  In this paper, we implement multi-label neural networks with optimal
thresholding to identify gas species among a multi gas mixture in a cluttered
environment. Using infrared absorption spectroscopy and tested on synthesized
spectral datasets, our approach outperforms conventional binary relevance -
partial least squares discriminant analysis when signal-to-noise ratio and
training sample size are sufficient.
