Research Project: Develop Rapid Optical Detection Methods for Food Hazards

Location: Quality and Safety Assessment Research Unit

2020 Annual Report

Objectives
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
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.

Progress Report
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
1. 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.

Review 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. https://doi.org/10.1016/j.lwt.2020.109254.
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. https://doi.org/10.1016/j.jfoodeng.2019.109782.
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. https://doi.org/10.1016/j.snb.2020.127789.
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. https://doi.org/10.4315/0362-028X.JFP-19-311.
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):163-178. https://doi.org/10.3920/WMJ2019.2510.
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. https://doi.org/10.1016/j.chemolab.2020.103936.
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. https://doi.org/10.4315/0362-028X.JFP-19-311.
Wang, B., Park, B., Chen, J., He, X. 2020. Rapid and label-free immunosensing of Shiga toxin subtypes with surface plasmon resonance imaging. Toxins. https://doi.org/10.3390/toxins12050280.
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.