光谱学与光谱分析, 2018, 38 (12): 3946, 网络出版: 2018-12-16   

基于基线漂移模型的气体光谱自动基线校正

Automatic Baseline Correction of Gas Spectra Based on Baseline Drift Model
作者单位
1 北京工业大学机械工程与应用电子技术学院, 北京 100124
2 清华大学精密仪器系, 精密测试技术及仪器国家重点实验室, 北京 100084
3 北京工业大学环境与能源工程学院, 北京 100124
摘要
傅里叶红外光谱是监测污染源废气排放的一种重要手段。 发展针对气体光谱的自动基线校正方法对于污染气体快速检测及长时间在线监测具有重要意义。 目前自动基线校正中的一个难点是如何准确校正存在宽峰的光谱: 宽峰在频域中具有一定低频成分, 基于频域滤波提取光谱中低频基线信息的方法因难以选择合适的分离条件容易产生基线扭曲。 采取自动识别基线点, 基于预先设定的基线模型拟合光谱基线的方法可以规避频域方法中分离条件选取的环节, 但其校正效果对所采用的基线模型非常敏感。 当基线模型中的自由度过小时, 拟合基线无法准确逼近光谱基线漂移, 基线校正的误差较大; 而当基线模型中的自由度过大时, 尤其是含有实际基线漂移中不存在的虚假自由度时, 容易产生基线扭曲。 目前常用的基线模型有线性、 多项式、 样条插值、 指数模型等, 在基线模型的选择上缺乏较为统一的标准。 本研究着眼于避免基线模型缺乏必要自由度或含有虚假自由度, 提出基于实际基线漂移的自由度建立基线模型。 研究发现, 气体光谱中主要的基线漂移在光谱中可被近似表示为波数的特定阶次(0次、 1次、 2次和4次项)的形式。 以此作为基线模型提出了一种自动基线校正新方法。 新方法以传统迭代多项式拟合自动基线校正方法作为基础, 将其中仅设定多项式最高阶次的基线模型改进为上述由具有物理意义支撑的特定阶次构成的基线模型; 此外, 增加了对吸收峰尾部的判定, 用于避免在采用阈值分辨吸收峰与基线时, 吸收峰尾部因吸光度较低被误识别为基线的问题。 以实测获得的含有水汽宽峰的空气光谱作为样本, 对所提方法的基线校正效果进行了验证, 并与迭代多项式拟合方法中两种较有代表性的Lieber和Mahadeven-Jansen(LMJ)方法以及Liu和Koenig(LK)方法的基线校正效果进行了对比。 实验结果表明, 所提方法与采用不同最高多项式阶次的LMJ及LK方法相比, 可更好的避免基线扭曲, 同时其校正后的光谱基线与吸光度0线间具有最低的方差平均值。 研究表明, 采用实际基线漂移的自由度建立光谱基线模型可获得良好的基线校正效果。
Abstract
Fourier transform infrared spectroscopy is an important method for monitoring air pollution emissions from pollution sources. Automatic baseline correction method for gas spectra is of great significance to air pollution monitoring applications, such as rapid detection and long-term on-line monitoring. One difficulty in the current automatic baseline correction is accurately correcting the spectra, which include broad peaks. The broad peakscontain low-frequency content in the frequency domain; thus, the method for extracting baseline information based on low-frequency filtering is prone to baseline distortion because of the difficulty in selecting the appropriate separation parameter. Automatically identifying the baseline point and fitting the baseline of the spectrum based on a preset baseline function can prevent the selection of separation conditions; however, the result of baseline correction is highly sensitive to the baseline function adopted. If the degree of freedom in the baseline function is excessively small, the baseline function cannot fit the baseline drift in the spectra accurately, and the error will be considerable after baseline correction. Meanwhile, if the degree of freedom in the baseline function is excessively large, in particular, when a false degree of freedom does not exist in the natural baseline drift, the fitted baseline may have baseline distortion. Many types of baseline functions exist, including linear, polynomial, spline interpolation, and exponential functions. At present, consensus is lacking regarding the selection criteria for baseline functions. In this study, we proposed a baseline function for gas spectra for extractive atmospheric monitoring based on the degree of freedom of the natural baseline drift; we aimed to avoid false degrees of freedom or lack of necessary degrees of freedom in the baseline function. We found that the degrees of freedom of major baseline drift in the gas spectrum can be approximated in specific order terms of wavenumbers (0, 1st-, 2nd-, and 4th-order terms). An automatic baseline correction method based on a polynomial baseline function with above (0, 1st-, 2nd-, and 4th-) order terms was proposed in this study. In the experiment, a measured air spectrum, which contained broad peaks of water vapor, was used as a sample to test the performance of the baseline correction method. The baseline correction result of the proposed automatic baseline correction method was compared with the that of two types of iterative polynomial fitting methods that were proposed by Lieber and Mahadeven-Jansen (LMJ) and by Liu and Koenig (LK). The experiment results indicated that compared with the LMJ and LK methods, the proposed method avoided the baseline distortion in the best possible manner, and the proposed method also showed the lowest average variance between the corrected baseline and the absorbance zero line. Our research showed that in automatic baseline correction, an effective baseline correction result can be obtained by establishing the baseline function with the freedom of the natural baseline drift.

王昕, 吕世龙, 李岩, 尉昊赟, 陈夏. 基于基线漂移模型的气体光谱自动基线校正[J]. 光谱学与光谱分析, 2018, 38(12): 3946. WANG Xin, L Shi-long, LI Yan, WEI Hao-yun, CHEN Xia. Automatic Baseline Correction of Gas Spectra Based on Baseline Drift Model[J]. Spectroscopy and Spectral Analysis, 2018, 38(12): 3946.

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