Emmert Johannes, Grauer Samuel J, Wagner Steven, Daun Kyle J
Opt Express. 2019 Sep 16;27(19):26893-26909. doi: 10.1364/OE.27.026893.
High-resolution absorption spectroscopy is a promising method for non-invasive process monitoring, but the computational effort required to evaluate the data can be prohibitive in high-speed, real-time applications. This study presents a fast method to estimate absorbance spectra from transmitted intensity signals. We employ Bayesian statistics to combine a measurement model with prior information about the shape of the baseline intensity and absorbance spectrum. The resulting linear least-squares problem shifts most of the computational effort to a preparation step, thereby facilitating quick processing and low latency for any number of measurements. The method is demonstrated on simulated tunable diode laser absorption spectroscopy data with additive noise and a fluctuating fringe. Results were highly accurate and the method was computationally efficient, having a processing time of only 2 ms per spectrum.
高分辨率吸收光谱法是一种很有前景的非侵入式过程监测方法，但在高速实时应用中，评估数据所需的计算量可能令人望而却步。本研究提出了一种从透射强度信号估计吸收光谱的快速方法。我们采用贝叶斯统计将测量模型与关于基线强度和吸收光谱形状的先验信息相结合。由此产生的线性最小二乘问题将大部分计算工作转移到一个准备步骤，从而便于对任意数量的测量进行快速处理和低延迟处理。该方法在具有加性噪声和波动条纹的模拟可调谐二极管激光吸收光谱数据上得到了验证。结果非常准确，该方法计算效率高，每个光谱的处理时间仅为2毫秒。
