  Deep learning methods have been widely applied to visual and acoustic
technology. In this paper, we proposed an odor labeling convolutional
encoder-decoder (OLCE) for odor identification in machine olfaction. OLCE
composes a convolutional neural network encoder and decoder where the encoder
output is constrained to odor labels. An electronic nose was used for the data
collection of gas responses followed by a normative experimental procedure.
Several evaluation indexes were calculated to evaluate the algorithm
effectiveness: accuracy 92.57%, precision 92.29%, recall rate 92.06%, F1-Score
91.96%, and Kappa coefficient 90.76%. We also compared the model with some
algorithms used in machine olfaction. The comparison result demonstrated that
OLCE had the best performance among these algorithms. In the paper, some
perspectives of machine olfactions have been also discussed.
