Sun Biao, Zhai Jinglei, Wang Zilong, Wu Tengyu, Yang Siwei, Xie Yuhao, Li Yunfeng, Liang Pei
School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.
College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018 China; College of Information Engineering, China Jiliang University, Hangzhou 310018 China.
Talanta. 2024 Jan 1;266(Pt 2):125120. doi: 10.1016/j.talanta.2023.125120. Epub 2023 Aug 27.
Enhancing the quality of spectral denoising plays a vital role in Raman spectroscopy. Nevertheless, the intricate nature of the noise, coupled with the existence of impurity peaks, poses significant challenges to achieving high accuracy while accommodating various Raman spectral types. In this study, an innovative adaptive sparse decomposition denoising (ASDD) method is proposed for denoising Raman spectra. This approach features several innovations. Firstly, a dictionary comprising spectral feature peaks is established from the input spectra by applying a chemometric feature extraction method, which better aligns with the original data compared to traditional dictionaries. Secondly, a dynamic Raman spectral dictionary construction technique is introduced to swiftly adapt to new substances, employing a limited amount of additional Raman spectral data. Thirdly, the orthogonal matching pursuit algorithm is utilized to sparsely decompose the Raman spectra onto the constructed dictionaries, effectively eliminating various random and background noises in the Raman spectra. Empirical results confirm that ASDD enhances the accuracy and robustness of denoising Raman spectra. Significantly, ASDD surpasses existing algorithms in processing Raman spectra of pesticide.
提高光谱去噪质量在拉曼光谱中起着至关重要的作用。然而，噪声的复杂性质，加上杂质峰的存在，在适应各种拉曼光谱类型的同时实现高精度面临着重大挑战。在本研究中，提出了一种创新的自适应稀疏分解去噪（ASDD）方法用于拉曼光谱去噪。该方法具有多项创新。首先，通过应用化学计量学特征提取方法从输入光谱中建立包含光谱特征峰的字典，与传统字典相比，它与原始数据的匹配度更高。其次，引入了动态拉曼光谱字典构建技术，利用有限数量的额外拉曼光谱数据快速适应新物质。第三，利用正交匹配追踪算法将拉曼光谱稀疏分解到构建的字典上，有效消除拉曼光谱中的各种随机噪声和背景噪声。实证结果证实，ASDD提高了拉曼光谱去噪的准确性和鲁棒性。值得注意的是，ASDD在处理农药拉曼光谱方面优于现有算法。
