State Key Laboratory of Electrical Insulation & Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2020 Apr 3;20(7):2015. doi: 10.3390/s20072015.
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.
基线漂移谱可用于定量和定性分析，但容易导致不准确甚至错误的结果。虽然有几种基于惩罚最小二乘法的基线校正方法，但它们都有一个或多个参数必须由用户进行优化。为此，本文提出了一种基于惩罚最小二乘法的自动基线校正方法。该算法首先对光谱信号的两端进行线性扩展，并在扩展范围内添加一个高斯峰。然后，通过自适应平滑参数惩罚最小二乘法(asPLS)对整个光谱进行校正，即通过改变 asPLS 的平滑参数 λ，在扩展范围内得到不同的均方根误差 (RMSE)，选择最小 RMSE 的最佳 λ。最后，用最佳 λ 的 asPLS 对原始信号的基线进行很好的估计。通过对模拟光谱和实测红外光谱的实验结果，证明了该方法可以自动处理不同类型的基线漂移。
