青柳 里果

ABSTRACT Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for imaging molecules in biological tissues owing to its high spatial resolution and sensitivity. Effective detection of key molecules in a sample is crucial for detailed evaluation of complex samples such as tissues. In this study, a target is a biomolecule, allantoin (C₄H₆N₄O₃), and the allantoin [M + H]⁺ and [M‐H]⁻ are adequately detected using ToF‐SIMS with a Bi cluster ion beam from an allantoin control sample. However, the detection of ions related to allantoin permeated in human skin tissue is not straightforward because there are interfering mass peaks in the ToF‐SISM spectra of control skin samples that make allantoin detection challenging, and the allantoin fragment ions have the same chemical structure as the fragment ion from other biomolecules in the tissue. In order to sufficiently detect the allantoin‐related ions, we focused on ion beams with a higher ionization yield, such as gas cluster ion beams (GCIBs). As a result, a ToF‐SIMS with water GCIB was significantly more effective in detecting the allantoin [M + H]⁺ and [M‐H]⁻ in the skin samples in both positive and negative ion spectra. The results revealed that the water GCIB approach is better suited for studying biological samples, as it effectively distinguishes the mass peaks of allantoin related ions using ToF‐SIMS.

The interpretation of time-of-flight secondary ion mass spectrometry (ToF-SIMS) data is often complicated because ToF-SIMS has a high sensitivity for detecting extremely low amounts of molecules and generally produces numerous types of fragment ions from each molecule. Although machine learning techniques have been applied to such complex ToF-SIMS data interpretation to classify the components in a sample, identifying unknown molecules is often difficult, even after classification or segmentation of complex datasets. We developed a new secondary ion mass spectrometry (SIMS) identification system based on full ToF-SIMS spectra by applying a supervised machine learning method, random forest (RF), with effective teaching information to express common organic molecules. We automatically extracted chemical structures for unknown material identification from string-converted molecules using a simplified molecular-input line-entry system. The ToF-SIMS spectra of 32 organic molecules, including peptides, polymers, and biomolecules such as cellulose, were used as a training dataset, and these molecules were correctly predicted using the SIMS identification system. The importance of RF indicated that mass peaks representing these structures were detected in the ToF-SIMS spectra and that the materials were identified based on the essential chemical structures of a target molecule. Moreover, the ToF-SIMS spectra of Styrofoam-like Ocean plastic samples were correctly identified as polystyrene by the system. This study demonstrates the potential of our SIMS identification system to accurately identify unknown organic molecules from full ToF-SIMS spectra, offering a robust approach for expanding molecular identification in complex samples.