Raman spectroscopy is proposed here for the study of forsterite–hydroxyapatite (FS–HA) composite coating on a stainless-steel substrate. However, in order to analyze the Raman spectrum accurately, noise and background removal is always required. A comparative study has been done for the correction of background. The waveletbased denoising of the signal was done using level 6 decomposition with sym4 wavelet and the thresholding method used was soft thresholding. In the present work, the effectiveness of the wavelet-based denoising method has been compared with Savitsky–Golay smoothing, quadratic regression, and the low-pass filter method. It is found that the wavelet-based denoising method works better as compared to other methods as it is able to smooth the signal and to increase the SNR while maintaining the peak intensity undistorted. Peaks are calculated for the different composition of the FS–HA composite. The variation of peak location in the processed Raman spectra suggests that the variation in concentration of FS and HA in the coating can be studied by using Raman spectroscopy.

中文翻译：

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从拉曼信号中去除基于小波的噪声，以研究316l不锈钢基底上的PLD涂层镁橄榄石-羟基磷灰石薄膜

本文提出了拉曼光谱法，用于研究不锈钢基材上的镁橄榄石-羟基磷灰石（FS-HA）复合涂层。然而，为了准确地分析拉曼光谱，总是需要噪声和背景去除。已经进行了比较研究以校正背景。基于信号的基于小波的去噪使用sym4小波进行的6级分解完成，所使用的阈值处理方法为软阈值处理。在目前的工作中，已经将基于小波的去噪方法与Savitsky-Golay平滑，二次回归和低通滤波器方法的有效性进行了比较。发现基于小波的去噪方法与其他方法相比效果更好，因为它能够平滑信号并增加SNR，同时保持峰值强度不变。计算出FS-HA复合材料不同组成的峰。处理后的拉曼光谱中峰位置的变化表明，可以通过使用拉曼光谱研究涂层中FS和HA浓度的变化。