy (SERS) at the cellular level. Sub-objective 2D: Develop methods for intervention carryover for Salmonella detection. Sub-objective 2E: Develop methods for plate detection with optimized agar media at the colony level. Objective 3: Develop methods to detect biofilms in poultry processing facilities with optical technologies. In developing the methods assess if any biomarkers can be identified to enhance or improve the detection sensitivity or specificity. Approach (from AD-416): Ensuring poultry meat is safe to eat is of utmost importance to producers and consumers alike and rapid and early detection of foodborne pathogenic bacteria and foreign material in poultry products is needed. This research, which is divided into three objectives, primarily investigates optical sensors for rapid or improved detection of pathogenic bacteria with imaging and spectroscopic methods. Obj. 1A: an early-warning imaging surveillance system will be developed to detect bile in poultry droppings from laying hens in their cages. These higher levels of bile have been linked to birds with very high levels of Salmonella. Spectra will be collected to optimize key wavelengths and then a color-imaging system will be optimized for wireless real-time monitoring. Obj. 1B: Building on the success of a high speed hyperspectral imaging system developed within the unit, research will be expanded to detect foreign materials in various processed poultry products. Spectral libraries of normal meat features (muscle, fat, skin) and foreign material (rubber, metal, plastics, bone) will be used to develop algorithms suitable for high- speed use and then tested in real time. Obj. 2A: Hyperspectral microscope imaging (HMI) will be used to classify and quantify pathogens commonly found in poultry and other meats. The focus will be on identifying single bacteria cells from chicken rinsate by combining cell morphology and spectral profiles into an automated method for counting and classifying pathogenic bacteria. Additionally, markers will be used to enhance detection or means to separate and concentrate the bacteria will be implemented (immunomagnetic beads). Obj. 2B: Multiplex fluorescence in- situ hybridization (m-FISH) will be combined with HMI to further enhance detection with new protocols that will combine multiplexed probes and enhanced HMI detection resulting in broader, more robust methods of identification. Obj. 2C: Surface enhanced Raman spectroscopy (SERS), utilizing aptamers or antibodies and nano-enhanced surfaces, will be studied for Salmonella detection in broiler meat. Both labeled and label- free SERS will be evaluated. Obj. 2D: At FSIS⿿s request, research to neutralize sanitizers, frequently used to reduce pathogens while processing poultry meat, will be conducted to prevent interference of those sanitizers on bacterial analysis. Four potential neutralizing agents (quaternary ammonium, peroxyacetic acid, acidified sodium chlorite, acid solution, and dibromodimethylhydantoin sanitizers) will be screened for efficacy. Obj. 2E: Hyperspectral imaging (HI) systems will be used to classify pathogenic serovars growing in agar plates and collaborations will explore additional agar additives (both chromogenic and non- chromogenic) that will help differentiate serovars of E. coli O157:H7 and other shiga-toxin producing E. coli (STEC). Obj. 3: First in the lab, and then in processing plants, HI systems will be used and paired with spray- on markers to enhance the detection of biofilms on equipment surfaces. This research is potentially collaborative with ARS Beltsville and will help to discriminate biofilms from other organic material. Development of database for phenotyping of biological materials with artificial intelligence (AI) and big data analytics: A generic framework of database system for big image data and AI research serving different applications is needed for safety and quality assessment of poultry meat. In support of Objective 1, ARS researchers in Athens, Georgia, developed generic data models and architecture for a database system for pathogenic microorganisms via AI and big data analytics. Data models reflecting hierarchical and embedded data relationships were designed for MongoDB, which is a cross-platform document-oriented database program (a NoSQL database program). The designed data models are being implemented for MongoDB applications. The research outcome will bridge a gap between big image data and AI technology to study the growth, health and safety of agricultural products, specifically from two case studies in soybean and poultry. Development of high-speed hyperspectral imaging technology for detection of foreign materials in poultry: Research to develop the high- speed hyperspectral imaging system for detection of foreign materials during the process of dried cranberries has demonstrated feasibility in rapid prototyping for similar applications during food processing. In support of Objective 1, ARS researchers in Athens, Georgia, has retrofitted the system to detect and sort foreign materials found during poultry processing. Research to spatially align the hyperspectral image channels of two different hyperspectral cameras (400-1,000nm; 1,000-2,500 nm) in the visible and near-infrared spectral range between 400 and 2,500 nm has been performed with diffeomorphic image registration. Research to develop hyperspectral/multispectral image processing and classification algorithms to rapidly discriminate foreign materials against chicken breast fillets in the entire visible and near-infrared spectral range of 400-2,500 nm is being conducted. Rapid identification of Campylobacter strains cultured under aerobic incubation using hyperspectral microscope imaging: Campylobacter is an organism of concern for food safety and is one of the leading causes of foodborne bacterial gastroenteritis. This pathogen can be found in broiler chickens, and the level of allowable contamination of processed poultry is regulated by federal agency guidelines. Traditional methods for detecting and isolating this pathogen from broiler chicken carcasses require time, expensive reagents, and artificially generated microaerophilic atmospheres. An aerobic medium that simplifies the procedure and reduces the expense of culturing Campylobacter has been recently described, and Campylobacter can be grown in this medium in containers that are incubated aerobically. In support of Objective 2, ARS researchers in Athens, Georgia, has developed a hyperspectral microscopic imaging (HMI) method for early and rapid detection of Campylobacter at the cellular level. The objective of this study was to utilize HMI to compare differences between Campylobacter cultures grown under artificially produced microaerobic atmospheres and cultures grown in aerobic medium. Hyperspectral microscopic images of three Campylobacter strains were collected cultures grown for 48 h microaerophilically and for 24 and 48 h aerobically, and a quadratic discriminant analysis was used to characterize the bacterial variability. Microaerobically cultured bacteria were detected with 98.7% accuracy, whereas detection accuracy of cultures grown in the novel medium was slightly reduced (-4.8 and -3.2% for 24 and 48 h, respectfully). The classification and spectral consistency were similar for cultures incubated in the aerobic medium for 24 h and cultures grown for 48 h under microaerobic conditions. Unsupervised prediction model for Salmonella detection with hyperspectral microscopy - A multi-year validation: Hyperspectral microscope imaging (HMI) have been previously explored as a tool for early and rapid detection of common foodborne pathogenic bacteria. A robust unsupervised classification approach to differentiate bacterial species with potential for single cell sensitivity is needed for a real-world application confirming the identity of pathogenic bacteria isolated from a food product. In support of Objective 2, ARS researchers at Athens, Georgia, developed a robust one-class soft independent model classification analogy (SIMCA) to determine if individual cells are Salmonella positive or negative. The model was constructed and validated with a spectral library built over five years, containing 13 Salmonella serotypes and 14 non-Salmonella foodborne pathogens. An image processing method designed to take less than one minute, paired with the one-class Salmonella prediction algorithm, resulted in an overall classification accuracy of 95.4%, with a Salmonella sensitivity of 0.97, and specificity of 0.92. SIMCA⿿s prediction accuracy was only achieved after a robust model incorporating multiple serotypes was established. These results demonstrate the potential for HMI as a sensitive and unsupervised presumptive screening method, moving towards early (< 8 h) and rapid (< 1 h) identification of Salmonella from food matrices. Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks: Conventional multivariate data analysis (MVDA) methods have been used for hyperspectral image (HI) analysis for classification of foodborne bacteria. However, MVDA methods are limited for big data analytics. In support of Objective 2, ARS researchers in Athens, Georgia, developed new data analytics and modeling paradigm with hybrid deep learning (DL) framework defined as ⿿Fusion-Net for rapid classification of foodborne bacteria at single-cell level. Hyperspectral microscope imaging (HMI) technology is useful for single-cell characterization, providing high resolution spatial, spectral and combined spatial-spectral profiles. However, direct analysis of these high-dimensional HMI data is challenging. In this work, HMI data were decomposed into three parts as morphological features, intensity images, and spectral profiles. Multiple advanced DL frameworks, including long- short term memory (LSTM) network, deep residual network (ResNet), and one- dimensional convolutional neural network (1D-CNN), were utilized, achieving classification accuracies of 92.2 %, 93.8 %, and 96.2 %, respectively. Taking advantage of fusion strategy, individual DL frameworks were stacked to form ⿿Fusion- Net that processed these features simultaneously with improved classification accuracy of up to 98. 4 %. This study demonstrated the ability of DL frameworks to assist HMI technology in single-cell classification as a diagnostic tool for rapid detection of foodborne pathogens. Deep learning techniques for detection of microbial colonies on agar plates: Deep learning-based hyperspectral image analysis showed promising results in a test trial with two sets of data to classify 15 different strains of the ⿿Big Six non-O157:H7 Shiga toxin producing Escherichia coli (STEC) plus O157:H7 E. coli colonies on modified Rainbow and modified MacConkey agar media. In support of Objective 2, ARS researchers in Athens, Georgia, have completed an experiment to collect one more replicate of the Big Six STEC and O157 in collaboration with ARS researchers at Clay Center, Nebraska. A high- performance computer with four graphics processing units for faster training of deep models has been installed for the analysis of the collected hyperspectral image data with artificial intelligent (AI) programming language such as Pytorch and Tensorflow. Development of a food grade dye for assessment of biofilm removal from stainless steel by cleaning and sanitizing agents: Microbiological contamination caused by ineffective sanitation procedures may lead to unacceptable risks in food production. As a result, poultry processors implement standard sanitary operating procedures (SSOP) to address sanitation conditions before, during, and after processing. An SSOP includes a pre-operational task by which selected areas that pose a high risk of contamination are visually inspected to assess sanitary conditions. Visual inspection may be limited due to contaminants being present in quantities too small to be seen by the human eye, though they still present a contamination risk. In support of Objective 3, ARS researchers in Athens, Georgia, evaluated a food grade dye for use in the development of a quantitative color difference methodology to measure the efficacy of cleaner/sanitizer solutions in removing biofilm components from stainless steel surfaces. Biofilms of Listeria and Pseudomonas were grown on food grade stainless-steel coupons, subjected to various cleaner/sanitizer treatments, and then stained with a dye. Resultant coupons were photographed and color differences between background and dyed area evaluated. Color differences conformed to a scale correlated with human visual perception. Results indicated that the method provides sensitivity for visual appraisal of treatment-response as well as species-response relationships. The method shows the potential as an enhancement for quantitative visual assessment of cleaning/sanitizing treatments of biofilms in a laboratory setting, and supplemental research is warranted to assess its efficacy for generally recognized as safe (GRAS) inspection of food processing environments as part of the Hazard Analysis Critical Control Point (HACCP) program. Accomplishments 01 Rapid and label-free immunosensing of Shiga toxin subtypes with surface plasmon resonance imaging. Shiga toxin-producing Escherichia coli (STEC) are responsible for gastrointestinal diseases reported in numerous outbreaks around the world. Current detection methods have limited implementation for rapid detection of high-volume samples needed for regulatory purposes. Surface plasmon resonance imaging (SPRi) has demonstrated simultaneous rapid and label-free screening of multiple pathogens. ARS researchers in Athens, Georgia, developed new SPRi method for rapid detection of Shiga toxins in collaboration with ARS researchers in Albany, California. Multiple antibodies were spotted on the same high-throughput biochip with a gold film. Multiple crosslinking and blocking steps were used to improve the orientation of antibodies on the biochip surface. Shiga toxins were detected based on the SPRi signal difference between immobilized testing antibodies and the control. Among the antibodies tested, Shiga toxin antibodies developed by researchers in Albany, California, showed high sensitivity for Shiga toxin detection rapidly with a low-level limit of detection (LOD). Furthermore, gold nanoparticles (GNPs) helped to amplify the SPRi signals of monoclonal antibodies in a sandwich platform and enhanced the sensitivity with the help of GNP-antibody conjugate. This result proved that a SPRi biochip, with selected antibodies, has the potential for rapid, high-throughput and multiplex detection of Shiga toxins.
Impacts
(N/A)
Publications
- Quyang, Q., Wang, L., Park, B., Kang, R., Wang, Z., Chen, Q., Guo, Z. 2020. Assessment of matcha sensory quality using hyperspectral microscope imaging technology. LWT - Food Science and Technology.
- Ouyang, Q., Yang, Y., Park, B., Kang, R., Wu, J., Chen, Q., Guo, Z., Li, H. 2019. A novel hyperspectral microscope imaging technology for rapid evaluation of particle size distribution in matcha. Journal of Food Engineering.
- Kang, R., Park, B., Eady, M.B., Ouyang, Q., Chen, K. 2020. Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks. Sensors and Actuators B: Chemical.
- Eady, M.B., Park, B., Hinton Jr, A. 2020. Rapid identification of Campylobacter species cultured under aerobic incubation using hyperspectral microscope imaging. Journal of Food Protection.
- Hinton Jr, A., Gamble, G.R., Berrang, M.E., Buhr, R.J., Johnston, J.J. 2019. Development of neutralizing buffered peptone water for salmonella verification testing in commercial poultry processing facilities. Journal of Food: Microbiology, Safety, and Hygiene. 10:359.
- Jia, B., Wang, W., Ni, X., Chu, X., Yoon, S.C., Lawrence, K.C. 2020. Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: A review. World Mycotoxin Journal. 13(2):163-177.
