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.

中文翻译：

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图像学习以准确识别复杂的混合物成分

复杂混合物的研究对于探索自然现象的演化非常重要，但混合物的复杂性大大增加了材料信息提取的难度。基于图像感知的机器学习技术有能力以数据驱动的方式解决这个问题。在此，我们报告了一种从混合成分中收集物质信息的二维光谱成像方法，并且可以轻松地将获得的特征图像提供给深度卷积神经网络 (CNN) 以建立光谱网络。结果表明，从所提出的图像端到端训练的单个 CNN 可以仅使用原始像素作为输入直接完成多分量样本的同步测量。我们的策略具有一些先天优势，例如数据获取快、成本低、