An Odor Labeling Convolutional Encoder-Decoder for Odor Sensing in Machine Olfaction

2020/11/25 by Tengteng Wen, Zhuofeng Mo, Wen, Tengteng +9
Agricultural and Biological Sciences · Engineering · Neuroscience · #Advanced Chemical Sensor Technologies #FOS: Computer and information sciences #Insect Pheromone Research and Control #Machine Learning (cs.LG) #Olfactory and Sensory Function Studies

paper · pdf · doi:10.48550/arxiv.2011.12538

openalex publication_date 2020/11/25 · openalex created_date 2020/12/07 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Critical review of electronic nose and tongue instruments prospects in pharmaceutical analysis
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

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