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ARS Home Southeast Area Athens, Georgia U.S. National Poultry Research Center Quality and Safety Assessment Research Unit Research





- Park, Bosoon
- Yoon, Seung-Chul
- Bowker, Brian
- 2023
- 2022
- 2021
- Publications
- Food Safety, (Animal and Plant Products) (108)

Research Project: Smart Optical Sensing of Food Hazards and Elimination of Non-Nitrofurazone Semicarbazide in Poultry **Location:
                            Quality and Safety Assessment Research Unit** 2023 Annual Report **Objectives**
                                

1. Develop imaging technologies to detect and identify plastics during poultry processing with hyperspectral imaging and artificial intelligence. 
1A. Develop hyperspectral imaging technology for detection and identification of plastic foreign objects during poultry processing.
1B. Develop AI technology for enhanced detection and smart robotic removal of foreign materials in hyperspectral imagery during poultry processing.

2. Detection and identification of foodborne bacteria and toxins in poultry products with high-throughput hyperspectral microscopy and surface plasmon resonance imaging.
2A. Rapid monitoring of indicator microorganisms in poultry processing.
2B. Develop advanced hyperspectral microscope imaging (HMI) methods and system for label-free detection and identification of pathogens at the cellular level with no enrichment.
2C. Develop high-sensitive and selective immunoassay method and system for foodborne bacteria and toxin detection with surface plasmon resonance imaging.

3. Eliminate the production of semicarbazide in non-nitrofurazone treated poultry by optimization of antimicrobial treatments and/or alternative antimicrobials during processing.

4. Develop safe and effective poultry processing strategies (scalding-picking-evisceration procedures) to reduce foodborne contaminants (pathogens/chemical) and enhance the sustainability of poultry processing.
4a. Develop sustainable poultry processing using artificial intelligence (AI) technology to improve poultry food safety.
4b. Develop Internet of Things (IoT) technology with various sensing platforms and data analytics for smart poultry processing and safety. **Approach**
                                

Research on poultry safety will focus on: 1) developing and validating early, rapid, sensitive, and/or high-throughput optical sensing techniques for detecting physical and biological hazards in poultry products, and 2) eliminating semicarbazide in non-nitrofurazone treated poultry by optimization of antimicrobial treatments. In research on optical detection of physical hazards, spectroscopic and hyperspectral imaging (HSI) technologies will be developed for detection and identification of plastic foreign objects. A robot rejector and control software will be developed to eliminate foreign materials (FM) when detected by HSI. Artificial intelligence (AI) technology will be developed for enhanced detection and smart robotic removal of FM during poultry processing through the development and evaluation of customized deep learning algorithms based on hyperspectral imaging. A vision-guided smart robotic manipulator will be designed and built to remove FM by self-learning AI algorithms. To develop methods and techniques for detecting and identifying biological hazards, time-lapse image data on pure-culture indicator organisms and poultry carcass rinses from different processing locations will be collected to build a library, which will be used for on-line counting of microcolonies to build prototype systems.  To detect foodborne pathogens, hyperspectral microscope imaging (HMI) methods will be developed with a spectral library of various pathogens using two HMI platforms of acousto-optical tunable filter (AOTF) and Fabry-Perot interferometer (FPI). In accordance with optimization of parameters on HMI and hypercubes, a transportable HMI system will be developed embedded with AI-based software for classification and identification. To identify foodborne bacteria and toxins, a highly-sensitive and selective immunoassay method and system will be developed using surface plasmon resonance imaging (SPRi). Microfluidic devices will be designed, simulated and fabricated for bacterial enrichment and separation. Both materials and parameters to develop a 3D printed biosensor for multiplex detection of pathogenic bacteria and toxins will be optimized and evaluated with food samples. Finally, a portable 3D printing platform for biosensor fabrication by integrating sample enrichment cartridge, biochip and SPRi detector will be developed. To develop techniques for eliminating the production of semicarbazide (SEM) in non-nitrofurazone treated poultry, a methodology for SEM analysis in chicken meat and a data library relating poultry processing conditions to SEM formation will be developed. Specifically, SEM in chicken leg quarters obtained from multiple processing facilities will be analyzed and methods to eliminate SEM production in poultry products under processing conditions will be developed. **Progress Report**
                                

Research was conducted to develop hyperspectral imaging technology for detection and identification of plastic foreign objects during poultry processing (Sub-Objective 1A). Researchers made significant progress on an engineering project to develop a prototype imaging system for the detection and classification of plastic foreign materials on poultry meat by acquiring a near-infrared pushbroom hyperspectral camera producing up to 670 image frames/second and successfully constructing a race-track-like conveyor system with a closed loop. The closed loop conveyor system was specifically designed to enable continuous sample traveling (including chicken parts and foreign materials) for imaging purposes, utilizing the capabilities of the high-speed near-infrared hyperspectral camera to achieve real-time image acquisition and processing. Furthermore, progress was made on the design and in-house fabrication of a camera mount tailored for the prototype system. These system components will be integrated into the final design of the imaging system prototype.

