[Design and analysis of spectral recognition system based on neural network]

Area of Science:

• Analytical Chemistry
• Artificial Intelligence
• Spectroscopy

Context:

• Spectral recognition is crucial for qualitative analysis across diverse fields like medicine, environmental monitoring, and the petrochemical industry.
• The advancement of pattern recognition technologies has significantly enhanced the capabilities of spectral recognition for rapid detection.
• Artificial neural networks (ANNs) offer robust, adaptive, and fault-tolerant solutions for complex signal processing and pattern recognition tasks.

Purpose:

• To introduce the fundamental principles of artificial neural network pattern recognition theory.
• To propose a novel spectral recognition method integrating multiple features and ANNs tailored for spectral analysis needs.
• To outline the system design and basic model framework for the proposed method.

Summary:

• The paper focuses on spectral signals adhering to the Lambert-Beer law.
• It details a spectral recognition method based on ANNs and multiple spectral features.
• A system design and model framework are presented, with an illustrative example provided for clarity.

Impact:

• This research provides a foundation for developing more accurate and efficient spectral recognition systems.
• The proposed method has the potential to improve diagnostic capabilities in medicine and monitoring in environmental science.
• It offers a valuable tool for the petrochemical industry, enabling faster and more reliable qualitative analysis.