Research Project: Smart Optical Sensing of Food Hazards and Elimination of Non-Nitrofurazone Semicarbazide in Poultry

Location: Quality and Safety Assessment Research Unit

2024 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 materials (FM) during poultry processing (Sub-objective 1A). Researchers made significant progress on an engineering project to develop a novel sensor fusion methodology that integrates deep learning and two powerful imaging techniques - color and hyperspectral imaging. The developed fusion technology utilizes deep learning algorithms based on You Only Look Once (YOLO) and Convolutional Neural Network (CNN) models. The YOLO version 8 Extra Large (YOLOv8x) and YOLOv7x were applied to color images, demonstrating good performance at detecting plastic pieces with 99% accuracy for 5 mm x 5 mm FM pieces and 80% accuracy for 2 mm x 2 mm FM pieces on chicken breast fillets. A one-dimensional CNN model was applied to analyze the detected FM objects in hyperspectral images (600 - 1700 nm) and predict their plastic types, providing a detection accuracy of 97% in identifying 12 different plastic types. This research underscores the importance of cutting-edge artificial intelligence (AI) technologies in tackling FM detection within the poultry industry. This sensor fusion methodology, with its proven effectiveness, will be further refined to enhance its impact, potentially increasing food safety standards and quality control measures for the poultry industry. Research was conducted to develop AI technology for enhanced detection and smart robotic removal of foreign materials (FM) in hyperspectral imagery during poultry processing (Sub-objective 1B). Researchers made progress on developing a high-performance deep learning (DL) model specifically designed for high-speed and high-throughput detection of FM in poultry meat. While DL models offer exceptional accuracy, their computational demands during inferencing time can hinder real-time predictions when dealing with the large volume of data generated by real-time hyperspectral imaging systems, especially for inline poultry processing applications. To address this challenge, researchers developed a real-time inferencing model based on NVIDIA TensorRT, a software development kit for high-performance DL inference on NVIDIA Graphics Processing Unit (GPU). The model was optimized for real-time processing with fixed numerical precisions on TensorRT. The optimized model for GPUs achieved excellent results for inference time performance, achieving 17 times faster processing compared to CPUs and 6 times faster than standard GPUs, without compromising detection accuracy. This development holds promise for further enhancing performance of real-time FM detection and classification using AI-based hyperspectral imaging in poultry processing. Progress was made on research for identification of the root cause of the generalization issue (Sub-objective 2B). The use of hyperspectral microscope imaging (HMI) with deep learning (DL) has proven effective in identifying pathogenic bacterial cells. However, models trained on HMI datasets from different years showed spectral-spatial bias, affecting their generalizability. Researchers investigated this issue and found that the bias was due to differences in brightness and blurriness of bacterial cells in hyperspectral images. To address this, researchers developed a software package to measure and maintain consistent brightness and blurriness during hyperspectral image acquisition. Implementing this software is expected to significantly reduce bias across datasets. Significant progress was made on research for development of a trustworthy detection model with uncertainty measurements (Sub-objective 2B). Like most AI models, the previous detection model showed low accuracy with adversarial data points despite high performance with data similar to the training set. To address this inconsistency, researchers (a) explored various methods to measure uncertainties in deep learning models, (b) implemented deep k-nearest neighbors using inductive conformal prediction as the optimal framework, and (c) developed a new bacterial detection model that leverages uncertainty measurements from sub-models with different modalities. This advanced model improved accuracy on adversarial data points (unseen data) from 40% to 89% while maintaining high accuracy on data similar to the training set (seen data). These findings suggest that (a) a reliable and generalizable detection model for foodborne bacteria can be constructed from non-generalizable sub-models using uncertainty measurements, and (b) adding more sub-models for additional modalities may further enhance performance on both seen and unseen data. Progress was made on research for development of the two-photon lithography (TPL) technique for the 3D printing of the desired microfluidic devices (Sub-objective 2C). TPL is a high-resolution 3D printing technique that uses high-intensity focused lasers to produce features smaller than the size of the focused light spot. Researchers developed the Projection TPL (P-TPL) technique, which is a high-throughput variant of TPL and demonstrated that leak-free interfaces can be fabricated, small-scale devices can be fabricated with TPL and then integrated with a larger chip, and to enable the printing of dense 3D structures using P-TPL. Microfluidic chips with sub-10 µm channels were printed using the TPL process, which was performed with a commercial Nanoscribe 3D printer. Printing was performed with a proprietary photoresist material that contains multifunctional acrylate oligomers. The printed material is optically transparent and enables imaging of the microchannels that are contained within these chips. Research to develop safe and effective poultry processing strategies (scalding-picking-evisceration procedures) to reduce foodborne contaminants (pathogens/chemical) and enhance the sustainability of poultry processing (Objective 4) was conducted. As part of a formal ARS agreement, university collaborators conducted trials on: 1) re-use of poultry processing wastewater for hydroponic plant production, 2) alternative uses of solid residues from poultry processing plants, 3) effects of broiler lairage and controlled atmosphere stunning (CAS) on poultry meat, 4) effects of CAS on metabolic function of broilers, 5) carcass quality at various points during processing, 6) effects of CAS on sensory attributes of meat, and 7) effects of broiler transport container sanitation and placement on Salmonella transmission. As part of this collaborative project, ARS researchers completed trials to determine the effects of broiler stunning method on meat biochemistry and functionality. As part of a separate ARS agreement, university collaborators conducted trials on alternative broiler slaughter and carcass chilling methods. A collaborative experiment was completed at a commercial broiler processing plant to investigate the effects of on-farm slaughter on product safety, quality, and processing efficiency. As part of this research, ARS researchers determined changes in meat biochemistry, functionality, and quality. In a separate pilot plant trial, ARS researchers investigated the effects of carcass position during delayed processing on broiler carcass and meat quality. More detailed descriptions of university collaborators’ research during FY2024 are included in separate annual performance reports for the agreements. Research progress on development of chemically modified non-selective agars for enhancement of contrast (Sub-objective 2A) and methodologies to eliminate semicarbazide production under poultry processing conditions as a means to eliminate production of semicarbazide in non-nitrofurazone treated poultry (Objective 3) were slowed due to a critical scientist vacancy. The recruitment process for filling this position was unsuccessful in FY2024 and is being restarted.