- Jia, B., Wang, W., Ni, X., Lawrence, K.C., Zhuang, H., Yoon, S.C., Gao, Z. 2020. Essential processing methods of hyperspectral images of agricultural and food products. Chemometrics and Intelligent Laboratory Systems. 198:Article 103936.
- Kang, R., Park, B., Eady, M.B., Ouyang, Q., Chen, K. 2020. Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks. Applied Microbiology and Biotechnology.
- Wang, B., Park, B., Chen, J., He, X. 2020. Rapid and label-free immunosensing of Shiga toxin subtypes with surface plasmon resonance imaging. Toxins.
- Gamble, G.R., Lawrence, K.C., Park, B., Yoon, S.C., Heitschmidt, G.W. 2019. Food grade dye for assessment of biofilm removal from stainless steel by cleaning and sanitizing agents. Food Protection Trends. Volume 39, Issue 6: Pages 442⿿448.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report Objectives (from AD-416): The goal of the research is to develop and validate early, rapid, sensitive and/or high-throughput methods and techniques for detecting biological and physical hazards in poultry (food) products with optical sensing methods and instruments. Thus, the nature of the research is to combine chemistry and engineering disciplines (optical, agricultural, and food) with microbiological techniques to solve food safety detection problems in poultry (food). Specific objectives are: Objective 1: Develop high-speed imaging methods for rapid detection of pathogens in live poultry flocks, and foodborne hazards, including foreign materials, in processed poultry products. Sub-objective 1A: Develop Salmonella surveillance system for early detection of diseased birds. Sub-objective 1B: Develop high-speed hyperspectral imaging methods and system for foreign material detection. Objective 2: Develop rapid methods and protocols for early detection, identification, and quantification of pathogens in poultry products (foods) using imaging spectroscopy. Sub-objective 2A: Develop hyperspectral microscope imaging (HMI) methods and system for early detection and identification of pathogen at the cellular level. Sub-objective 2B: Develop fluorescence in-situ hybridization (FISH) imaging methods to identify pathogenic bacteria at the cellular level. Sub-objective 2C: Develop nanobiosensor for pathogen detection with surface enhanced Raman spectroscopy (SERS) at the cellular level. Sub-objective 2D: Develop methods for intervention carryover for Salmonella detection. Sub-objective 2E: Develop methods for plate detection with optimized agar media at the colony level. Objective 3: Develop methods to detect biofilms in poultry processing facilities with optical technologies. In developing the methods assess if any biomarkers can be identified to enhance or improve the detection sensitivity or specificity. Approach (from AD-416): Ensuring poultry meat is safe to eat is of utmost importance to producers and consumers alike and rapid and early detection of foodborne pathogenic bacteria and foreign material in poultry products is needed. This research, which is divided into three objectives, primarily investigates optical sensors for rapid or improved detection of pathogenic bacteria with imaging and spectroscopic methods. Obj. 1A: an early-warning imaging surveillance system will be developed to detect bile in poultry droppings from laying hens in their cages. These higher levels of bile have been linked to birds with very high levels of Salmonella. Spectra will be collected to optimize key wavelengths and then a color-imaging system will be optimized for wireless real-time monitoring. Obj. 1B: Building on the success of a high speed hyperspectral imaging system developed within the unit, research will be expanded to detect foreign materials in various processed poultry products. Spectral libraries of normal meat features (muscle, fat, skin) and foreign material (rubber, metal, plastics, bone) will be used to develop algorithms suitable for high- speed use and then tested in real time. Obj. 2A: Hyperspectral microscope imaging (HMI) will be used to classify and quantify pathogens commonly found in poultry and other meats. The focus will be on identifying single bacteria cells from chicken rinsate by combining cell morphology and spectral profiles into an automated method for counting and classifying pathogenic bacteria. Additionally, markers will be used to enhance detection or means to separate and concentrate the bacteria will be implemented (immunomagnetic beads). Obj. 2B: Multiplex fluorescence in- situ hybridization (m-FISH) will be combined with HMI to further enhance detection with new protocols that will combine multiplexed probes and enhanced HMI detection resulting in broader, more robust methods of identification. Obj. 2C: Surface enhanced Raman spectroscopy (SERS), utilizing aptamers or antibodies and nano-enhanced surfaces, will be studied for Salmonella detection in broiler meat. Both labeled and label- free SERS will be evaluated. Obj. 2D: At FSIS⿿s request, research to neutralize sanitizers, frequently used to reduce pathogens while processing poultry meat, will be conducted to prevent interference of those sanitizers on bacterial analysis. Four potential neutralizing agents (quaternary ammonium, peroxyacetic acid, acidified sodium chlorite, acid solution, and dibromodimethylhydantoin sanitizers) will be screened for efficacy. Obj. 2E: Hyperspectral imaging (HI) systems will be used to classify pathogenic serovars growing in agar plates and collaborations will explore additional agar additives (both chromogenic and non- chromogenic) that will help differentiate serovars of E. coli O157:H7 and other shiga-toxin producing E. coli (STEC). Obj. 3: First in the lab, and then in processing plants, HI systems will be used and paired with spray- on markers to enhance the detection of biofilms on equipment surfaces. This research is potentially collaborative with ARS Beltsville and will help to discriminate biofilms from other organic material. Evaluate factors causing low recovery of Campylobacter during regulatory sampling following acidified sodium chlorite (ASC) treatment of broiler carcasses and subsequent carry-over into neutralizing Buffered Peptone Water (nBPW) rinses. Since some poultry processing plants use ASC treatments on broiler carcasses to reduce pathogens in their final products, ARS has developed a nBPW rinse to reduce the effect of these treatments on samples sent to the Food Safety Inspection Service (FSIS) for testing. Solution alkalinity was shown to positively correlate with the presence of un-reduced chlorite anion in the recovery broth, implying that low recovery of Campylobacter from rinses may be due to residual chlorite, an oxidizing agent. To demonstrate the susceptibility of Campylobacter to residual chlorite, three strains were inoculated into pH= 7.5 nBPW with or without addition of sodium chlorite and stored for 24 hours at 4oC prior to culturing. Microbial counts from the solutions indicated that residual chlorite can decrease recovery by up to 4 log CFU/ ml relative to controls. Acceptable recovery of Campylobacter from nBPW rinses containing residual chlorite may require development of a suitable neutralizing agent in the nBPW recovery medium. Reduction of Campylobacter on poultry thighs using sequential treatments of antimicrobials. Campylobacter is a major concern for poultry processors as USDA performance standards have become stricter. This study evaluated the use of a low pH processing aid and peracetic acid (PAA) applied either individually or in consecutive dip treatments to reduce Campylobacter in thighs. Thighs were inoculated with a C. coli marker strain and each dipped into bags containing 1 L of treatment 1 for 6 s. Thighs were allowed 5 s to drip, placed onto foil for 60 s, and dipped into treatment 2 for 6 s. After 5 s drip time, each was placed in a bag with 150 mL buffered peptone water and hand shaken for 60 s; controls involved the same procedure with no treatment. Rinsates were serially diluted, plated onto Campy Cefex agar with 200 ppm gentamicin and incubated microaerobically for 48 h at 42°C. Procedures were replicated 5 times. Significant reductions (P<0.05) compared to untreated using consecutive dips of low pH and PAA were 98.2% and 99.3%, respectively. Treatments of low pH followed by peracetic acid reduced Campylobacter 99. 