タンデム質量分析計を搭載した飛行時間型二次イオン質量分析法と機械学習による海洋プラスチックの評価

抄録

Ocean plastic collected from the sea around Japan was evaluated using ToF-SIMS with tandem mass spectrometer (MSMS) and machine learning. The expanded polystyrene-like ocean plastic samples were selected and then they were classified into smaller size (less than 7 nm) and larger size (more than 7 nm). The depth profiles of the smaller and larger size expanded polystyrene-like ocean plastics were measured with ToF-SIMS and then some of the PS-related peaks were selected as precursor ions for MSMS to confirm their chemical structures. The MSMS spectra showed the pathways of each PS-related fragment ion. The ToF-SIMS datasets were analyzed using unsupervised machine learning methods such as principal component analysis (PCA) and sparse autoencoder (SAE). These methods showed that there were differences between the smaller and larger plastic samples and between the analysis depths of the depth profiles. These analysis results are useful for the evaluation of ocean plastic samples.