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Peak detection is an important step in mass spectrometry as accurately identifying characteristic peaks is key to data analysis. In order to address the issue of false peak detection, while simultaneously ensuring accurate detection of weak and overlapped peaks, this paper introduces an improved algorithm for mass spectrometry integrating weighted continuous wavelet transform with particle swarm optimization-based Otsu (WWTPO). The algorithm applies the weighted continuous wavelet transform (WCWT) to compress the frequency spectrum signal into a smaller scale range, which allows for the acquisition of more distinct and informative peak information. Moreover, the algorithm employs the particle swarm optimization (PSO) algorithm to iteratively evaluate the optimal image segmentation threshold, which addresses the challenge of inaccurate Otsu image segmentation. The method was applied to detect simulated peaks as well as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) datasets. The performance evaluation was conducted using receiver operating characteristic (ROC) curves, F1 measure and F-scores. Through comparison with continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (WSTGA), multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS), the results demonstrate that WWTPO exhibits excellent performance in peak detection. The determination of 4-isopropyltoluene also demonstrates that WWTPO has excellent practical application. This method not only maintains a low false peak identification rate but also detects more weak peaks and overlapping peaks, further improving the accuracy and efficiency of peak detection in mass spectrometry. [ABSTRACT FROM AUTHOR]

Mass spectrometry data may be affected by random noise and baseline drift due to experimental instruments and conditions, posing significant challenges for detecting spectral peaks, particularly when identifying weak and separating overlapping peaks. To increase the sensitivity and enhance the resolution, we propose a mass spectral peak detection algorithm that integrates resolution enhancement and image segmentation. Initially, the extended Mexican hat wavelet is proposed by integrating the peak sharpening method with its wavelet. This approach accurately transforms mass spectra into wavelet space using the continuous wavelet transform. Subsequently, the triangular single-peak thresholding method, a more suitable threshold segmentation approach for spectral analysis, is introduced to identify ridges in the two-dimensional wavelet space. Compared to traditional Otsu and its improved variants, long-tailed single-peaked histograms are more effectively processed by this method with lower computational complexity, enabling faster identification of segmentation thresholds and image segmentation. Ultimately, peak positions are determined by utilizing ridge and valley lines in wavelet space along with the original spectrum. To evaluate the performance of the peak recognition algorithm, two metrics are introduced: the receiver operating characteristic (ROC) curve and the balanced F score (F1 score). When compared to multi-scale peak detection (MSPD), continuous wavelet transform and image segmentation (CWT-IS), the developed approach is more suitable for weak and highly overlapping peaks. The robustness and practicality of the method are verified through peak detection using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectra. [ABSTRACT FROM AUTHOR]

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification. • An efficient peak detection algorithm for MS using CWT and genetic algorithm-based Otsu (WSTGA) was proposed. • The genetic algorithm-based Otsu was introduced to find optimal threshold segmentation. • The ridge determination condition was improved to enhance the denoising ability of WSTGA. • WSTGA can detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate. [ABSTRACT FROM AUTHOR]

• Measurement matrix is sparsed into sparse measurement matrix and base dictionary. • The segmented reconstruction method is used to obtain the reconstructed signal. • A new wavelet threshold function is proposed to realize feature enhancement. In the process of bearing signal acquisition, the overloaded data often causes great pressure on the equipment. In order to solve this problem, compressed sensing theory is used to collect signals, and the measurement matrix is sparsed into a sparse measurement matrix and a base measurement matrix. The fusion of the sparse dictionary and the measurement matrix expands the selections of the measurement matrix and improves the reconstruction accuracy. For the phenomenon of the local aggregation of matrix energy caused by the initialization of the sparse measurement matrix, the method of segmented reconstruction is proposed to solve it. Then, in view of the situation that the useful information in the reconstructed signal is polluted by a lot of noise, a new wavelet threshold denoising method is proposed, which is fused with the empirical wavelet transform (EWT), and the margin index and the peak-to-peak value are used as sensitive parameters to adjust the threshold, the effect of denoising and fault feature enhancement are performed simultaneously. Through two-step strategy, the effective acquisition of bearing signals and the enhancement of fault features are realized. The performance of this method is verified by simulation and actual bearing data and the results are encouraging. [ABSTRACT FROM AUTHOR]