Baseline drift spectra are used for quantitative and qualitative analysis, which can easily lead to inaccurate or even wrong results. Although there are several baseline correction methods based on penalized least squares, they all have one or more parameters that must be optimized by users. For this purpose, an automatic baseline correction method based on penalized least squares is proposed in this paper. The algorithm first linearly expands the ends of the spectrum signal, and a Gaussian peak is added to the expanded range. Then, the whole spectrum is corrected by the adaptive smoothness parameter penalized least squares (asPLS) method, that is, by turning the smoothing parameter λ of asPLS to obtain a different root-mean-square error (RMSE) in the extended range, the optimal λ is selected with minimal RMSE. Finally, the baseline of the original signal is well estimated by asPLS with the optimal λ. The paper concludes with the experimental results on the simulated spectra and measured infrared spectra, demonstrating that the proposed method can automatically deal with different types of baseline drift.

中文翻译：

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基于罚最小二乘法的自动基线校正方法。

基线漂移光谱用于定量和定性分析，很容易导致不准确甚至错误的结果。尽管有几种基于最小二乘法的基线校正方法，但它们都有一个或多个必须由用户优化的参数。为此，本文提出了一种基于惩罚最小二乘的自动基线校正方法。该算法首先线性扩展频谱信号的末端，然后将高斯峰添加到扩展的范围。然后，通过自适应平滑度参数惩罚最小二乘（asPLS）方法校正整个频谱，即通过转动asPLS的平滑参数λ在扩展范围内获得不同的均方根误差（RMSE），选择具有最小RMSE的最佳λ。最后，asPLS用最佳λ很好地估计了原始信号的基线。本文在模拟光谱和实测红外光谱上给出了实验结果，表明该方法可以自动处理不同类型的基线漂移。