Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

Area of Science:

• Materials Science
• Analytical Chemistry
• Sensor Technology

Background:

• Chemiresistive sensors are vital for gas detection, offering advantages like flexibility and fast response.
• Challenges include poor selectivity and stability in complex environments.
• Industrialization necessitates improved gas monitoring solutions.

Purpose of the Study:

• To systematically review advancements in data processing for chemiresistive gas sensors.
• To highlight the impact of pattern recognition on gas sensing.
• To analyze feature extraction methods and recognition algorithms.

Main Methods:

• Review of feature extraction techniques (response curve characteristics, curve fitting, transform domain).
• Analysis of traditional and neural network-based recognition algorithms for gas discrimination.
• Extensive literature review of existing research.

Main Results:

• Pattern recognition significantly improves selectivity and anti-interference stability.
• Various data processing methods offer effective feature extraction.
• Neural network algorithms show promise for complex gas discrimination.

Conclusions:

• Pattern recognition is crucial for overcoming limitations of chemiresistive gas sensors.
• Further research in data processing and algorithms will enhance gas detection capabilities.
• Future prospects lie in integrated sensor systems and advanced AI for real-world applications.