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.

中文翻译：

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自适应单稳态随机共振处理一氧化氮的紫外吸收光谱

当将紫外线（UV）吸收光谱技术用于一氧化氮（NO）检测时，背景噪声将直接影响浓度反转的准确性，尤其是在低浓度下。传统的处理方法试图消除背景噪声，这会损害吸收光谱的特性。但是，随机共振（SR）可以利用噪声提取微弱的特征信号。本文报道了用于处理UV NO吸收光谱的单稳态随机共振（MSR）模型。通过分析NO的紫外吸收光谱特征，建立评价指标，设计了一种自适应MSR方法进行参数优化。数值模拟结果表明，当噪声强度增加时，吸光度峰值可以放大，光谱信噪比（SNR）处于建议方法的稳定范围内。最终，该实验获得的NO检出限（3σ）为1.456 ppm，浓度的最大相对偏差为6.32％，这对于处理UV NO吸收光谱是令人满意的。