Hyperspectral scattering profiles for prediction of the microbial spoilage of beef

Yankun Peng; Jing Zhang; Jianhu Wu; Hui Hang

doi:10.1117/12.819424

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27 April 2009 Hyperspectral scattering profiles for prediction of the microbial spoilage of beef

Yankun Peng, Jing Zhang, Jianhu Wu, Hui Hang

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Proceedings Volume 7315, Sensing for Agriculture and Food Quality and Safety; 73150Q (2009) https://doi.org/10.1117/12.819424
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

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Yankun Peng, Jing Zhang, Jianhu Wu, and Hui Hang "Hyperspectral scattering profiles for prediction of the microbial spoilage of beef", Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150Q (27 April 2009); https://doi.org/10.1117/12.819424

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