Rapid determination of bulk composition and quality of marine biomass in Mass Spectrometry

Navigating the analysis of mass spectrometry data for marine biomass and fish demands a technologically adept approach to derive accurate and actionable insights. This research will introduce a novel AI methodology to interpret a substantial repository of mass spectrometry datasets, utilizing pre-training strategies like Next Spectra Prediction and Masked Spectra Modeling, targeting enhanced interpretability and correlation of spectral patterns with chemical attributes. Three core research objectives are explored: 1) precise fish species and body part identification via binary and multi-class classification, respectively; 2) quantitative contaminant analysis employing multi-label classification and multi-output regression; and 3) traceability through pair-wise comparison and instance recognition. By validating against traditional baselines and various downstream tasks, this work aims to enhance chemical analytical processes and offer fresh insights into the chemical and traceability aspects of marine biology and fisheries through advanced AI applications.