Image learning to accurately identify complex mixture components

The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniques have the ability to cope with this problem in a data-driven way. Herein, we report a 2D-spectral imaging method to collect matter information from mixture components, and the obtained feature images can be easily provided to deep convolutional neural networks (CNNs) for establishing a spectral network. The results demonstrated that a single CNN trained end-to-end from the proposed images can directly accomplish synchronous measurement of multi-component samples using only raw pixels as inputs. Our strategy has some innate advantages, such as fast data acquisition, low cost, and simple chemical treatment, suggesting that it can be extensively applied in many fields, including environmental science, biology, medicine, and chemistry.