2% from untreated thighs. Peracetic acid followed by low pH showed significant reductions compared to all other treatments (99.9% from untreated). This data suggests that treatment with an oxidizing agent (PAA) following by an acidic treatment maximizes Campylobacter reduction. Treating with this sequence may allow processors to meet the strict performance standards on Campylobacter in broiler parts. Machine learning with convolutional neural networks enhanced classification performance of bacteria with hyperspectral microscope imagery. Illnesses caused by foodborne pathogens have become a global issue in the food industry. A hyperspectral microscope imaging (HMI) method, combined with a Machine Learning technique called convolutional neural networks (CNN), demonstrated the potential to classify foodborne bacterial species at the cellular level. HMI can acquire unique spatially- resolved spectral features of different bacterial cells. For HMI data analysis, ARS researchers at Athens, Georgia applied two different CNN models, including U-net and one-dimensional CNN (1D-CNN), to classify foodborne pathogenic bacteria. The U-net algorithm was used for automatic segmentation of bacterial cells, which performed better for generating an accurate mask with a faster processing time than conventional segmentation methods using Otsu and Watershed algorithms. For classification with bacterial species including Campylobacter, E. coli, Listeria, Salmonella and Staphylococcus, the 1D-CNN algorithm achieved a higher accuracy of 93.9% than conventional classification algorithms such as k-Nearest Neighbor (79.6%) and Support Vector Machine (82.8%). Thus, these two novel machine learning methods for classification improved performance of HMI for classification of foodborne bacteria. Foodborne pathogenic bacteria identified and classified by visible/NIR hyperspectral microscopic imaging with deep learning algorithm. A hyperspectral microscope imaging (HMI) system, operating at wavelengths between 450-800 nm, allows the collection of spatial and spectral images from foodborne bacterial samples within 45 sec. Using these high- resolution contiguous spectral images, ARS researchers in Athens, Georgia utilized a novel deep learning method to identify and classify non-O157 Shiga toxin-producing Escherichia coli (STEC) including the ⿿Big Six (O26, O45, O103, O111, O121, and O145) at the serogroup level. The deep learning algorithm used stacked auto-encoder and soft-max regression (SAE- SR), to classify bacterial serogroups at the cellular level. The SAE-SR deep-learning algorithm performed with 97% classification accuracy and was much better than other classical algorithms such as linear discriminant analysis (LDA) with only 87.8% accuracy and support vector machine (SVM) with 94.5% accuracy. Thus, the newly developed deep learning algorithms classified the serogroups better than conventional chemometric models. High-throughput Shiga-toxin detection with immune-sensing technology and surface plasmon resonance imaging. Shiga toxin-producing Escherichia coli (STEC) are responsible for gastrointestinal illness and even death. Current detection methods, such as real-time PCR and enzyme immunoassay (EIA), lack the rapid detection with high-volume samples that are needed for regulatory agencies. ARS researchers in Athens, Georgia, have demonstrated an optical method with surface plasmon resonance imaging (SPRi) that has the potential for rapid, label-free, and simultaneous screening of multiple pathogenic bacteria. Recently, they extended the evaluation of SPRi for detection of Shiga-toxins (Stx1, Stx2) produced by E. coli. By collaborating with ARS researchers in Albany, California, they selected the optimal Shiga-toxin antibodies among eight different antibodies (Stx1pAb, 1-2mAb, 1d-3mAb, 1e-4mAb, Stx2pAb, 2-1mAb, 2-2mAb, and 2-10mAb) for use with a SPRi gold (50 nm) coated sensor chip through a mercaptoundecanoic acid monolayer and carbodiimide crosslinking, and subsequently blocked with 1% skim-milk proteins. Shiga-toxins were detected by SPR-sensorgram analysis of the difference images between target molecules and references. Among the eight antibodies tested, Stx1pAb and Stx1d-3mAb for toxoid 1A and Stx2-1mAb and Stx2-2mAb for toxoid 2A performed well with a 50-ng/mL limit-of-detection and detection time of 20 min. The results suggest that the SPRi method with selected antibodies has the potential for rapid and high-throughput Shiga-toxin detection. Deep learning techniques for detection of microbial colonies on agar plates. Research was conducted to evaluate the feasibility of deep learning-based hyperspectral image analysis to classify 15 different strains of the ⿿Big Six��� non- O157:H7 Shiga toxin producing Escherichia coli (STEC) plus O157:H7 E. coli colonies on each of two agar media types including modified Rainbow and modified MacConkey agar. Initial results indicated that the deep learning technique, known as 3D convolutional neural network, resulted in a detection accuracy of 88-92%, compared to earlier chemometric-based detection methods with 49-60% accuracy. Development of database for phenotyping of biological materials with artificial intelligence (AI) and big data analytics. An international collaborative research project is being conducted to develop a generic data models and architecture for a database system that can be optimized for phenotyping plants and pathogenic microorganisms via Artificial Intelligence and big data analytics. Preliminary research indicated that a database-type, known as NoSQL, was necessary for managing unstructured data such as images, metadata and research notes coming from various sources including laboratory instruments, field work, and sensors. Accomplishments 01 Reduction of Campylobacter on poultry thighs using sequential treatments of antimicrobials. Campylobacter is a major concern for poultry processors, as USDA performance standards have become stricter. ARS researhers in Athens, Georgia, evaluated the use of a low-pH processing aid and peracetic acid (PAA) applied in either individual- or consecutive-dip treatments to reduce Campylobacter in thighs. Thighs were inoculated with a marker strain of C. coli and then dipped into bags containing either the low pH processing aid or the PAA. Combinations of dual low-pH dips, dual PAA dips, low pH then PAA, and PAA then low-pH were all evaluated against a control of dual buffer dips. Peracetic acid followed by low pH dips showed significant reductions compared to all other treatments (99.9% from untreated). This data suggests that treatment with an oxidizing agent (PAA) following by an acidic treatment (low pH) maximizes Campylobacter reduction. Treating with this sequence may allow processors to meet the strict performance standards on Campylobacter in broiler parts.
Impacts
(N/A)
Publications
- Eady, M.B., Gayatri, S., Park, B. 2018. Detection of Salmonella from chicken rinsate with hyperspectral microscope imaging compared against RT- PCR. Talanta. 195:313-319.
