A custom-built metal oxide-based olfactory sensing system was used to analyze the headspace from beef strip loins (M. Longissimus lumborum) stored at 4 degrees C and 10 degrees C. Area-based features were extracted from the raw signals using various signal processing techniques. Classification models using radial basis function neural networks were developed using the extracted features and performance tested using leave-1-out cross validation method. The developed models classified the beef samples into two groups; "unspoiled" (<6.0 log(10) cfu/g) and "spoiled" (>6.0 log(10) cfu/g) based on the microbial population. Maximum total classification accuracies above 90% were obtained for the samples stored at the two temperatures. Scaling the signals did have a positive influence in improving the classification accuracies obtained. Back propagation neural network prediction model using the pooled data (containing the area scaled feature) resulted in a R-squared of >0.70 between predicted and actual spoilage population from the 10 degrees C and 4 degrees C stored samples. (C) 2008 Elsevier Ltd. All rights reserved.