Neural-Network-Based Classification of Meat: Evaluation of Techniques to Overcome Small Dataset Problems

摘要

One of the objectives of our multidisciplinary research group is to develop sensors for the detection of Salmonella contamination in beef. Similar to most biological studies, beef contamination classification studies using artificial neural networks (ANNs) are restricted to small datasets. This study evaluates selected techniques of data domain expansion and synthetic sample generation on small datasets associated with meat contamination. Mega-trend-diffusion (MTD) and functional virtual population (FVP) techniques for data domain expansion and synthetic sample generation were assessed on the small datasets. The datasets used were obtained from a thin-film (TF) module electronic nose system in response to the headspace of control and Salmonella-inoculated packaged meat samples. Back-propagation neural networks (BPNNs) were used to determine classification accuracies of the synthetically expanded datasets. For aged beef datasets, the maximum mean of average overall classification accuracies provided by FVP technique was 90%. The maximum mean of average overall classification accuracies obtained by FVP technique was about 81% for fresh beef datasets. MTD technique also provided similar accuracies (in the lower 80s). Both techniques were found useful for expanding the domain range of the small dataset in order to test and evaluate BPNN-based classification models.