Guo Yixin, Jin Weiqi, Wang Weilin, He Yuqing, Qiu Su
Appl Opt. 2023 Jun 20;62(18):4766-4776. doi: 10.1364/AO.489478.
Baseline correction is necessary for the qualitative and quantitative analysis of samples because of the existence of background fluorescence interference in Raman spectra. The asymmetric least squares (ALS) method is an adaptive and automated algorithm that avoids peak detection operations along with other user interactions. However, current ALS-based improved algorithms only consider the smoothness configuration of regions where the signals are greater than the fitted baseline, which results in smoothing distortion. In this paper, an asymmetrically reweighted penalized least squares method based on spectral estimation (SEALS) is proposed. SEALS considers not only the uniform distribution of additive noise along the baseline but also the energy distribution of the signal above and below the fitted baseline. The energy distribution is estimated using inverse Fourier and autoregressive models to create a spectral estimation kernel. This kernel effectively optimizes and balances the asymmetric weight assigned to each data point. By doing so, it resolves the issue of local oversmoothing that is typically encountered in the asymmetrically reweighted penalized least squares method. This oversmoothing problem can negatively impact the iteration depth and accuracy of baseline fitting. In comparative experiments on simulated spectra, SEALS demonstrated a better baseline fitting performance compared to several other advanced baseline correction methods, both under moderate and strong fluorescence backgrounds. It has also been proven to be highly resistant to noise interference. When applied to real Raman spectra, the algorithm correctly restored the weak peaks and removed the fluorescence peaks, demonstrating the effectiveness of this method. The computation time of the proposed method was approximately 0.05 s, which satisfies the real-time baseline correction requirements of practical spectroscopy acquisition.
由于拉曼光谱中存在背景荧光干扰，因此基线校正对于样品的定性和定量分析是必要的。非对称最小二乘法（ALS）是一种自适应自动算法，可避免峰值检测操作以及其他用户交互。然而，当前基于ALS的改进算法仅考虑信号大于拟合基线的区域的平滑配置，这会导致平滑失真。本文提出了一种基于光谱估计的非对称重新加权惩罚最小二乘法（SEALS）。SEALS不仅考虑了沿基线的加性噪声的均匀分布，还考虑了拟合基线上下信号的能量分布。使用逆傅里叶和自回归模型估计能量分布以创建光谱估计核。该核有效地优化并平衡了分配给每个数据点的非对称权重。通过这样做，它解决了非对称重新加权惩罚最小二乘法中通常遇到的局部过度平滑问题。这种过度平滑问题可能会对基线拟合的迭代深度和准确性产生负面影响。在模拟光谱的对比实验中，与其他几种先进的基线校正方法相比，SEALS在中等和强荧光背景下均表现出更好的基线拟合性能。它也被证明对噪声干扰具有高度抗性。当应用于实际拉曼光谱时，该算法正确地恢复了弱峰并去除了荧光峰，证明了该方法的有效性。所提出方法的计算时间约为0.05秒，满足实际光谱采集的实时基线校正要求。
