LMJSSP for analysis of prophylactic lubricants, spermicides and residues

Deng, J. S.; Metwally, Haidy; Theriault, R.; Richardson, R. H.; Ellis, Randy E.; Oleschuk, Richard D.

doi:10.1016/j.talanta.2023.124959

Cited by 1 publication

(2 citation statements)

References 37 publications

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“…Among them, some studies focus on the classification and evaluation of peaks. 26−28 Some focus on the analysis of mass spectrometry data in the fields of metabolomics, 29−32 food and drug analysis, 33−40 biomedical analysis and disease diagnosis, 41,42 and volatile organic compound (VOC) detection. 43,44 And others are devoted to the use of machine learning methods to achieve fast and accurate database matching.…”

Section: ■ Introductionmentioning

confidence: 99%

“…With the continuous development of machine learning techniques, they have gained more and more applications in the field of mass spectrometry. Among them, some studies focus on the classification and evaluation of peaks. − Some focus on the analysis of mass spectrometry data in the fields of metabolomics, − food and drug analysis, − biomedical analysis and disease diagnosis, , and volatile organic compound (VOC) detection. , And others are devoted to the use of machine learning methods to achieve fast and accurate database matching. − These methods are primarily concerned with making the most of the data that are already available. Recently, some studies have also begun to design algorithms to automatically adjust the mass spectrometer directly .…”

Section: Introductionmentioning

confidence: 99%

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SR-Unet: A Super-Resolution Algorithm for Ion Trap Mass Spectrometers Based on the Deep Neural Network

Ai,

Zhao,

et al. 2023

Anal. Chem.

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The mass spectrometer is an important tool for modern chemical analysis and detection. Especially, the emergence of miniature mass spectrometers has provided new tools for field analysis and detection. The resolution of a mass spectrometer reflects the ability of the instrument to discriminate between adjacent mass-to-charge ratio ions, and the higher the resolution, the better the discrimination of complex mixtures. Quadrupole ion traps are generally considered as a low-resolution mass spectrometry method, but they have gained wide attention and development in recent years because of their suitability for miniaturization and high qualitative capability. For an ion trap mass spectrometer, the mass sensitivity and resolution can be mutually constrained and need to be balanced by setting an appropriate scanning speed. In this study, a super-resolution U-net algorithm (SR-Unet) is proposed for ion trap mass spectrometry, which can estimate the possible ions from the overlapping ion peaks of low-resolution spectra and improve the equivalent resolution while ensuring sufficient sensitivity and analysis speed of the instrument. By determining the mass spectra of a linear ion trap mass spectrometer (LTQ XL) in Turbo and Normal scan modes, the same unit mass resolution as that at a scan speed of 16,667 Da/s was successfully obtained at 125,000 Da/s. Also, the experiments demonstrated that the algorithm is capable of the mass-to-charge ratio and instrument migration. SR-Unet can be migrated and applied to a miniature mass spectrometer for cruise detection of volatile organic compounds (VOCs), and the identification of VOC species in Photochemical Assessment Monitoring Stations (PAMS) was improved from 31 to 50 species with the same monitoring and analysis speed requirement. Further, super-unit mass resolution peptide detection was achieved on a miniature mass spectrometer with the help of the SR-Unet algorithm, which reduced the full width at half-maxima (FWHM) of bradykinin divalent ions (m/z 531) from 0.35 to 0.15 Da at a scan speed of 375 Da/s and improved the equivalent resolution to 3540. The proposed method provides a new idea to enhance the field mixture detection capability of miniature ion trap mass spectrometers.

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Section: ■ Introductionmentioning

confidence: 99%