シンポジウムセッション
第2日 6月11日（火） 9:35～9:55 B会場（中ホール200）
- 2B-S-0935
機械学習を用いた予測EIマススペクトルライブラリーの構築と構造推定方法の開発
(JEOL)
o久保歩・ 窪田梓・ 生方正章・ 長友健治
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.