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Accurate compositional analysis on complex mixtures via multi-task spectral data learning
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AI
DOI：10.1016/j.aca.2025.345050.png)
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Abstract
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• A mask-guided multi-task (MGMT) framework is proposed for complex compositional analysis. • The prediction masking mechanism enforces logical consistency in multi-task outputs. • ResNet1D enhances the accuracy of quantitative analysis. • 179,072 spectral data have been analyzed using MGMT framework. • MGMT framework outperforms common methods in terms of accuracy.