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Proceedings Paper

Hyperspectral scattering profiles for prediction of the microbial spoilage of beef
Author(s): Yankun Peng; Jing Zhang; Jianhu Wu; Hui Hang
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Paper Abstract

Spoilage in beef is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. There is still no technology for the rapid, accurate and non-destructive detection of bacterially spoiled or contaminated beef. In this study, hyperspectral imaging technique was exploited to measure biochemical changes within the fresh beef. Fresh beef rump steaks were purchased from a commercial plant, and left to spoil in refrigerator at 8°C. Every 12 hours, hyperspectral scattering profiles over the spectral region between 400 nm and 1100 nm were collected directly from the sample surface in reflection pattern in order to develop an optimal model for prediction of the beef spoilage, in parallel the total viable count (TVC) per gram of beef were obtained by classical microbiological plating methods. The spectral scattering profiles at individual wavelengths were fitted accurately by a two-parameter Lorentzian distribution function. TVC prediction models were developed, using multi-linear regression, on relating individual Lorentzian parameters and their combinations at different wavelengths to log10(TVC) value. The best predictions were obtained with r2= 0.96 and SEP = 0.23 for log10(TVC). The research demonstrated that hyperspectral imaging technique is a valid tool for real-time and non-destructive detection of bacterial spoilage in beef.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150Q (27 April 2009); doi: 10.1117/12.819424
Show Author Affiliations
Yankun Peng, China Agricultural Univ. (China)
Jing Zhang, China Agricultural Univ. (China)
Jianhu Wu, China Agricultural Univ. (China)
Hui Hang, China Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 7315:
Sensing for Agriculture and Food Quality and Safety
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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