Vazquez-Osorio Noe, Castro-Ramos J, Sánchez-Escobar Juan Jaime
Coordinación de Óptica, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico.
Subdirección de Investigación y Extensión, Centro de Enseñanza Técnica Industrial, Jalisco, Mexico.
Appl Spectrosc. 2023 Sep;77(9):1009-1024. doi: 10.1177/00037028231179744. Epub 2023 Jul 14.
Due to its various advantages, Raman spectroscopy has become a powerful tool in different fields of science and engineering; however, in specific applications, this technique's limiting factor is closely related to the inherent noise of the Raman spectra. To eliminate the noise of a Raman spectrum, preserving its position, intensity, and width characteristic, we propose using a genetic matching pursuit-Hermite atoms (GMP-HAs) algorithm in this work. This algorithm helps recover Raman spectra immersed in Gaussian noise with the least number of atoms. The noise-free Raman signal is reconstructed with the GMP-HAs algorithm, transforming the typical best-matching atom search into an optimization problem. Specifically, we maximize the fitness function, defined as the correlation between current residual and Hermite atoms, with the genetic algorithm MI-LXPM encoded in a real domain and avoiding local maxima, by adding a stopping criterion based on an exponential adjustment according to the algorithm's behavior in the presence of noise. Simulated and biological Raman spectra are used to evaluate the proposed algorithm and compare its performance with typically known methods for denoising, such as the Savitzky- Golay filter (SG) and basis pursuit denoising. Using the signal-to-noise ratio (S/N)metric resulted in a 0.31 dB advantage in the S/N product for the proposed algorithm with respect to SG. Additionally, it is shown that the algorithm uses only 25.3% of the number of atoms needed by the matching pursuit algorithm. The results indicate that the GMP-HAs algorithm has better denoising capabilities, and at the same time, the Raman spectra are decomposed with fewer atoms compared to known sparse algorithms.
由于其诸多优点，拉曼光谱已成为科学与工程不同领域的强大工具；然而，在特定应用中，该技术的限制因素与拉曼光谱的固有噪声密切相关。为了消除拉曼光谱的噪声，同时保留其位置、强度和宽度特征，我们在这项工作中提出使用遗传匹配追踪 - 厄米特原子（GMP - HAs）算法。该算法有助于用最少数量的原子恢复淹没在高斯噪声中的拉曼光谱。利用GMP - HAs算法重建无噪声的拉曼信号，将典型的最佳匹配原子搜索转化为一个优化问题。具体而言，我们通过在实数域中编码的遗传算法MI - LXPM最大化适应度函数，该适应度函数定义为当前残差与厄米特原子之间的相关性，并通过根据算法在噪声存在时的行为添加基于指数调整的停止准则来避免局部最大值。使用模拟和生物拉曼光谱来评估所提出的算法，并将其性能与诸如Savitzky - Golay滤波器（SG）和基追踪去噪等典型的已知去噪方法进行比较。使用信噪比（S/N）指标，所提出的算法相对于SG在S/N乘积方面具有0.31 dB的优势。此外，结果表明该算法仅使用匹配追踪算法所需原子数量的25.3%。结果表明，GMP - HAs算法具有更好的去噪能力，同时与已知的稀疏算法相比，分解拉曼光谱所需的原子数量更少。