Progress was made on research to develop an artificial intelligence (AI) technology for enhanced detection and smart robotic removal of foreign materials in hyperspectral imagery during poultry processing (Sub-objective 1B).  A real-time hyperspectral imaging (HSI) technology, incorporating high-performance deep learning (DL), is being developed to detect foreign materials, including plastics, on chicken breast meat. The challenge lies in achieving online real-time sensing capabilities critical for industrial deployment, given the computational demands of both HSI and DL. Researchers addressed this challenge by implementing hardware and software suitable for real-time HSI-based DL inferencing. To meet the processing time constraint of approximately 0.25 second/chicken breast fillet, the software system was designed to adopt parallel computing in both CPU and GPU, allowing for the parallelization of hyperspectral data acquisition and processing. Work done in FY2023 showed this approach enhanced the efficiency and speed of data handling. Researchers worked to design the system so that acquired and pre-processed hyperspectral data in the CPU are transferred to a GPU for foreign material detection using a previously developed DL model that employs CPU threads to mitigate transfer latency through asynchronous creation of multiple streams and overlapping multiple transfers with kernel execution. Within the GPU, tensor cores are then utilized to perform inference on the DL-based model trained to identify spectral responses associated with foreign materials. These advancements to address high computational demands will enable real-time detection of foreign materials, leveraging the power of HSI and DL technologies in the safety assessment of chicken meat products.

Significant progress was made on a project on rapid monitoring of indicator microorganisms in poultry processing (Sub-objective 2A). Multiple time-lapse recordings were made of indicator microorganisms (non-pathogenic generic E. coli, Listeria, and Pseudomonas) growing on brain heart infusion (BHI) agar and standard methods agar (SMA). Three incubators were modified to capture high-resolution images (45.7MP) using a digital color camera at one-minute intervals over a typical 24 h incubation period. A total of 14 time-lapse recordings were completed, including repetitions of each bacterium on both agars. Change detection analyses were performed, resulting in binary images showing significant changes relative to the initial state image. Color time-lapse videos were created, and statistical means were computed and plotted against the timeline, providing a quantitative view of colony growth. Similar analyses were performed on Shiga toxin-producing E. coli (STEC) on chromogenic Rainbow agar, resulting in 15 additional data sets. The preliminary results showed that the average time for detecting microcolony growth and counting colonies was approximately 7, 14, and 15 h after incubation on BHI for generic E. coli, Listeria, and Pseudomonas, respectively. On SMA, average time was 9 h for generic E. coli and 16 h for Listeria. A low-angle darkfield illuminator was tested and found to offer higher contrast for future imaging purposes where bacterial color is not crucial.

