Performance evaluation of normalized difference based classifier for efficient discrimination of volatile organic compounds

Abstract

Discrimination of volatile organic compounds (VOCs) is essential for mitigating their hazardous effects. Pre-processing of sensor responses and pattern recognition improves the classification results significantly. In our recent work, we reported that Normalized Difference Sensor Response Transformation (NDSRT) outperforms other popular pre-processing techniques. In this paper, we have assessed the NDSRT approach on three well-published datasets consisting of single and binary mixtures of VOCs. The raw sensor responses were transformed into virtual multi-sensor responses having concentration invariance, thereby giving rise to compacted clusters with good inter-cluster separation. In this transformation domain, even a simpler four-neuron artificial neural network (ANN) classifier was sufficient to discriminate all the test samples accurately. After application of NDSRT, Dataset- I showed a minimum cluster separation of 0.80 units while individual clusters were compacted by at least 85.01%. For Dataset- II, the average inter-cluster separation was improved by 1.4% while classification accuracy was improved from 33% to 100%. Further, for Dataset- III, the inter-cluster separation for 'worst scenario' was improved by at least 19% while for 'best scenario' it improved by 34%. Further, a simpler four neuron feed-forward back-propagation neural network (BPNN) was then used to classify 16, 30, and 50, unknown test samples taken from Dataset- I, II and III, respectively, which were not used during the training of the BPNN. The mean squared error (MSE) for these test samples was 6.7 × 10-3, 1.7 × 10-1 and 1.0 × 10-3, respectively. All the test samples were classified accurately in the proposed transformation domain. © 2018 IOP Publishing Ltd.