Paper spray ionization-high-resolution mass spectrometry (PSI-HRMS) of peroxide explosives in biological matrices

Selectivity of Explosives Analysis with Ambient Ionization Single Quadrupole Mass Spectrometry: Implications for Trace Detection

Ambient ionization (AI) is a rapidly growing field in mass spectrometry (MS). It allows for the direct analysis of samples without any sample preparation, making it a promising technique for the detection of explosives. Previous studies have shown that AI can be used to detect a variety of explosives, but the exact gas-phase reactions that occur during ionization are not fully understood. This is further complicated by differences in mass spectrometers and individual experimental set ups between researchers. This study investigated the gas-phase ion reactions of five different explosives using a variety of AI techniques coupled to a Waters QDa mass spectrometer to identify selective ions for explosive detection and identification based on the applied ambient ionization technique. The results showed that the choice of the ion source can have a significant impact on the number of ions observed. This can affect the sensitivity and selectivity of the data produced. The findings of this study provide new insights into the gas-phase ion reactions of explosives and could lead to the development of more sensitive and selective AI-based methods for their detection.

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

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.

SWIFTSIN: A High-Resolution Ion Isolation Waveform for the Miniaturized Linear Ion Trap Mass Spectrometer by Coarse to Fine Excitation

To figure out the reason for the drawback of the stored waveform inverse Fourier transform (SWIFT) waveform and realize the high-resolution ion isolation on the miniaturized linear ion trap mass spectrometer, we studied the efficiency that ions can be excited under different excitation durations and amplitudes at different frequencies and compared the overlap ratios of the effective excitation frequency bandwidths of the adjacent ions. According to this, we proposed a new coarse-to-fine isolation waveform named SWIFTSIN. By superposing one or more sinusoidal waveforms on the SWIFT waveform and modulating the phases of the superposed sinusoidal waveforms, the generated SWIFTSIN waveform can achieve unit mass isolation on the miniaturized linear ion trap mass spectrometer without reducing the intensity of the target ion. The isolation ability of the SWIFTSIN waveform was verified by isolating a single isotope peak in the mixed samples.

Rapid Detection of Estrogens in Cosmetics by Chemical Derivatization and Paper-Spray Ionization Mass-Spectrometry

Estrogens in personal care products are harmful to customers. Conventional methods such as HPLC and LC-MS require tedious sample pretreatment and long analytical time. Paper-spray ionization mass spectrometry (PSI-MS) is a powerful tool for the determination of compounds with little time and minimal pretreatment procedures. Since most estrogens show poor responses in PSI-MS, we developed a chemical derivatization and PSI-MS method to determinate three estrogens: estradiol, estriol and ethinyloestradiol with estradiol valerate as the internal standard (I.S.). After derivatization with 2-fluoro-1-methyl-pyridinium-p-toluene-sulfonate, the three estrogens could be quantified in seconds. This method showed good linearity in the range of 0.1~30 μg·mL^-1, with R² > 0.999. Their recovery results were all between 85%~115%. The limits of detection (LOD) were 0.04 μg·mL^-1, 0.02 μg·mL^-1 and 0.02 μg·mL^-1 for estradiol, estriol and ethinyloestradiol respectively, which improved around 200, 2000, and 900 times compared to non-derivative PSI-MS. The method could quantitatively determine estrogens in cosmetics.

Applications of ambient ionization mass spectrometry in 2021: An annual review

Ambient ionization mass spectrometry (AIMS) has revolutionized the field of analytical chemistry, enabling the rapid, direct analysis of samples in their native state. Since the inception of AIMS almost 20 years ago, the analytical community has driven the further development of this suite of techniques, motivated by the plentiful advantages offered in addition to traditional mass spectrometry. Workflows can be simplified through the elimination of sample preparation, analysis times can be significantly reduced and analysis remote from the traditional laboratory space has become a real possibility. As such, the interest in AIMS has rapidly spread through analytical communities worldwide, and AIMS techniques are increasingly being integrated with standard laboratory operations. This annual review covers applications of AIMS techniques throughout 2021, with a specific focus on AIMS applications in a number of key fields of research including disease diagnostics, forensics and security, food safety testing and environmental sciences. While some new techniques are introduced, the focus in AIMS research is increasingly shifting from the development of novel techniques toward efforts to improve existing AIMS techniques, particularly in terms of reproducibility, quantification and ease-of-use.