拉曼光谱分析技术具有快速响应、非接触、检测限制小、灵敏度高的优点,广泛应用于生产生活的众多领域。然而实际测得的原始拉曼光谱总会有不同程度的基线漂移,严重影响光谱分析的有效性和准确性。针对现有基线校正方法容易造成估计基线偏低、校正后光谱抬升的问题,提出了一种基于局部对称重加权惩罚最小二乘(LSRPLS)的基线校正算法,该算法在非对称惩罚最小二乘的基础上,使用softsign函数引入局部对称加权的思想,对光谱中无谱峰的基线区域赋予相近的权重,并通过迭代调整估计基线的权重。在模拟和实际拉曼光谱上分别进行了验证。实验结果表明:LSRPLS基线校正算法不仅能对不同类型的光谱基线进行校正,而且与现有的基线校正方法相比,具有更高的准确度和稳定性。基线校正后的光谱在主成分空间上的聚集度得到提升,模型的分类准确性明显提高,说明LSRPLS算法在去除基线的同时,能够保留光谱的有效信息,为拉曼光谱的进一步分析提供了依据。

Raman spectroscopy has advantages of rapid response, non-contact, less detection restrictions and high selectivity, which make it widely used in many fields of production and life. However, the actual measured Raman spectra contain varying degrees of baseline drift, which seriously affects the validity and accuracy of spectral analysis. In order to solve the issues that the final baseline is underestimated in the no peak region and the height of peaks might be overestimated in existing baseline correction methods, we propose a novel correction algorithm, which is named locally symmetric reweighted penalized least squares (LSRPLS). Based on asymmetrical least squares, the method works by iteratively adjusting weights of the difference between the fitted baseline and the original signal, introducing the idea of local symmetric weighting by a softsign function. The algorithm is applied to the simulated and the actual Raman spectra to correct the baseline drifting. The results show that the LSRPLS algorithm can not only correct different types of baselines, but also has good advantages in accuracy and stability compared with the existing baseline correction methods. In addition, after baseline correction, the distribution of samples in principal component spaces becomes concentrated, and the classification accuracy of the model is significantly improved. This indicates that the LSRPLS algorithm can retain the spectral information effectively while removing the baseline, which provides a basis for further analysis of Raman spectroscopy.