Significant progress was made to develop advanced AI methods for hyperspectral data analytics for label-free detection and identification of pathogens at the cellular level with no enrichment (Sub-objective 2B) using three approaches. First, research was conducted on bias removal from spectral data of pathogenic bacteria. The use of hyperspectral microscope imaging (HMI) in conjunction with deep learning (DL) has proven effective and efficient in identifying pathogenic bacterial cells. However, it was observed that models trained on HMI datasets collected in different years were spectrally biased, limiting their generalizability. To address this issue, ARS researchers worked to identify the source of bias and developed AI methods to remove bias while preserving bacterial detection capability. Following bias removal, the accuracy of AI classification models improved from 60% to 98%. This bias-removal method not only enabled model building for accurate and robust bacterial detection using a large amount of existing data but also allowed its application to new HMI datasets without the need for per-image calibration, which is particularly challenging for image acquisition using HMI systems. Second, incremental learning for efficient, accurate, and robust detection of foodborne bacteria was researched. Given the significant variation in spatial-spectral features of single-cell bacteria, it becomes crucial to update the detection model incrementally to adapt to practical data collection schedules and reduce computational memory requirements for big data during model training. Researchers at ARS also devised a new method for chunk-based incremental learning. This approach involves storing a minimal number of previous instances to retrain and update the model effectively with a new chunk of data. By employing this method and utilizing less than 40% of previous instances, the Fusion-Net model achieved a 99% test accuracy. When combined with the bias removal method, this incremental learning approach can further reduce the percentage of previous instances required for updating the model to detect foodborne bacteria. Third, model evaluation and redesign for bacterial detection using explainable AI were investigated. Through the utilization of explainable AI methods, ARS researchers discovered two issues with the existing training methods of the highly accurate Fusion-Net model for bacterial detection. Firstly, one of its subnetworks was not fully trained, and secondly, another subnetwork did not contribute significantly to overall model performance. To overcome these challenges, ARS researchers removed the non-contributing subnetwork from the Fusion-Net architecture followed by developing an algorithm that enabled simultaneous and complete training of all subnetworks while incorporating performance measurements for each subnetwork. The resulting Fusion-Net model exhibited similar high accuracy as before in bacterial detection but demonstrated enhanced robustness by utilizing all spatial-spectral features in HMI. This redesigned model was successfully employed for bacterial viability detection, achieving 100% accuracy, and species classification with a 98% test accuracy.
During FY2023 significant progress was made to fabricate and test microfluidic devices for rapid detection of foodborne bacteria, such as Escherichia coli and Salmonella (Sub-objective 2C). Compared to conventional cultivation and molecular biology-based methods, microfluidics-based methods provide more freedom and flexibility for manufacturing biosensors in the lab and conducting rapid bacteria detection. ARS researchers worked with an external collaborator to conduct research on the fabrication of microfluidic devices for collecting foodborne bacteria. The microchannels were fabricated using two different technologies, 1) stereo-lithography (SLA) 3D printing and 2) computer numerical control (CNC) milling. To test the particle separation performance, fluorescent particles with different diameters were used to mimic bacteria and interfering particles in food matrices. At the same time, ARS researchers made progress in building a new fabrication platform based on state-of-the-art micro/nano 3D printing technology.
Research progress on optimizing antimicrobial treatments and/or alternative antimicrobials during processing as a means to eliminate production of semicarbazide in non-nitrofurazone treated poultry (Objective 3) was slowed due to a critical scientist vacancy. The recruitment process for filling this position was initiated.

Research to develop safe and effective poultry processing strategies to reduce foodborne contaminants and enhance the sustainability of poultry processing (Objective 4) was initiated. As part of a formal ARS agreement, university collaborators conducted research to determine the efficacy of utilizing controlled atmosphere stunning (CAS) in broiler slaughter as a means to enhance processing sustainability. Trials were conducted to determine the effects on food safety and processing efficiency. As part of this research effort, ARS researchers completed an initial trial to determine the interacting effects of the collaborator’s CAS system and different carcass deboning times on poultry meat quality and functionality. **Accomplishments**
                                

