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A line-scan spectral imaging system was developed for online identification of wholesome and unwholesome freshly slaughtered chicken carcasses on commercial processing lines. Hyperspectral images acquired by the imaging system for 5,549 wholesome and 93 unwholesome chicken carcasses on a 140 bird per minute (bpm) processing line were analyzed to optimize region of interest size and location and determine key wavebands by which to implement online multispectral inspection based on single-waveband intensity and a two-waveband ratio. Multispectral inspection algorithms were developed for real-time online chicken inspection. The imaging system inspected over 100,000 chickens on the processing line during continuous operation and accurately identified over 99% of wholesome and over 96% of unwholesome chickens. A system of this type can perform food safety inspection tasks accurately and consistently on high-speed processing lines (e.g., at least 140 bpm), to help poultry processors improve production efficiency and satisfy increasing consumer demand for poultry products. PRACTICAL APPLICATIONS

The line-scan spectral imaging system was designed as a tool to assist poultry processors in meeting the requirements of the HACCP-Based Inspection Models Project as implemented by Food Safety and Inspection Service (FSIS). The system is particularly well suited for presorting poultry carcasses on high-speed processing lines by removing systemically diseased birds prior to the inspection stations. This can increase efficiency and reduce cross-contamination risks by minimizing the presence and unnecessary processing of unwholesome birds on the processing line. Real-time data collection and prompt removal of unwholesome poultry carcasses can enhance the ability of FSIS to certify U.S. poultry products for both domestic and export markets. In addition, the spectral imaging methodology of this system has the potential for easy adaptation to other high-speed food processing tasks, particularly those involving automated inspection for quality indicators.
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Document Type: Research Article

Publication date: 2011-02-01

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