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Day 2, June 11（Tue.） 9:35-9:55 Room B (Convention Hall 200)
- 2B-S-0935
Construction of a Mass Spectrum Library Containing Predicted EI Mass Spectra Using a Machine Learning Model and the Development of Structure Elucidation Method
(JEOL)
oAyumi Kubo, Azusa Kubota, Masaaki Ubukata, Kenji Nagatomo
There are many compounds that are not registered in existing mass spectral libraries. We have developed a qualitative analysis method that combines a machine learning model, soft ionization method, and accurate mass analysis for the purpose of estimating the structure of such compounds. We have improved the accuracy of structural formula estimation using this method by making two improvements. The first improvement is narrowing down the structural formula candidates using the retention index (RI). Before comparing EI mass spectra, we added a process to compare the RI predicted from the structural formula and the measured RI and exclude candidates with large RI differences. The second improvement is to improve the EI mass spectrum prediction accuracy by optimizing hyperparameters and feature vectors. We investigated the effect of each parameter using an orthogonal array and determined the optimal level for each.