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Physiology of Smell and Olfactory Pathway

Physiology of Smell and Olfactory Pathway

Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...

Olfaction

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

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Updated: Oct 12, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.

Zhenyi Ye¹, Yuan Liu², Qiliang Li¹

¹Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

This summary is machine-generated.

Advanced machine learning methods enhance electronic nose (E-Nose) performance for precise odor identification. This review summarizes progress in feature extraction, modeling, and drift compensation for improved E-Nose accuracy and stability.

Keywords:

electronic nose gas sensor array machine learning neural networks review

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Area of Science:

Sensor Technology
Artificial Intelligence
Analytical Chemistry

Background:

Electronic noses (E-Noses) utilize machine learning for odor identification.
Advanced machine learning is vital for E-Nose applications in robotics, food, environment, and medicine.
Recent research focuses on integrating machine learning into E-Nose systems.

Purpose of the Study:

To review recent advancements in machine learning for E-Nose technology.
To provide insights into new research directions for E-Nose development.
To summarize progress in feature extraction, modeling, and sensor drift compensation.

Main Methods:

Machine learning techniques for feature extraction from raw sensor signals.
Development of advanced modeling methods for odor prediction.
Integration of gas sensor drift compensation techniques.

Main Results:

Machine learning significantly improves E-Nose qualitative and quantitative odor analysis.
Feature extraction enhances pattern recognition by removing noise and redundancy.
Drift compensation mitigates accuracy degradation, boosting E-Nose stability.

Conclusions:

Advanced machine learning is crucial for high-performing E-Nose systems.
Continued research in feature extraction, modeling, and drift compensation will further advance E-Nose capabilities.
E-Nose technology shows great promise across diverse application areas.