- Park, B., Eady, M.B., Oakley, B., Yoon, S.C., Lawrence, K.C., Gamble, G.R. 2019. Hyperspectral microscope imaging methods for multiplex detection of Campylobacter. Journal of Spectral Imaging. 8:a6.
- Kang, R., Park, B., Chen, K. 2019. Identifying non-O157 Shiga toxin- producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images. Spectrochimica Acta.
- Landrum, M., Cox Jr, N.A., Cosby, D.E., Berrang, M.E., Gamble, G.R., Da Costa, M.J., Pesti, G.M. 2018. Low pH processing aid to lower the presence of naturally occurring campylobacter on whole broiler carcasses. Advanced Food and Nutritional Sciences. 3:7-13.
- Gamble, G.R., Berrang, M.E., Cosby, D.E., Cox, N.A., Hinton, A. 2019. Neutral pH Sodium Chlorite decreases recovery of Campylobacter in neutralizing buffered peptone water from simulated broiler carcass rinses. Journal of Food Safety.
- Landrum, M.A., Cox Jr, N.A., Wilson, J.L., Berrang, M.E., Gamble, G.R., Harrison, M.A., Fairchild, B.D., Kim, W.O., Hinton Jr, A. 2019. Reduction of campylobacter on poultry thighs using sequential treatments of antimicrobials. Advanced Food and Nutritional Sciences. 4:1-7.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report Objectives (from AD-416): The goal of the research is to develop and validate early, rapid, sensitive and/or high-throughput methods and techniques for detecting biological and physical hazards in poultry (food) products with optical sensing methods and instruments. Thus, the nature of the research is to combine chemistry and engineering disciplines (optical, agricultural, and food) with microbiological techniques to solve food safety detection problems in poultry (food). Specific objectives are: Objective 1: Develop high-speed imaging methods for rapid detection of pathogens in live poultry flocks, and foodborne hazards, including foreign materials, in processed poultry products. Sub-objective 1A: Develop Salmonella surveillance system for early detection of diseased birds. Sub-objective 1B: Develop high-speed hyperspectral imaging methods and system for foreign material detection. Objective 2: Develop rapid methods and protocols for early detection, identification, and quantification of pathogens in poultry products (foods) using imaging spectroscopy. Sub-objective 2A: Develop hyperspectral microscope imaging (HMI) methods and system for early detection and identification of pathogen at the cellular level. Sub-objective 2B: Develop fluorescence in-situ hybridization (FISH) imaging methods to identify pathogenic bacteria at the cellular level. Sub-objective 2C: Develop nanobiosensor for pathogen detection with surface enhanced Raman spectroscopy (SERS) at the cellular level. Sub-objective 2D: Develop methods for intervention carryover for Salmonella detection. Sub-objective 2E: Develop methods for plate detection with optimized agar media at the colony level. Objective 3: Develop methods to detect biofilms in poultry processing facilities with optical technologies. In developing the methods assess if any biomarkers can be identified to enhance or improve the detection sensitivity or specificity. Approach (from AD-416): Ensuring poultry meat is safe to eat is of utmost importance to producers and consumers alike and rapid and early detection of foodborne pathogenic bacteria and foreign material in poultry products is needed. This research, which is divided into three objectives, primarily investigates optical sensors for rapid or improved detection of pathogenic bacteria with imaging and spectroscopic methods. Obj. 1A: an early-warning imaging surveillance system will be developed to detect bile in poultry droppings from laying hens in their cages. These higher levels of bile have been linked to birds with very high levels of Salmonella. Spectra will be collected to optimize key wavelengths and then a color-imaging system will be optimized for wireless real-time monitoring. Obj. 1B: Building on the success of a high speed hyperspectral imaging system developed within the unit, research will be expanded to detect foreign materials in various processed poultry products. Spectral libraries of normal meat features (muscle, fat, skin) and foreign material (rubber, metal, plastics, bone) will be used to develop algorithms suitable for high- speed use and then tested in real time. Obj. 2A: Hyperspectral microscope imaging (HMI) will be used to classify and quantify pathogens commonly found in poultry and other meats. The focus will be on identifying single bacteria cells from chicken rinsate by combining cell morphology and spectral profiles into an automated method for counting and classifying pathogenic bacteria. Additionally, markers will be used to enhance detection or means to separate and concentrate the bacteria will be implemented (immunomagnetic beads). Obj. 2B: Multiplex fluorescence in- situ hybridization (m-FISH) will be combined with HMI to further enhance detection with new protocols that will combine multiplexed probes and enhanced HMI detection resulting in broader, more robust methods of identification. Obj. 2C: Surface enhanced Raman spectroscopy (SERS), utilizing aptamers or antibodies and nano-enhanced surfaces, will be studied for Salmonella detection in broiler meat. Both labeled and label- free SERS will be evaluated. Obj. 2D: At FSIS�s request, research to neutralize sanitizers, frequently used to reduce pathogens while processing poultry meat, will be conducted to prevent interference of those sanitizers on bacterial analysis. Four potential neutralizing agents (quaternary ammonium, peroxyacetic acid, acidified sodium chlorite, acid solution, and dibromodimethylhydantoin sanitizers) will be screened for efficacy. Obj. 2E: Hyperspectral imaging (HI) systems will be used to classify pathogenic serovars growing in agar plates and collaborations will explore additional agar additives (both chromogenic and non- chromogenic) that will help differentiate serovars of E. coli O157:H7 and other shiga-toxin producing E. coli (STEC). Obj. 3: First in the lab, and then in processing plants, HI systems will be used and paired with spray- on markers to enhance the detection of biofilms on equipment surfaces. This research is potentially collaborative with ARS Beltsville and will help to discriminate biofilms from other organic material. A red dye, F, D, & C Red #3, has been determined to be a suitable candidate for use in poultry processing establishments to disclose bacterial biofilms and other contaminants. The dye is approved by FDA for use in food, and a methodology has been developed by which the dye can be used as a stain to visually disclose the presence of biofilm extracellular polymeric substances (EPS), including proteins and carbohydrates, and individual gram-positive and gram-negative cells. In addition, the dye will disclose the presence of other proteins, carbohydrates, and fats on processing equipment resulting from inadequate cleaning and sanitation procedures. The new methodology will be of value to food plant managers as a tool to monitor cleaning and sanitation effectiveness. Listeria innocua and Pseudomonas putida biofilms were produced as models for pathogenic Listeria and Pseudomonas biofilms. Numerous cleaning and sanitizing agents currently utilized in food processing facilities were investigated for their abilities to both inactivate and remove biofilm matrices and cells. Biofilms were grown on stainless steel coupons and the coupon was subjected to cleaning/sanitizing treatments. Following each cleaner/sanitizer treatment on the model biofilms, a red dye methodology was used to stain the residual biofilm matrix material. Results