Background In analytical chemistry, spatial information about materials is commonly captured through imaging techniques, such as traditional color cameras or with advanced hyperspectral cameras and microscopes. However, efficiently extracting and analyzing this spatial information for exploratory and predictive purposes remains a challenge, especially when using traditional chemometric methods. Recent advances in deep learning and artificial intelligence have significantly enhanced image processing capabilities, enabling the extraction of multiscale deep features that are otherwise challenging to capture with conventional image processing techniques. Despite the wide availability of open-source deep learning models, adoption in analytical chemistry remains limited because of the absence of structured, step-by-step guidance for implementing these models. Results This tutorial aims to bridge this gap by providing a step-by-step guide for applying deep learning approaches to extract spatial information from imaging data and integrating it with other data sources, such as spectral information. Importantly, the focus of this work is not on training deep learning models for image processing but on using existing open source models to extract deep features from imaging data. Significance The tutorial provides MATLAB code tutorial demonstrations, showcasing the processing of imaging data from various imaging modalities commonly encountered in analytical chemistry. Readers must run the tutorial steps on their own datasets using the codes presented in this tutorial.

翻译：背景：在分析化学中，材料的空间信息通常通过成像技术获取，如传统彩色相机或先进的高光谱相机与显微镜。然而，高效提取并分析这些空间信息以用于探索性和预测性目的仍具挑战性，尤其是在使用传统化学计量学方法时。深度学习与人工智能的最新进展显著提升了图像处理能力，使得提取多尺度深度特征成为可能，而这些特征通过传统图像处理技术难以捕获。尽管开源深度学习模型广泛可用，但由于缺乏实施这些模型的结构化、逐步指导，其在分析化学中的应用仍受限。结果：本教程旨在通过提供逐步指南，弥合这一差距，指导如何应用深度学习方法从成像数据中提取空间信息，并将其与其他数据源（如光谱信息）整合。重要的是，本工作的重点不在于训练用于图像处理的深度学习模型，而在于利用现有开源模型从成像数据中提取深度特征。意义：本教程提供了MATLAB代码演示，展示了处理分析化学中常见各种成像模态数据的方法。读者需使用本教程中的代码，在自己的数据集上运行教程步骤。