In spectroscopic analysis, the peak-based signal-to-noise ratio (pSNR) is commonly used but suffers from limitations such as sensitivity to noise spikes and reduced effectiveness for broader peaks. We introduce the area-based signal-to-noise ratio (aSNR) as a robust alternative that integrates the signal over a defined region of interest, reducing noise variance and improving detection for various lineshapes. We used Monte Carlo simulations (n=2,000 trials per condition) to test aSNR on Gaussian, Lorentzian, and Voigt lineshapes. We found that aSNR requires significantly lower amplitudes than pSNR to achieve a 50% detection probability. Receiver operating characteristic (ROC) curves show that aSNR performs better than pSNR at low amplitudes. Our results show that aSNR works especially advantageously for broad peaks and could be extended to volume-based SNR for multidimensional spectra.
翻译:在光谱分析中,基于峰值的信噪比(pSNR)虽被广泛使用,但存在诸多局限,例如对噪声尖峰敏感以及对较宽峰值的检测效能降低。我们引入基于面积的信号噪声比(aSNR)作为一种稳健的替代方案,该方法通过对感兴趣区域内的信号进行积分来降低噪声方差,从而提升对不同线型的检测能力。我们采用蒙特卡洛模拟(每种条件 n=2,000 次试验)在 Gaussian、Lorentzian 和 Voigt 线型上测试了 aSNR。研究发现,要达到 50% 的检测概率,aSNR 所需的信号幅度显著低于 pSNR。受试者工作特征(ROC)曲线表明,在低幅度条件下,aSNR 的性能优于 pSNR。我们的结果表明,aSNR 对于宽峰尤其具有优势,并可进一步推广至多维光谱的体积信噪比计算。