indicate that most cleaning/sanitizing agents are able to inactivate the cells residing in the biofilm, but that the matrix and dead cells were not removed from the coupons. Only treatment of the biofilm using a mixture of enzymes was capable of completely removing the biofilms, but this treatment did not affect cell inactivation. The results suggest that in order for poultry processing plants to effectively inactivate and remove biofilms, a two stage process consisting of enzyme treatment followed by disinfectant treatment may be necessary. Additional data of E. Coli O157:H7 and other Shiga-Toxin producing E. Coli (STEC) strains, which have been identified by the Food Safety Inspection Service as adulterants, were collected. Both high-resolution digital images and hyperspectral images of pathogenic E. Coli growing on either Rainbow agar or a modified MacConkey agar, provided by ARS scientists at the Meat Animal Research Institute, Nebraska, were collected. Results from data analysis indicated that images of E. Coli pathogens growing on the modified MacConkey agar were no better at classifying the pathogens than images of the pathogens on Rainbow agar. Very high resolution digital images and high-resolution hyperspectral imaging both showed some promise at increasing prediction accuracies, especially the high resolution hyperspectral images of Rainbow agar, compared to lower resolution hyperspectral images collected in the past. However, accuracies were around 90 percent and not sufficiently better to warrant additional research in this direction. Next, high-resolution DSLR and hyperspectral images of E. Coli O157:H7 and the other STEC strains described above were also collected on R & F E. coli O157:H7 Chrome Medium and CHROMagar O26/O157 agars. Multiplex and Label-Free Screening of Foodborne Pathogens Using Surface Plasmon Resonance Imaging. In order to protect outbreaks caused by foodborne pathogens, more rapid and efficient methods are needed for pathogen screening from food samples. Surface plasmon resonance imaging (SPRi) is an emerging optical technique, which allows for label-free screening of multiple targets simultaneously with minimum or no sample preparation. ARS researchers at Athens, Georgia evaluated the feasibility of SPRi in simultaneous detection of four most important foodborne pathogens, Salmonella spp., Shiga-toxin producing E. coli, Listeria monocytogenes, and Campylobacter jejuni. The SPRi-biochip was functionalized with corresponding polyclonal antibodies and blocking agents. Bacterial cells were tested for performance of the multiplex SPRi method. The influence of antibody concentration and immobilization pH was optimized, and the specificity was evaluated. The cells were also fragmentized thermally and ultrasonically, whose SPRi signals were compared with intact cells. The results suggest that the new SPRi technique demonstrated the potentials in multiplex pathogen screening, and with further improvements in sensitivity, this platform could provide a flexible and automated means to pathogen detection in food matrices. Identification and detection of Campylobacter from poultry rinsate with hyperspectral microscope imaging. Campylobacter are the major group of bacteria responsible for foodborne gastroenteritis in humans. Most of the cases of campylobacteriosis have revealed their poultry origins so that various control measures have been employed both at the farm and processing levels. Campylobacter jejuni is the most common cause of bacterial foodborne illness in the United States. Current method for isolation and detection of Campylobacter from foods are culture-based techniques with several selective agars designed to isolate Campylobacter colonies. In addition, several immunological and molecular techniques are commercially available for the detection and identification of Campylobacter. However, most significant drawbacks of current commercially available methods include laborious, time-consuming, and expensive. ARS researchers at Athens, Georgia develop a hyperspectral microscope imaging (HMI) method to demonstrate the potential to identify Campylobacter from other foodborne pathogens including E. coli, Listeria, Staphylococcus, and Salmonella with high accuracy. Also, HMI method was able to classify C. coli., C. fetus, and C. jejuni with over 95% accuracy. Hyperspectral imaging for detection of bile on droppings from poultry. When the gallbladder becomes infected, inflammation can result in excretion of large amounts of bile in a manner that changes the color of excreta from brown to green. One such discoloration of poultry droppings results from hens infected with S. enteritidis. This occurrence raises the possibility that machine-vision techniques could be useful for detection of infected birds or those with other gastrointestinal illnesses. ARS researchers at Athens, Georgia developed a hyperspectral imaging (HSI) method to detect bile in droppings for possibly early detection of broilers infected by Salmonella. Using key wavelengths identified earlier with visible/near-infrared spectroscopy, HSI was able to identify small amounts of bile in poultry droppings. Additional image processing methods, including background separation, noise removal, and segmentation of bile contaminants, were used to identify potentially infected poultry flocks. The methods developed with HSI was further evaluated with low-cost color cameras and associated image processing for potential use as an early indicator of bird illness and/or infection. Accomplishments 01 Multiplex and label-free screening of foodborne pathogens using surface plasmon resonance imaging. In order to protect outbreaks caused by foodborne pathogens, more rapid and efficient methods are needed for pathogen screening from food samples. Surface plasmon resonance imaging (SPRi) is an emerging optical technique, which allows for label-free screening of multiple targets simultaneously with minimum or no sample preparation. ARS researchers at Athens, Georgia, evaluated the feasibility of SPRi for simultaneous detection of four important foodborne pathogens, Salmonella spp., Shiga-toxin producing E. coli, Listeria monocytogenes, and Campylobacter jejuni. The SPRi-biochip was functionalized with corresponding polyclonal antibodies and blocking agents. Bacterial cells were tested for performance of the multiplex SPRi method. The influence of antibody concentration and immobilization pH was optimized, and the specificity was evaluated. The results suggest that the new SPRi technique demonstrated the potential for multiplex pathogen screening, and with further improvements in sensitivity, this platform could provide a flexible and automated means to pathogen detection in food matrices.
Impacts
(N/A)
Publications
- Chen, J., Park, B. 2017. Effect of immunomagnetic bead size on recovery of foodborne pathogenic bacteria. International Journal of Food Microbiology. 267: 1-8.
- Chen, J., Park, B. 2018. Label-free screening of foodborne Salmonella using surface plasmon resonance imaging. Analytical and Bioanalytical Chemistry. 410:5455-5464. DOI:10.1007/s00216-017-0810-z.
- Berrang, M.E., Harrison, M., Meinersmann, R.J., Gamble, G.R. 2017. Self- contained chlorine dioxide generation and delivery pods for decontamination of floor drains. Journal of Applied Poultry Research. 26(3) :410-415. doi: 10.3382/japr/pfx009.
- Berrang, M.E., Gamble, G.R., Hinton Jr, A., Johnson, J. 2018. Neutralization of residual antimicrobial processing chemicals in broiler carcass rinse for improved detection of Campylobacter. Journal of Applied Poultry Research. doi:10.3382/japr/pfx071.