1. Developed Surface-enhanced Raman Spectroscopy (SERS) method with nanoparticle substrates for Salmonella detection in chicken rinse. Salmonella is a foodborne pathogenic bacteria commonly found on broiler chickens during processing that is responsible for causing gastrointestinal illnesses. ARS researchers in Athens, Georgia, developed a Salmonella detection method that reduces the necessary time for confirmation, by collecting Surface-Enhanced Raman Spectroscopy (SERS) spectra from bacteria colonies, applied to a substrate of biopolymer encapsulated silver nanoparticles. Chicken rinses containing Salmonella Typhimurium (ST) were analyzed by SERS and compared to traditional plating and polymerase chain reaction (PCR) methods. Analyses showed that SERS spectral features of ST and non-Salmonella colonies were significantly different. A support vector machine (SVM) classification algorithm was able to separate ST and non-Salmonella samples with an overall classification accuracy of 96%. Findings suggest that SERS is a highly accurate tool for pathogen detection that may be useful for regulatory purposes. 2. Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods. Foodborne illness is a significant threat to food safety and public health. A leading cause of foodborne illness is food contamination with pathogenic bacteria. It is crucial to identify pathogenic bacteria in contaminated food as early as possible. Hyperspectral microscope imaging (HMI) utilizes spectral-spatial features to identify pathogenic bacteria with a high level of accuracy. However, bacterial detection with dark-field HMI requires accurate segmentation of single-cell bacteria from hyperspectral images. ARS researchers in Athens, Georgia, developed a method to automatically segment single-cell pathogenic bacteria using deep learning and image processing for bacterial segmentation to identify a single-cell. To validate a method Escherichia coli, Listeria, Salmonella, and Staphylococcus were used to acquire hyperspectral imaging (hypercube) of bacterial cells under different growth conditions. Based on the hypercube, four different deep learning models were developed and evaluated for bacterial cell segmentation. AI-based automated segmentation methods performed with over 94% accuracy. This accurate and robust auto-segmentation technique streamlined the detection of pathogenic bacteria with HMI by reducing processing time from raw image acquisition to classification within 15 seconds. 3. Classification between live and dead foodborne bacteria with machine learning. Identification of live foodborne bacteria is essential for ensuring food safety and preventing foodborne illnesses. Accurate techniques for assessing bacteria viability are needed. ARS researchers in Athens, Georgia, developed deep learning methods for hyperspectral data analytics to accurately distinguish between live and dead foodborne bacteria based on their spectral and morphological features. Three deep learning models (Fusion-Net I, II, and III) were developed and evaluated for their ability to classify live and dead bacterial cells of six pathogenic strains, including Escherichia coli (EC), Listeria innocua (LI), Staphylococcus aureus (SA), Salmonella Enteritidis (SE), Salmonella Heidelberg (SH), and Salmonella Typhimurium (ST). Fusion-Net I achieved high accuracy in identifying live bacterial cells, with a classification accuracy of 100% for LI, SE, ST strains and over 92% for EC, SA, SH. Fusion-Net II and III models were even more robust, achieving 100% accuracy consistently in classifying dead cells in all six strains. Fusion-Net III also showed the ability to identify bacterial strains over 96% accuracy, making it a dual-task model with potential applications for early detection of live foodborne bacteria prior to outbreaks. These findings suggest that the use of hyperspectral microscope imaging and deep learning models could provide a new tool for identifying bacterial viability quickly and accurately, thereby improving the efficiency and reliability of food safety inspection. 4. Design of microfluidic devices for biosensor systems. Some of the major challenges of rapid bacteria detection in food matrices include: 1) limited sensitivity and specificity, and 2) high-throughput capacity to detect multiple bacteria at the same time. To solve these two problems, ARS researchers in Athens, Georgia, developed passive microfluidic devices which can separate, enrich, and detect bacteria from food matrices without applying incubating processes to the samples prior to detection. The passive microfluidic designs simplify the overall biosensor structure and make it easy to operate. Designs of microfluidic devices were achieved by fluidic dynamic simulations with various channel geometries, finding out the optimized microchannel sizes and geometries for the separation and enrichment of bacteria size particles from larger and smaller interfering particles in sample solutions. The passive microfluidic designs were useful to integrate multiple functions and high-throughput capacity into a simple and streamlined biosensor system to detect foodborne bacteria without conventional enrichment process. 5. Semicarbazide in chicken leg quarters obtained from multiple processing facilities. In recent years, the presence of semicarbazide in poultry has been confounding the U.S. poultry industry and causing export restrictions. Semicarbazide is a regulatory marker for the use of nitrofurazone, an antibiotic banned from use in animals intended for human consumption. ARS researchers in Athens, Georgia, conducted a survey of commercial processing plants to identify potential sources of semicarbazide increases in poultry during processing. Data indicated that semicarbazide increased during processing in some plants but not others. For those plants demonstrating an increase in semicarbazide during processing, chill tank conditions were identified as the primary source of processing induced semicarbazide formation. Further research is being conducted to assess chill tank parameters necessary to minimize the chemical production of semicarbazide during processing, primarily by pH regulation. These data are critical to the U.S. poultry industry to provide a basis to reopen export markets. **Review Publications**
                            *Park, B., Shin, T., Cho, J., Lim, J., Park, K. 2022. Improving blueberry firmness classification with spectral and textural features of microstructures using hyperspectral microscope imaging and deep learning. Postharvest Biology and Technology. https://doi.org/10.1016/j.postharvbio.2022.112154.*
                                *Eady, M.B., Setia, G., Park, B., Wang, B., Sundaram, J. 2023. Biopolymer encapsulated AgNO3 nanoparticle substrates with surface-enhanced Raman spectroscopy (SERS) for Salmonella detection from chicken rinse. International Journal of Food Microbiology. https://doi.org/10.1016/j.ijfoodmicro.2023.110158.*
                                *Wang, Q., Childree, E., Box, J., Lopez-Vela, M., Sprague, D., Cherones, J., Higgins, B. 2023. Microalgae can promote nitrification in poultry-processing wastewater in the presence and absence of antimicrobial agents. ACS ES&T Engineering. https://doi.org/10.1021/acsestengg.2c00360.*
                                *Park, B., Shin, T., Kang, R., Fong, A., Mcdonogh, B., Yoon, S.C. 2023. Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.107802.*
                                *Park, B., Shin, T., Wang, B., Mcdonogh, B., Fong, A. 2023. Classification between live and dead foodborne bacteria with hyperspectral microscope imagery and machine learning. Journal of Microbiological Methods. https://doi.org/10.1016/j.mimet.2023.106739.*


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