Accurate compositional analysis on complex mixtures via multi-task spectral data learning

Accurate compositional analysis of complex mixtures directly from their overlapping spectral data is critical for advancements in materials discovery, process control, and environmental monitoring. However, existing deep learning models often lack a mechanism to enforce logical consistency, leading to physically implausible predictions, such as assigning a concentration value to a component that is simultaneously classified as absent.

We propose a novel multi-task learning framework to explicitly link component identification and quantification. It introduces a prediction masking mechanism where the probabilistic outputs from a multi-label classification branch directly guide the predictions of a parallel regression branch, ensuring concentrations are only predicted for components identified as present. With ResNet1D as the feature extractor, experimental results on the MetalOxides benchmark dataset show that our framework substantially outperforms common machine learning methods.

This framework enforces physical plausibility in spectral predictions, providing a more logically consistent and accurate tool for complex mixture analysis. This work is significant for advancing critical applications in materials discovery, process control, and environmental monitoring that rely on dependable quantitative analysis.