- Jiang, H., Yoon, S.C., Zhuang, H., Wang, W., Yang, Y. 2017. Evaluation of factors in development of Vis/NIR spectroscopy models for discriminating PSE, DFD and normal broiler breast meat. British Poultry Science. 58(6) :673-680.
- Eady, M.B., Park, B., Yoon, S.C., Haidekker, M., Lawrence, K.C. 2018. Methods for hyperspectral microscope calibration and spectra normalization from images of bacteria cells. Transactions of the ASABE. 61(2): 437-448.
- Eady, M.B., Park, B. 2018. Unsupervised classification of Salmonella Typhimurium from mixed bacteria cultures with hyperspectral microscope imaging. Journal of Spectral Imaging. DOI:10.1255/jsi.2018.a6.
- Seo, Y., Park, B., Yoon, S.C., Lawrence, K.C., Gamble, G.R. 2018. Morphological image analysis for foodborne bacteria classification. Transactions of the ASABE. 61(1): 5-13.
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Progress 10/01/16 to 09/30/17
Outputs
Progress Report Objectives (from AD-416): The goal of the research is to develop and validate early, rapid, sensitive and/or high-throughput methods and techniques for detecting biological and physical hazards in poultry (food) products with optical sensing methods and instruments. Thus, the nature of the research is to combine chemistry and engineering disciplines (optical, agricultural, and food) with microbiological techniques to solve food safety detection problems in poultry (food). Specific objectives are: Objective 1: Develop high-speed imaging methods for rapid detection of pathogens in live poultry flocks, and foodborne hazards, including foreign materials, in processed poultry products. Sub-objective 1A: Develop Salmonella surveillance system for early detection of diseased birds. Sub-objective 1B: Develop high-speed hyperspectral imaging methods and system for foreign material detection. Objective 2: Develop rapid methods and protocols for early detection, identification, and quantification of pathogens in poultry products (foods) using imaging spectroscopy. Sub-objective 2A: Develop hyperspectral microscope imaging (HMI) methods and system for early detection and identification of pathogen at the cellular level. Sub-objective 2B: Develop fluorescence in-situ hybridization (FISH) imaging methods to identify pathogenic bacteria at the cellular level. Sub-objective 2C: Develop nanobiosensor for pathogen detection with surface enhanced Raman spectroscopy (SERS) at the cellular level. Sub-objective 2D: Develop methods for intervention carryover for Salmonella detection. Sub-objective 2E: Develop methods for plate detection with optimized agar media at the colony level. Objective 3: Develop methods to detect biofilms in poultry processing facilities with optical technologies. In developing the methods assess if any biomarkers can be identified to enhance or improve the detection sensitivity or specificity. Approach (from AD-416): Ensuring poultry meat is safe to eat is of utmost importance to producers and consumers alike and rapid and early detection of foodborne pathogenic bacteria and foreign material in poultry products is needed. This research, which is divided into three objectives, primarily investigates optical sensors for rapid or improved detection of pathogenic bacteria with imaging and spectroscopic methods. Obj. 1A: an early-warning imaging surveillance system will be developed to detect bile in poultry droppings from laying hens in their cages. These higher levels of bile have been linked to birds with very high levels of Salmonella. Spectra will be collected to optimize key wavelengths and then a color-imaging system will be optimized for wireless real-time monitoring. Obj. 1B: Building on the success of a high speed hyperspectral imaging system developed within the unit, research will be expanded to detect foreign materials in various processed poultry products. Spectral libraries of normal meat features (muscle, fat, skin) and foreign material (rubber, metal, plastics, bone) will be used to develop algorithms suitable for high- speed use and then tested in real time. Obj. 2A: Hyperspectral microscope imaging (HMI) will be used to classify and quantify pathogens commonly found in poultry and other meats. The focus will be on identifying single bacteria cells from chicken rinsate by combining cell morphology and spectral profiles into an automated method for counting and classifying pathogenic bacteria. Additionally, markers will be used to enhance detection or means to separate and concentrate the bacteria will be implemented (immunomagnetic beads). Obj. 2B: Multiplex fluorescence in- situ hybridization (m-FISH) will be combined with HMI to further enhance detection with new protocols that will combine multiplexed probes and enhanced HMI detection resulting in broader, more robust methods of identification. Obj. 2C: Surface enhanced Raman spectroscopy (SERS), utilizing aptamers or antibodies and nano-enhanced surfaces, will be studied for Salmonella detection in broiler meat. Both labeled and label- free SERS will be evaluated. Obj. 2D: At FSIS�s request, research to neutralize sanitizers, frequently used to reduce pathogens while processing poultry meat, will be conducted to prevent interference of those sanitizers on bacterial analysis. Four potential neutralizing agents (quaternary ammonium, peroxyacetic acid, acidified sodium chlorite, acid solution, and dibromodimethylhydantoin sanitizers) will be screened for efficacy. Obj. 2E: Hyperspectral imaging (HI) systems will be used to classify pathogenic serovars growing in agar plates and collaborations will explore additional agar additives (both chromogenic and non- chromogenic) that will help differentiate serovars of E. coli O157:H7 and other shiga-toxin producing E. coli (STEC). Obj. 3: First in the lab, and then in processing plants, HI systems will be used and paired with spray- on markers to enhance the detection of biofilms on equipment surfaces. This research is potentially collaborative with ARS Beltsville and will help to discriminate biofilms from other organic material. Listeria innocua biofilms were produced as a model for pathogenic Listeria biofilms. A series of generally regarded as safe (GRAS) dyes, as well as non-GRAS dyes, were tested for their abilities to bind preferentially to specific components of the Extracellular Polymeric Matrix (EPM) formed by the biofilms. Results indicate that most of the dyes studied are not suitable for use as biofilm disclosing agents due to either 1) non-specificity, making discrimination of biofilm from other contaminants non-viable, or 2) low binding to any of the components of EPM, making observation of the dyed biofilm extremely difficult. One dye, Calcofluor White, exhibited promising results. The dye binds preferentially to the carbohydrate portion of the EPM, and under illumination with 365-nm radiation can disclose biofilms with high specificity compared to background contaminants also found in poultry processing environments. The challenge in implementing this dye as a tool to disclose biofilms in poultry processing environments is that it does not carry GRAS status, though material safety data sheet (MSDS) show no harmful effects. Label-free multiplex detection of foodborne pathogenic bacteria using surface plasmon resonance imaging (SPRi). Foodborne outbreaks caused by pathogenic bacteria are a serious public health concern. Traditionally such pathogens are detected individually which is time consuming. In order to improve the screening efficiency for common pathogens, it is desirable to detect multiplex pathogens in a single test to shorten the time and reduce the overall cost of food safety screening. ARS researchers at Athens, Georgia, have developed a method based on the SPRi technique to detect foodborne Salmonella cells on a sensor surface modified with anti-Salmonella