提出面积信噪比，提升光谱分析中宽峰检测的稳定性与灵敏度。
The Area Signal-to-Noise Ratio: A Robust Alternative to Peak-Based SNR in Spectroscopic Analysis
- 用信号积分区域替代峰值点计算信噪比，降低噪声干扰
- 在50%检出率下，aSNR所需信号幅度比pSNR低得多
- 特别适合宽峰和多维光谱，对低信号更敏感
在光谱分析中，传统基于峰值的信噪比（pSNR）易受噪声尖峰影响，且对宽峰效果不佳。本文提出基于面积的信噪比（aSNR），通过在指定区域内积分信号，降低噪声方差，提升各类线型（高斯、洛伦兹、沃伊格特）的检测能力。我们采用蒙特卡洛模拟（每种条件2,000次试验）验证，在50%检出概率下，aSNR所需信号幅度显著低于pSNR。受试者工作特征（ROC）曲线表明，aSNR在低信号条件下表现更优。该方法尤其适用于宽峰，未来可扩展至多维光谱的体积信噪比。
原文摘要 · Abstract (English)
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.
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