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.

Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning.

Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was developed. Specifically, volatile compounds in coffee aroma were detected using self-aspiration corona discharge ionization mass spectrometry (SACDI-MS), and the acquired MS data were processed using a customized deep learning algorithm to perform origin authentication automatically. To facilitate high-throughput analysis, an air curtain sampling device was designed and coupled with SACDI-MS to prevent volatile mixing and signal overlap. An accuracy of 99.78% was achieved in the classification of coffee samples from six origins at a throughput of 1 s per sample. The proposed approach may be effective in preventing coffee fraud owing to its straightforward operation, rapidity, and high accuracy and thus benefit consumers.

Deep learning enabled miniature mass spectrometer for rapid qualitative and quantitative analysis of pesticides on vegetable surfaces.

Excessive pesticide use poses a significant threat to food safety. Rapid on-site detection of multi-target pesticide residues in vegetables is crucial due to their widespread distribution and limited shelf life. In this study, a rapid on-site screening method for pesticide residues on vegetable surfaces was developed by employing a miniature mass spectrometer. A direct pretreatment method involves placing vegetables and elution solution into a customized flexible ziplock bag, allowing thorough mixing, washing, and filtration. This process effectively removes pesticide residues from vegetable surfaces with minimal organic solvent usage and can be completed within 2 min. Moreover, this study introduced a deep learning algorithm based on a one-dimensional convolutional neural network, coupled with a feature database, to autonomously discriminate detection outcomes. By combining full scan MS and tandem MS analysis methods, the proposed method achieved a qualitative recognition accuracy of 99.62%. Following the qualitative discrimination stage, the target pesticide residue and internal standard can be simultaneously isolated and fragmented in the ion trap, thus enabling on-site quantitative analysis and warning. This method achieved a quantitative detection limit of 10 µg/kg for carbendazim in cowpea. These results demonstrate the feasibility of the proposed analytical system and strategy in food safety applications.