Abstract Baseline wander is a common problem in analysis with Fourier Transform Infrared Spectrometer (FTIR). And it is necessary to correct baseline drift for further quantitative and qualitative analysis. Several baseline correction algorithms based on penalized least squares have been proposed. However, these methods are usually used in noise-free or low-noise environments. In this paper, a novel algorithm named adaptive smoothness parameter penalized least squares was proposed. The smoothness parameters were set by user at first. Then, the smoothness parameter was updated iteratively according to the difference between the original spectrum and fitted baseline. When the iteration reaches the termination condition, the fitted baseline can be obtained. In the end of the paper, experimental results on simulated spectra and measured infrared spectra of methane were given. The simulated spectra results demonstrate that the proposed method has better performance than existing methods, especially when the spectra contain high noise. The results of infrared spectra confirm that the proposed method has good performance and can be applied to correct spectral baseline accurately.

中文翻译：

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使用自适应平滑参数惩罚最小二乘法对红外光谱进行基线校正

摘要 基线漂移是傅里叶变换红外光谱仪（FTIR）分析中的常见问题。并且有必要校正基线漂移以进行进一步的定量和定性分析。已经提出了几种基于惩罚最小二乘法的基线校正算法。但是，这些方法通常用于无噪声或低噪声环境。本文提出了一种新的自适应平滑参数惩罚最小二乘算法。平滑度参数首先由用户设置。然后，根据原始光谱和拟合基线之间的差异迭代更新平滑度参数。当迭代达到终止条件时，就可以得到拟合的基线。在论文的最后，给出了甲烷的模拟光谱和实测红外光谱的实验结果。模拟光谱结果表明，所提出的方法比现有方法具有更好的性能，特别是当光谱包含高噪声时。红外光谱结果表明，该方法具有良好的性能，可用于准确校正光谱基线。