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 degrees C. Every 12 h, hyperspectral scattering profiles over the spectral region between 400 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 log(10)(TVC) value. The best predictions were obtained with r(2) = 0.95 and SEP = 0.30 for log(10)(TVC). The research demonstrated that hyperspectral imaging technique showed potential for real-time and non-destructive detection of bacterial spoilage in beef. (C) 2010 Elsevier Ltd. All rights reserved.