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.
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.
This project replaces project 6040-42000-044-000D, "Develop Rapid Optical Detection Methods for Food Hazards," which ended March 2021. Substantial progress has been made on this project. Hyperspectral imaging technology for detection and identification of plastic foreign objects: In support of Objective 1, ARS researchers in Athens, Georgia, collected and analyzed spectra and hyperspectral images of 30 different types of foreign materials including plastic, rubber gloves, metal, fabric, and wood with wavelength range from 400 nm to 2,500 nm twice. A sensor fusion technique was developed to use spatial and spectral information of two hyperspectral imaging systems operating in two different spectral ranges of visible and near-infrared (400 nm-1,000 nm and 1,000 nm-2,500 nm). Spectral-spatial deep learning techniques for detection of Shiga toxin producing Escherichia coli (STEC) colonies on agar plates: In support of Objectives 1 and 2, ARS researchers in Athens, Georgia, developed a deep learning technique using spatial-spectral features in hyperspectral images to detect 15 different STEC serovars on solid agar media. Both modified MacConkey and modified Rainbow agar types were compared for the study. The overall classification accuracies were 91% and 94% for the MacConkey and Rainbow agar, respectively. Early detection of microcolonies of indicator microorganisms in poultry processing: In support of Objective 2, ARS researchers in Athens, Georgia, constructed an incubation system allowing time-lapse imaging of agar plates in situ to study the growth of three non-pathogenic bacteria (Pseudomonas putida, Listeria innocua, and Escherichia coli K12) on non-selective agar plates. The study results suggested that colonies were detectable with a high-resolution digital camera as early as 8 hours. Development of automatic bacteria cell segmentation methods: Single-cell bacteria segmentation has been a bottleneck for rapid bacterial detection with hyperspectral microscope imaging (HMI) because low-quality results from existing automatic segmentation methods prevented them from practical use and high-quality output from manual segmentation required a good amount of expert's time and effort (e.g., 30-60 minutes per hyperspectral data or hypercube). In support of Objective 2, ARS researchers in Athens, Georgia, developed an automatic segmentation method that consisted of two steps, bacterial segmentation with deep learning (DL) and single-cell selection with ellipse fitting evaluation. A DL-based segmentation method performed with 94.1% accuracy and less than 15 seconds for the result, which was better than previous manual segmentation methods with 88% and up to 60 minutes. Design and simulation of microfluidic channels for bacteria separation and enrichment: In support of Objective 2, ARS researchers in Athens, Georgia, developed simulation models to enrich Salmonella with six different sizes of particles (500 nm, 1 µm, 2 µm, 3 µm, 10 µm, and 20 µm) to represent various species in complex food matrix for three types of microfluidic channels. The channel width, height, aspect ratio of cross-section, and injection flow rate were optimized. The simulation results on Salmonella concentration and the microfluidic channel design parameters for optimum separation/enrichment efficiency were obtained. Evaluation of Poultry Processing Conditions for the Formation of Semicarbazide (SEM) on Chicken Products: In support of Objective 3, collaborative research was conducted to evaluate poultry processing conditions that may result in the formation of semicarbazide (SEM) on chicken products. A comprehensive survey of broiler processing establishments was continued to get a better understanding of the formation of SEM on chicken products to determine which factors may or may not contribute to this formation. Frozen leg quarters were received from 24 processing plants at several locations and shifts throughout the plant. Environmental factors such as antimicrobial treatment pH, temperature and time were recorded and submitted with each sample. Over the course of the project a total of 576 samples were received, separated, carefully logged, ground, and frozen in 2 g samples for analysis. Each sample was repeated in triplicate. A multi-step solvent extraction method was performed on each sample for future HPLC (High Performance Liquid Chromatography) analysis by collaborators.
1. Artificial Intelligence (AI) classification of pathogens from chicken rinse. Food contamination with pathogenic bacteria is a leading cause of foodborne illness and requires early and rapid detection of pathogens in food matrices. Current detection and classification methods have limitations with regards to their implementation for field-deployable detection due to the high volume of samples that are needed for regulatory purposes. ARS researchers in Athens, Georgia, developed a method to detect foodborne pathogens in chicken rinse using hyperspectral microscope imaging and deep learning (HMI-DL) techniques. The developed model called Fusion-Net is an artificial intelligence algorithm that can identify single-cell bacteria in hyperspectral images. The model was trained with four species (Salmonella, E. coli, Listeria, and Staphylococcus) spiked in chicken rinse. The AI-based Fusion-Net model was able to classify foodborne pathogens with 98.7% test accuracy. The result proved that HMI-DL technique has the potential for rapid, high-throughput detection of foodborne bacteria at the single cell level. A commercial partner has adopted the ARS model and included it in their HMI interface operating software for bacteria classification.