基于改进的自适应迭代重加权惩罚最小二乘的空间外差拉曼光谱基线校正方法

Spatial Heterodyne Raman Spectral Baseline Correction Based on ImprovedAdaptive Iterative Re-Weighted Penalized Least Square Method

空间外差拉曼光谱技术因其非接触、无损、快速、高稳定性和高光谱分辨率等特点,已经广泛应用于各个物质探测领域。由于复原光谱中存在荧光背景干扰,对样品进行定性和定量分析时需要对光谱进行基线校正。为了解决由拉曼峰引起的拟合基线抬升的问题,提出了一种改进的自适应迭代重加权惩罚最小二乘(airPLS)的基线拟合方法,即基于拉曼峰截断的airPLS基线拟合方法。该方法能够自动识别拉曼峰,并在对光谱进行拉曼峰截断后进行airPLS迭代拟合,以获得更准确的基线。使用仿真光谱和实测光谱进行验证,并与常见的基线拟合方法进行对比,结果显示,改进的airPLS基线拟合准确度显著提升,仿真光谱的基线拟合均方根误差优于0.0052。实测拉曼光谱的校正谱特征峰清晰可见,荧光基线趋势被有效去除,满足拉曼光谱数据处理的需求。

Objective Raman spectroscopy is a non-destructive analytical technique based on the inelastic scattering of light by matter.While inducing Raman signals,the fluorescence background directly affects the characterization of sample Raman properties.The common approaches to reducing fluorescence background are primarily implemented through hardware and software methods.Hardware methods mainly involve techniques such as shifted excitation Raman difference spectroscopy,time-resolved Raman spectroscopy,and deep ultraviolet Raman spectroscopy.Although these methods exhibit effective outcomes,they often entail complex instrument setups and high costs.Software methods,on the other hand,refer to utilizing signal processing techniques to subtract fluorescence background from Raman spectra.Raman spectra are characterized by typically discontinuous with sharp peaks,contrasted with the continuous and smooth trends often present in fluorescence spectra.Given the difference in their spectral characteristics,when Raman spectroscopy analysis is carried out,employing baseline correction algorithms to eliminate fluorescence interference helps ensure the reliability and accuracy of Raman spectroscopy data.Common methods for mitigating fluorescence background include polynomial fitting,discrete wavelet transform,morphological algorithms,variational mode decomposition,least squares methods,and neural networks.However,due to the presence of Raman peaks,these methods typically result in varying degrees of baseline rise in the fitting outcomes.In the present study,we report an adaptive iterative re-weighted penalized least square(airPLS)method based on Raman peaks truncation.By identifying the positions of Raman peaks,truncating the corresponding regions,and employing the airPLS algorithm for baseline fitting,the method reduces the rise in the fitted baseline caused by abrupt changes in intensity within the Raman peak regions,making the fitted baseline approach closer to the true baseline.We hope that this improved method will further enhance the accuracy of Raman spectroscopy baseline fitting.Methods Baseline fitting is conducted with airPLS based on Raman peaks truncation.Initially,the spectral signal is denoised by the Savitzky-Golay filter.Subsequently,we employ a peak-finding algorithm to identify Raman peaks within the denoised spectrum and use the first derivative of the spectrum to determine the left and right boundaries for each Raman peak.Following this,we truncate the Raman peaks within these defined boundaries to obtain the original baseline.An airPLS fitting is performed on this original baseline to derive a new baseline.At this stage,we compute the difference between the new baseline and the original baseline and truncate the regions where the absolute difference exceeds a threshold.We iterate this process by subjecting the truncated signal to successive airPLS fitting until the absolute difference between the baselines from two consecutive fittings is less than the threshold,concluding the iteration.The resulting fitted baseline is output.Here,the threshold represents the average of the absolute differences between the baselines from two consecutive fittings.Subtracting the fitted baseline from the original Raman spectrum yields the corrected Raman spectrum.Results and Discussions The airPLS based on Raman peaks truncation has demonstrated outstanding performance in baseline fitting.Simulated Raman spectra and measured Raman spectra from lipstick are utilized to validate the proposed baseline fitting method,respectively.Comparative analyses are conducted against commonly used baseline fitting methods,including polynomial fitting,discrete wavelet transform,variational mode decomposition,and airPLS.As depicted in Figs.6(a),7(a),and 7(b),although the algorithm proposed in this article achieves a baseline fit closer to the theoretical baseline in regions with weak Raman peaks or without Raman peaks,the improvement compared to the aforementioned algorithms is not notably conspicuous.This indicates that the fitting capabilities of these algorithms are similar in spectra exhibiting gradual trends.However,near the Raman peak regions,as shown in Figs.6(b),6(c),and 7(c),these methods experience baseline elevation due to abrupt changes in spectral peak intensity.In contrast,the proposed baseline fitting method,which incorporates Raman peaks truncation,minimizes the influence of Raman peaks on the fitting results,resulting in the closest fit to the theoretical values.Table 3 presents the root mean square errors(RMSE)between the fitted baseline and the theoretical baseline for our method and the aforementioned commonly used methods,evaluating the performance of these methods in spectra exhibiting both single and complex trends.Comparative analysis indicates that under various signal-to-noise ratios of spectra,our method yields the lowest RMSE,showcasing its superior performance.The baseline fitting results of the lipstick Raman spectrum shown in Fig.8 are consistent with the simulated analysis outcomes.In the region devoid of Raman peaks(800‒1000 cm-1),the baseline fitting capabilities of each algorithm are similar.However,within the region with numerous Raman peaks(1100 to 1650 cm-1),the baseline fits of all algorithms are affected to varying degrees by the spectral peaks,resulting in baseline rises.Our baseline fitting method,employing spectral peak truncation and an iterative approach,significantly mitigates the influence of spectral peaks on the fitting outcomes.This method maximizes the preservation of Raman peak intensities and stands out as the optimal choice among the various methods evaluated.Conclusions We introduce the Raman peak-truncated airPLS baseline fitting method.The utilization of Raman peak truncation mitigates the influence of abrupt changes in Raman peak intensity on baseline fitting.The method not only inherits the effective baseline fitting performance of airPLS in peak-free regions but also resolves the issue of baseline elevation caused by Raman peak intensity,thereby enhancing the accuracy of baseline fitting.Comparative experiments conducted on simulated spectra demonstrate the superior baseline fitting performance of Raman peak-truncated airPLS.Under different spectral signal-to-noise ratios,the RMSE for Type 1 spectra fitted by our method is below 0.0042,and for Type 2 spectra,it is below 0.0052,which is the lowest among various methods.In experiments fitting Raman spectra from lipstick samples,airPLS based on Raman peaks truncation outperforms several commonly used algorithms.It accurately restores Raman peak intensities without distorting the corrected spectra,effectively removing fluorescence baseline trends and meeting the requirements of Raman spectroscopy data processing.