Nitric oxide detection using principal component analysis spectral structure matching to the UV derivative spectrum

Area of Science:

• Environmental Science
• Analytical Chemistry
• Spectroscopy

Background:

• Ultraviolet (UV) spectroscopy is crucial for real-time environmental monitoring, particularly for diesel vehicle nitrogen monoxide (NO) emissions.
• Field experiments face challenges like spectral drifts, jitters from optical power variations, electrical signal drift, and refractive index fluctuations, necessitating system recalibration.
• Pulse xenon lamps, common UV sources, exhibit specific emission lines that directly interfere with spectral analysis.

Purpose of the Study:

• To address spectral interference and drifts in UV spectroscopy for environmental monitoring.
• To develop and validate a novel spectral structure matching method based on principal component analysis (PCA).
• To compare the proposed PCA method with conventional polynomial fitting for feasibility and variability assessment.

Main Methods:

• Proposed a spectral structure matching method utilizing principal component analysis (PCA).
• Applied UV derivative spectroscopy to isolate target gas absorption peaks.
• Conducted experimental validation using a NO UV detection system with calibration and comparison tests.

Main Results:

• The PCA-based method demonstrated reduced interference from spectral structures and fluctuations.
• Achieved a calibration relative error of less than 9% and a measurement relative error of less than 6%.
• The method proved effective in optimizing NO detection in UV spectroscopy.

Conclusions:

• The proposed spectral structure matching method offers an improved approach for UV spectroscopic analysis.
• This method is unaffected by specific lamp emission lines and is not limited by spectral region or target gas.
• Provides a viable alternative for enhanced environmental monitoring systems.