|Chao, Kuanglin - Kevin Chao|
Submitted to: Poultry Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/4/2007
Publication Date: 11/7/2007
Citation: Chao, K., Yang, C., Chen, Y.R., Kim, M.S., Chan, D.E. 2007. Hyperspectral/multispectral line-scan imaging system for automated poultry carcass inspection applications for food safety. Poultry Science. 86(11):2450-2460.
Interpretive Summary: American chicken plants now process over eight billion birds annually. Federal regulations prohibit the sale of any birds showing signs of septicemia or toxemia, which typically occur for 0.1% to 0.5% of the birds processed. Individual USDA inspectors conduct bird-by-bird inspections at maximum speeds of 35 birds per minute (bpm) to remove all septicemic/toxemic chickens. The inspection process is subject to human variability, and the inspection speed restricts the maximum output possible for the processing plants while making inspectors prone to fatigue and repetitive injury problems. The objective of this study was to develop a multispectral image classification method using an algorithm based on fuzzy logic, implementing key wavelengths determined from hyperspectral image data collected online from a commercial processing line during in-plant testing of the hyperspectral/multispectral line-scan imaging system, for identifying systemically diseased chickens exhibiting septicemia/toxemia. In-plant testing demonstrated that the hyperspectral/multispectral line-scan imaging system can be used for automated online inspection of chicken carcasses for the detection of systemically diseased birds on high-speed processing lines, and implementation of the imaging system on processing lines can be adapted to suit specific screening/processing goals by adjusting the classification threshold used for separating wholesome and systemically diseased birds. Applications for the system include automated food safety inspection of birds for pre-screening that increases processing efficiency by eliminating systemically diseased birds from evisceration lines which consequently can be operated more quickly, and for kill line paw-harvesting operations that can minimize the loss of wholesome paws with lower equipment requirements compared to current paw-harvesting operations. This information is useful to the Food Safety and Inspection Service (FSIS), and poultry processing plants.
Technical Abstract: A hyperspectral/multispectral line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. In-plant testing was conducted for chickens on a commercial evisceration line moving at a speed of 70 birds per minute. Hyperspectral image data was acquired for a calibration data set of 543 wholesome and 64 systemically diseased birds, and for a testing data set of 381 wholesome and 100 systemically diseased birds. The calibration data set was used to develop the imaging system’s parameters for conducting multispectral inspection based on fuzzy logic detection algorithms using selected key wavelengths. Using a threshold of 0.4 for fuzzy output decision values, multispectral classification was able to achieve 90.6% accuracy for wholesome birds and 93.8% accuracy for systemically diseased birds in the calibration data set, and 97.6% accuracy for wholesome birds and 96.0% accuracy for systemically diseased birds in the testing data set. By adjusting the classification threshold, 100% accuracy was achieved for systemically diseased birds with a decrease in accuracy for wholesome birds to 88.7%. This adjustment shows that the system can be feasibly adapted as needed for implementation for specific purposes, such as paw harvesting operations or pre-screening for food safety inspection. This line-scan imaging system is ideal for directly implementing multispectral classification methods developed from hyperspectral image analysis.