Accomplishments
1. Advances in generalizability of deep learning models for hypercubes from foodborne bacteria. Foodborne pathogens persist as a serious public safety concern in the United States impacting millions of people annually. While hyperspectral microscope imaging (HMI) combined with deep learning (DL) methods presents a potent strategy for the swift and accurate detection of foodborne bacteria, the widespread application of HMI-DL for food safety is somewhat constrained by generalization issues of the DL models for bacterial detection. ARS researchers at Athens, Georgia, developed an advanced artificial intelligence (AI) algorithm to identify and address a persistent problem with the generalizability of current AI models. This study delineated a method to account for the between-image variation that cause problematic spectral discrepancies across different datasets. This new AI approach improves data generalizability by eliminating the need for intricate per-image calibrations, a notable hurdle in the application of darkfield HMI technologies. Results showed that implementation of this correction strategy not only maintained bacterial detectability but also boosted the accuracy of the current Fusion-Net AI model from 38-70% to 95-99%. The enhanced AI model developed by ARS is a critical step towards the seamless integration of powerful HMI techniques into practical food safety investigations, marking a considerable advancement in foodborne pathogen detection.

Review Publications
Lu, Y., Jia, B., Yoon, S.C., Ni, X., Zhuang, H., Guo, B., Gold, S.E., Fountain, J.C., Glenn, A.E., Lawrence, K.C., Zhang, F., Wang, W., Lu, J., Wei, C., Jiang, H., Luo, J. 2024. Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology. International Journal of Food Microbiology. 416. https://doi.org/10.1016/j.ijfoodmicro.2024.110661.
Hou, J., Park, B., Li, C., Wang, X. 2023. A multiscale computation study on bruise susceptibility of blueberries from mechanical impact. Postharvest Biology and Technology. https://doi.org/10.1016/j.postharvbio.2023.112660.
Huan, C., Shin, T., Park, B., Ro, K.S., Jeong, C., Jeon, H., Tan, P. 2024. Coupling hyperspectral imaging with machine learning algorithms for detecting microplastics in soils. Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2024.134346.
Li, D., Park, B., Chen, Q., Ouyang, Q., Kang, R. 2024. Quantitative prediction and visualization of matcha color physicochemical indicators using hyperspectral microscope imaging technology. Food Control. https://doi.org/10.1016/j.foodcont.2024.110531.
Kang, R., Sun, S., Ouyang, Q., Huang, J., Park, B. 2024. 3D-GhostNet: A novel spatial-spectral algorithm to improve foodborne bacteria classification coupled with hyperspectral microscopic imaging technology. Sensors and Actuators B: Chemical. https://doi.org/10.1016/j.snb.2024.135706.
Campos, R., Yoon, S.C., Chung, S., Bhandarkar, S.M. 2023. Semisupervised deep learning for the detection of foreign materials on poultry meat with near-infrared hyperspectral imaging. Sensors. 23(16):7014. https://doi.org/10.3390/s23167014.
Reina, M.A., Mcconnell, A., Figueroa, J.C., Riggs, M.R., Buhr, R.J., Price, S.B., Macklin, K.S., Bourassa, D.B. 2023. Quantification of Salmonella Infantis transfer from transport drawer flooring to broiler chickens during holding. Poultry Science. 103(2). Article 10377. https://doi.org/10.1016/j.psj.2023.103277.
Reina, M.A., Urrutia, A., Figueroa, J.C., Riggs, M.R., Macklin, K.S., Buhr, R.J., Price, S.B., Bourassa, D.B. 2023. Application of pressurized steam and forced hot air for cleaning broiler transport container flooring. Poultry Science. 103(2). Article 103276. https://doi.org/10.1016/j.psj.2023.103276.