PREPRINT: SpectroSim: Batch Detection in Marine Biomass

The batch detection of marine biomass constitutes a significant real-world application within the fish processing industry, contributing to food safety, fraud prevention, and stock management. Recent advancements have demonstrated that Rapid Evaporative Ionization Mass Spectrometry (REIMS) when coupled with Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), yields exceptional outcomes in fraud detection, contamination identification, and biomass analysis. Although several studies have employed REIMS and OPLS-DA for species identification and contamination detection—including limited applications to marine biomass—these efforts have not yet addressed the challenge of batch detection, which involves determining the specific batch of processed samples from which a fish originates. Contrastive Learning, an emerging alternative to conventional binary classification, has proven effective for batch detection of marine biomass analyzed via REIMS. Leveraging a high-dimensional REIMS dataset provided by Plant and Food Research, New Zealand, comprising mass spectrometry profiles of New Zealand marine biomass, we propose a novel Contrastive Learning approach termed SpectroSim, building upon the SimCLR framework. The new method introduces a bespoke encoder head, replacing the traditional ResNet backbone with a Transformer architecture, alongside a custom projection head meticulously designed for mass spectrometry data. Comprehensive experimental results indicate that SpectroSim surpasses the balanced classification accuracy of established deep learning frameworks and other prevalent baseline models. Notably, as an unsupervised methodology, SpectroSim achieves near-perfect accuracy (98.02%) in a self-supervised context, independent of class labels.