Diffusion Enhanced Model And Its Application In Peak Detection

The spectral peak contains the biological or chemical information of the substance to be tested,such as the type of disease,the nature and concentration of the substance,etc.Therefore,the accuracy of the peak detection is important for the subsequent analysis of the signal.Generally,because of the noise,the spectral signal needs to be smoothed.Therefore,it is easy to hide the weak peak and the overlapping peak during the smoothing process,resulting in a decrease in the accuracy of peak detection.Among the existing peak detection models,the continuous wavelet transform(CWT)model is the best one.Through analysis,it can be found that the mexihat wavelet can be regarded as the second derivative of the Gaussian function.Therefore,using the mexihat wavelet to process the signal is equivalent to using the Gaussian function to smooth the second derivative of the signal.Compared with other models,the CWT model has a peak enhancement step before smoothing,which leads to better performance for peak detection.For this reason,this paper combines the signal enhancement step with the time-space fractional nonlinear diffusion model to obtain a new diffusion enhancement model to improve peak detection.The main results are as following:First,design,implementation and verification of diffusion enhancement models.The time-space fractional diffusion enhancement model(TSFDEM)is established by combining the signal enhancement step with the time-space fractional nonlinear diffusion model.The numerical method is given.The influence of the diffusion threshold on the proposed model is discussed and a method for automatically taking diffusion threshold is also suggested;The selection of the time and space fractional derivative order is briefly discussed.The peak detection results are compared between the proposed model and the CWT model.In addition,the enhancement performance is verified by adding enhancement step to the other four common models.The results show that the enhancement step in the new peak detection framework can significantly improve the accuracy of peak detection,and the proposed time-space fractional diffusion enhancement model has a better performance.Second,evaluation of peak detection performance.Some actual mass spectrometry are taken to verify the performance of the proposed model.The cubic spline interpolation is used for baseline correction,and then the proposed diffusion enhancement model is used toenhance and smooth the mass spectrometry,finally the peak is detected by the local maximum value and the amplitude threshold.The performance of the peak detection is verified by comparing the peak detection results.The results show that the diffusion enhancement model can improve the detection performance of the mass spectrometry peaks,especially in the case of low false detection rate.After that,the enhancement step is added to the other four common models and re-detected those mass spectrometry.The results show that adding enhancement step to the peak detection framework can also improve the performance of peak detection.