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Peak Detection Algorithm for Mass Spectrometry Integrating Weighted Continuous Wavelet Transform with Particle Swarm Optimization-Based Otsu

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.

Open Access
Improved Prediction of Oxide Content in Cement Raw Meal by Near-Infrared Spectroscopy Using Sequential Preprocessing through Orthogonalization (SPORT)

Near-infrared (NIR) spectroscopy is a nondestructive technique extensively employed in various fields. Despite its advantages, near-infrared spectroscopy still faces significant challenges due to the intricate physical and chemical phenomena that arise from the interaction between light and matter. This interaction typically results in light absorption and scattering, leading to the NIR signal containing comprehensive information about these phenomena’s interactions. Accurate determination of and in cement raw meal requires minimizing scattering effects from the spectrum, but selecting an appropriate pretreatment technique is often challenging. In this paper, we enhance the predictive ability of NIRS for determining the four oxides in cement raw meal by implementing sequential preprocessing through orthogonalization (SPORT). The SPORT method uses sequential orthogonal partial least squares (SO-PLS) to integrate data blocks obtained from different preprocessing techniques. We compare our method with conventional pretreatment methods for determining the content of four oxides in raw materials of cement using near-infrared spectroscopy. The results suggest that the SPORT method exhibits commendable calibration performance and distinctive characteristics. Moreover, SPORT demonstrates significant preprocessing selectivity, making it effective in addressing the challenges associated with complex interactions in near-infrared spectral analysis. In conclusion, the utilization of SPORT sequential pretreatment in near-infrared spectroscopy shows promising results for enhancing the accuracy and efficiency of determining oxide content in raw materials of cement. The findings of this study help promote the application of near-infrared spectroscopy in the cement industry, especially quality control. Further exploration of the SPORT method’s potential in other materials analysis fields may open new avenues for nondestructive techniques in various scientific disciplines.

Determination of Phenolics and Biological Activity of Viola dalatensis Gagnep. by High-Performance Liquid Chromatography (HPLC) and Multivariate Analysis

Viola dalatensis, belonging to the Violaceae family, is an endemic species to Vietnam. The present study aimed to determine phenolic compounds in extracts from aerial parts of the plant species using high performance liquid chromatography with diode-array detection (HPLC-DAD). In addition, free radical scavenging and bovine albumin denaturation inhibition assays were performed to evaluate the antioxidant and in vitro anti-inflammatory activities of the extracts. Most of the phenolic compounds in the methanolic extract were detected at the greatest concentrations compared to the other extracts, which was also supported by the highest total phenolic content (26.70 ± 0.1059 mg GAE/g). In general, the antioxidant activities of the methanolic and ethanolic extracts were found to be stronger than those of the other extracts. The aqueous extract exhibited the most potent inhibitory effect on albumin denaturation (IC50 = 90.39 ± 8.415 µg/mL), comparable with that of diclofenac. Through principal component analysis, 88.60% of the total variance in the phenolic dataset was explained. The correlation analysis showed that chlorogenic acid, ferulic acid, epigallocatechin gallate, rutin, and kaempferol may play important roles in antioxidant activity while gallic acid could contribute to in vitro anti-inflammatory activity of the extracts. The findings of this study provide the first evidence of the bioactive compounds present in V. dalatensis and shed light on the potential health-promoting properties linked to its aerial parts.

Determination of Polystyrene Microplastic in Soil by Pyrolysis – Gas Chromatography – Mass Spectrometry (pyr-GC-MS)

Pyrolysis-gas chromatography-mass spectrometry (pyr-GC-MS) is emerging as a promising alternative for the detection and quantification of microplastic pollution. For a robust quantification it is essential to improve our understanding of interferences in the pyrolysis of microplastics. Here we investigate the effects of different soil matrices, mainly differing by their organic carbon content (Corg, 1.0–13.6%), and of the polymer molecular weight (Mw) on the pyr-GC-MS analysis of polystyrene (PS) microplastics. In addition, we evaluated the effectiveness of adding poly(4-fluorostyrene) (PSF) as internal standard to circumvent the matrix effects. The three main markers of PS pyrolysis, i.e., styrene, styrene-dimer and styrene-trimer, were monitored. The ratio between the dimer and the trimer significantly varied between the matrices and tended to decrease with the increasing of the Corg in the soil, mainly due to an increased trimer formation. A strong matrix effect affected the slope of the calibration curves by 2 to 8-fold and was correlated with the Corg in the soils. This effect was mitigated when the areas of the markers were normalized by the area of the corresponding marker of PSF. PS of low Mw (Mw 35,000) presented a reduced formation of the three markers compared to PS of high Mw (Mw 400,000), and styrene-dimer was proportionally less formed than the other two markers. Differences in the slopes of calibration curves depended on the marker chosen, highlighting the relevance of selecting the pyrolysis marker in the quantification of microplastics using pyr-GC-MS.