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Proceedings Paper

Detection of organic residues on food processing equipment surfaces by spectral imaging method
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Paper Abstract

Organic residues on equipment surfaces in poultry processing plants can generate cross contamination and increase the risk of unsafe food for consumers. This research was aimed to investigate the potential of LED-induced fluorescence imaging technique for rapid inspection of organic residues on poultry processing equipment surfaces. High-power blue LEDs with a spectral output at 410 nm were used as the excitation source for a line-scanning hyperspectral imaging system. Common chicken residue samples including fat, blood, and feces from ceca, colon, duodenum, and small intestine were prepared on stainless steel sheets. Fluorescence emission images were acquired from 120 samples (20 for each type of residue) in the wavelength range of 500-700 nm. LED-induced fluorescence characteristics of the tested samples were determined. PCA (principal component analysis) was performed to analyze fluorescence spectral data. Two SIMCA (soft independent modeling of class analogy) models were developed to differentiate organic residues and stainless steel samples. Classification accuracies using 2-class ('stainless steel' and 'organic residue') and 4-class ('stainless steel', 'fat', 'blood', and 'feces') SIMCA models were 100% and 97.5%, respectively. An optimal single-band and a band-pair that are promising for rapid residue detection were identified by correlation analysis. The single-band approach using the selected wavelength of 666 nm could generate false negative errors for chicken blood inspection. Two-band ratio images using 503 and 666 nm (F503/F666) have great potential for detecting various chicken residues on stainless steel surfaces. This wavelength pair can be adopted for developing a LED-based hand-held fluorescence imaging device for inspecting poultry processing equipment surfaces.

Paper Details

Date Published: 20 April 2010
PDF: 10 pages
Proc. SPIE 7676, Sensing for Agriculture and Food Quality and Safety II, 767608 (20 April 2010); doi: 10.1117/12.850102
Show Author Affiliations
Jianwei Qin, USDA Agricultural Research Service (United States)
Won Jun, USDA Agricultural Research Service (United States)
Moon S. Kim, USDA Agricultural Research Service (United States)
Kaunglin Chao, USDA Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 7676:
Sensing for Agriculture and Food Quality and Safety II
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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