摘要
光谱数据在采集过程中易发生基线偏移现象,导致后续的鉴别和分析结果偏离真实值。因此,在光谱数据分析前,需利用基线校正技术获取更为准确的光谱数据。基于稀疏贝叶斯学习(SBL)的基线校正方法无需人工选择参数,基线校正结果在贝叶斯框架下具有最优性。然而,现有的稀疏贝叶斯建模较为简单,无法适用于复杂的稀疏结构。在实际应用中,当纯谱的某些谱峰较宽时,对应的稀疏向量将具有一定的块稀疏特性。利用额外的块稀疏结构,有助于进一步提升SBL方法的性能。为了建模稀疏向量的块稀疏结构特性,在原有的贝叶斯模型框架中引入模式耦合分层模型。得益于稀疏贝叶斯框架固有的学习能力,引入的模式耦合分层模型可自适应地学习稀疏向量的块稀疏结构,从而大幅提升了基于SBL的基线校正方法的性能。为验证本文方法的基线校正性能,首先利用模拟数据集进行仿真实验,并将该方法与SSFBCSP方法和SBL-BC方法在不同噪声方差条件下进行对比。仿真实验结果表明,该方法恢复谱峰较宽纯谱的效果提升明显,特别是当噪声方差较大时,其他方法的性能均有不同程度的下降,但该方法依然具有较好的稳定性。蒙特卡罗仿真实验结果也显示该方法纯谱拟合的标准化均方根误差明显优于其他对比方法。最后,利用色谱数据集与三种矿物的拉曼光谱数据集进行实测数据的基线校正性能验证,结果表明该方法能产生比其他方法更为平滑的纯谱拟合结果,且去噪效果更优。
Baseline deviation often occurs with the spectrum data acquisition,making the subsequent identification and analysis results deviate from the true values.Therefore,it is necessary to utilize the baseline correction technology to obtain more accurate spectrum data before the spectrum data analysis.The sparse Bayesian learning(SBL)-based baseline correction method can provide the optimal baseline correction results within the Bayesian framework,and it does not need to select parameters manually.However,the SBL framework is too simple to apply to complex sparsity structures.In practical implementations,if the peak of the pure spectrum is wide,the corresponding sparse representation vector would exhibit a block-sparsity property.The performance of the SBL method will be further improved if the additional block-sparse structure can be exploited appropriately.To this end,we introduce a coupling pattern model into the SBL framework to adaptively learn the block-sparse structure.Due to the inherent learning capability of the SBL framework,the proposed method can significantly improve the baseline correction performance.We conducted several simulations to evaluate the performance improvement,where the proposed method is compared to SSFBCSP and SBL-BC with different noise variances.The simulation results verify the superiority of the proposed method for wide peak recovery.Specifically,it has good stability when the noise level is high,but the performance of other methods degrades substantially.Monte Carlo simulation results further demonstrate that our method can significantly improve the pure spectrum fitting’s normalized mean square error(NMSE)performance.Finally,one real chromatogram dataset and three Raman datasets are used to validate the performance of the proposed method.The experimental results indicate that our method can produce smoother pure spectrum fitting and better denoising effects than others.
作者
陈苏怡
李浩然
戴继生
CHEN Su-yi;LI Hao-ran;DAI Ji-sheng(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2022年第12期3730-3735,共6页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(62071206)资助。
关键词
光谱分析
块稀疏
稀疏贝叶斯学习
基线校正
Spectral analysis
Block sparse
Sparse Bayesian learning(SBL)
Baseline correction
