DeepMIR: A Hybrid Convolutional Neural Network-Transformer Framework for Accurate Identification of Target Components from Mid-Infrared Spectra of Mixtures

< 0.05). Extensive real-world validation demonstrates high accuracy across diverse scenarios, 94.8% for complex liquid solvents, 93.9% for solid pigment mixtures relevant to cultural heritage, and 99.1% for commercial blended textiles. Dedicated limit-of-detection studies establish reliable detection thresholds of 20% v/v in liquids and 10% w/w in solids. To ensure accessibility, DeepMIR has been deployed as an open-access web server, providing a powerful and practical tool for the scientific community that significantly outperforms traditional library search methods in both accuracy and reliability.