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

Ultraviolet (UV) spectroscopy is widely applied in real-time environmental monitoring, especially in diesel vehicle nitrogen monoxide (NO) emissions. However, in field experiments, UV absorption spectrum may exist for different degrees of drifts. Spectral jitters may exist for various reasons such as optical power variation, electrical signal drift, and the refractive index jitters of the optical path for an extended period of time, which causes the detection system to be calibrated. And the pulse xenon lamps as the UV source are characterized by specific emission lines that interfere in spectral analysis directly. For these problems, we proposed the spectral structure matching method based on principal component analysis (PCA), which was compared with the conventional polynomial fitting method to observe feasibility and variability. Further, the UV derivative spectrum was applied to the system appropriately, due to the variation of the absorption peak, and was only related to the target gas by using the above method. We validated our method experimentally by performing the NO UV detection system with the calibration and the comparison test. The results suggested that the calibration relative error was less than 9% and the measurement relative error was less than 6% for this wide range by the proposed processes, which optimized the interference of spectral structures and fluctuation to the system and therefore provided better monitoring. This study may provide an alternative spectral analysis method that is unaffected on the specific emission lines of lamps and is not limited to the spectral region and the target gas.

Adaptive monostable stochastic resonance for processing UV absorption spectrum of nitric oxide.

When ultraviolet (UV) absorption spectroscopy technology is used for nitric oxide (NO) detection, the background noise will directly affect the accuracy of concentration inversion, especially in low concentrations. Traditional processing methods attempt to eliminate background noise, which damages the absorption spectrum characteristics. However, stochastic resonance (SR) can utilize the noise to extract a weak characteristic signal. This paper reports a monostable stochastic resonance (MSR) model for processing an UV NO absorption spectrum. By analyzing the characteristics of UV absorption spectrum of NO, the evaluation indexes were constructed, thereby an adaptive MSR method was designed for parameter optimization. The numerical simulation confirmed the absorbance peak can be amplified and spectral signal-to-noise ratio (SNR) can be in the stable range of the proposed method, when noise intensity increased. Finally, this experiment obtained a NO detection limit (3σ) of 1.456 ppm and the maximum relative deviation of concentration is 6.32% by this proposed method, which is satisfactory for processing of the UV NO absorption spectrum.