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

TL;DR

This paper introduces OLCE, a convolutional encoder-decoder model for odor identification in machine olfaction, achieving high accuracy and outperforming other algorithms.

Contribution

The paper presents a novel odor labeling convolutional encoder-decoder (OLCE) model specifically designed for machine olfaction, demonstrating superior performance.

Findings

OLCE achieved 92.57% accuracy in odor identification.

OLCE outperformed existing algorithms in comparative tests.

High precision and recall indicate reliable odor detection.

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.