[Spectral Smoothing with Adaptive Multiscale Window Average].

In order to smooth the spectra automatically and reliably, a spectral smoothing algorithm with adaptive multiscale window average (AWMA) is demonstrated. In this method, different positions of the spectra are smoothed by windows of different width, and the width of the windows will directly affect smoothing. The window with inappropriate width may cause excessive denoising (peak distortion or loss) or inadequate denoising (the flat region of the spectra still contains a lot of noise). So, how to get the right width of the window is the key of spectral smoothing. The algorithm optimized the width of windows by an iterative method, and verified whether the width is the best according to statistical Z-test. In order to increase the reliability of the algorithm, a comprehensive comparison of the thresholds of hypothesis according to simulation data of different SNR was performed. When the threshold is set to 1. 1, the denoising effect can be the best. In this work, the AMWA algorithm was tested by simulated spectra and real syectra, and it can automatically adapt to different spectral shape and different noise intensity. A comprehensive comparison of AMWA smoothing, Savitzky-Golay smoothing and moving average smoothing was performed in this paper, and the AMWA algorithm is better than the other two algorithms. Results show that the AMWA algorithm not only has better denoising effect, but also has higher accuracy and fidelity. This method has achieved great effect not only to simulated spectra but also to real spectra.