antibody spots. Salmonella could be detected from chicken carcass rinse matrix without sample pre-treatment. This technique does not require expensive detection labels and is promising in multiplex screening of foodborne pathogens simultaneously, as the sensor chip can be modified with dozens of spots consisting of antibodies targeting different pathogenic species. Bacterial cell classification with hyperspectral microscopy. Hyperspectral microscope imaging (HMI) have shown potential as an early, rapid, and sensitive presumptive detection tool for identifying pathogenic bacteria on a cellular level. Classification accuracy of HMI was high at the serotype level of the same species of bacteria for Salmonella. However, to confirm HMI technology for pathogen detection, the consistency of spectral signatures from bacteria has to be validated. ARS researchers at Athens, Georgia, have investigated the impact of environmental growth stresses such as growth media, incubation temperature and pH on HMI cellular spectra. Testing the same Salmonella strain on a variety of environmental factors found that acidic pH values and high incubation temperatures influence the cellular spectra, most likely due to the outer membrane of the cell generating acid and heat response proteins. The bacteria�s growth media suggested that the cell�s spectra did alter slightly with media, while media with similar color indicators were spectrally similar. These results suggest that pathogens can be classified with a high accuracy if they are grown within a range of pH and temperature values which do not induce cellular stress. Visible/near-infrared (NIR) spectroscopy for detection of bile in poultry layer and broiler droppings. Bile is produced by the gallbladder and is a fluid with high salt content that aids in the digestion of lipids in the small intestine. When the gallbladder becomes infected, inflammation can result in excretion of large amounts of bile which changes the color of excreta from brown to green. Often extreme bile discoloration is an indicator of birds infected with Salmonella enteritidis. ARS researchers in Athens, Georgia, used visible and near infrared spectroscopy to identify the spectral characteristics of bile in droppings. This information can be used to monitor flock gut health with either a machine vision, multispectral imaging, or hyperspectral imaging systems which could result in a surveillance system for early identification of unhealthy birds. Advanced image processing techniques for detection of microbial colonies on agar plates. Although big data research in hyperspectral imaging is emerging in remote sensing and many tools and methods have been developed in many other applications such as bioinformatics, the tools and methods still need to be evaluated and adjusted in applications where conventional machine learning algorithms are still dominant. A preliminary study was conducted to find appropriate big data analytic tools and methods, with an emphasis on the state-of-art machine learning method known as deep learning. This deep learning method is being evaluated to identify foodborne pathogens on agar plates. Imaging modified MacConkey agar for Escherichia (E.) coli detection. Current methods of plating pathogenic E. coli result in ambiguity among the most concerning shiga-toxin producing E. coli (STEC), known as the �Big Six�, and other non-pathogenic background microflora. In recent years, an agar media was developed by ARS scientists at Clay Center, Nebraska, that has the potential to differentiate between the Big Six and background microflora. Currently, ARS scientists at Athens, Georgia, are collecting hyperspectral imaging data of the new agar growing the Big Six and the most concerning E. coli O157:H7. Data are also being collected on modified Rainbow agar to allow comparison to current methods. The results will be used in model development to screen agar plates for presumptive- positive colonies for further testing. Accomplishments 01 Neutralizing-buffered peptone water carcass wash. Broiler carcasses are tested for presence of human pathogens by a whole carcass rinse method using buffered peptone water (BPW). During the course of commercial broiler processing, carcasses are washed with various antimicrobial processing-aid chemicals to lower bacterial contamination. ARS researchers in Athens, Georgia, showed that enough of the antimicrobial chemical is carried with a broiler carcass into a carcass rinse sample to confound detection of Salmonella by continued antimicrobial action in the rinsate during simulated shipment to an offsite lab. The scientists developed a new medium, neutralizing � BPW (n-BPW) that was effective in counteracting four of the most common antimicrobial treatments currently used in broiler processing and allow detection of Salmonella. This is very important as it makes a false negative result far less likely. The new medium has been adopted by the Food Safety and Inspection Service for regulatory sampling of broiler carcasses. 02 Detection and characterization of Salmonella with immunomagnetic separation (IMS) and surface enhanced Raman spectroscopy (SERS). Current detection and characterization techniques for Salmonella are time consuming and rapid methods could benefit investigation and control of foodborne outbreaks. ARS researchers in Athens, Georgia, developed a method that combines the advantages of immunomagnetic separation (IMS) and surface enhanced Raman spectroscopy (SERS) techniques to detect and characterize Salmonella in 24 hours with high sensitivity and minimal sample preparation. After selectively capturing the pathogen from food using IMS and overnight culture, SERS spectra were collected and analyzed with multivariate statistical models. The detection and characterization accuracies were confirmed by traditional methods, and validated in mixture samples consisting of common foodborne bacteria. The specificity for detecting Salmonella from other species was higher than accuracies between individual Salmonella serotypes. Overall, the approach provides an inexpensive alternative to current methods, and with the expansion of spectral libraries, the serotyping specificity could be improved with more robust spectral analysis.
Impacts
(N/A)
Publications
- Chu, X., Wang, W., Yoon, S.C., Ni, X., Heitschmidt, G.W. 2017. Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging. Biosystems Engineering. 157:13-23.
- Gamble, G.R., Berrang, M.E., Buhr, R.J., Hinton Jr, A., Bourassa, D.V., Johnston, J.J., Ingram, K.D., Adams, E.S., Feldner, P.W. 2017. Neutralization of bactericidal activity related to antimicrobial carry- over in broiler carcass rinse samples. Journal of Food Protection. 80(4) :685-591.
- Park, B., Seo, Y., Eady, M.B., Yoon, S.C., Hinton Jr, A., Lawrence, K.C., Gamble, G.R. 2017. Classification of Salmonella serotypes with hyperspectral microscope imagery. Annals of Clinical Pathology. 5(2):1108- 1116.
- Wang, B., Park, B., Xu, B., Kwon, Y. 2017. Label-free biosensing of Salmonella enterica serovars at single-cell level. Journal of Nanobiotechnology (Biomed Central Open Access). 15:40.
- Chen, J., Park, B., Eady, M.B. 2017. Simultaneous detection and serotyping of Salmonellae by immunomagnetic separation and label-free surface enhanced Raman spectroscopy. Journal of Food Analytical Methods. 10:3181- 3193.
- Chen, J., Park, B., Huang, Y., Zhao, Y., Kwon, Y. 2017. Label-free SERS detection of Salmonella Typhimurium on DNA aptamer modified AgNR substrates. Journal of Food Measurement and Characterization. doi:10.1007/ s11694-017-9558-6.
- Eady, M.B., Park, B. 2016. Rapid identification of Salmonella serotypes through hyperspectral microscopy with different lighting sources. Journal of Spectral Imaging. 5(a4):1-10.
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