【摘要】 三维荧光光谱作为一种快检技术，通过捕捉激发波长-发射波长-荧光强度的三维矩阵信息，全面表征样品中的荧光物质特性。近年来，机器学习算法在特征提取、模式识别和图像重构等领域实现突破。基于机器学习的三维荧光技术通过算法与光谱/成像技术的深度融合，能够解决传统方法难以应对的高维数据解析、图像退化、快速分类等问题，已在环境、农产品/食品、中药材等领域展现出巨大的应用价值。系统梳理了该技术在环境监测、农产品质量安全、中药材质量控制等方面的研究进展，旨在为推动三维荧光技术在该交叉领域的应用提供参考。更多还原

【Abstract】 Three-dimensional fluorescence spectroscopy, as a rapid detection technique, comprehensively characterizes the properties of fluorescent substances in samples by capturing the three-dimensional matrix information of excitation wavelength-mission wavelength-fluorescence intensity. In recent years, machine learning algorithms have achieved breakthroughs in fields such as feature extraction, pattern recognition, and image reconstruction. Three-dimensional fluorescence technology based on machine learning, through the deep integration of algorithms and spectral/imaging techniques, can solve problems such as high-dimensional data analysis, image degradation, and rapid classification that are difficult to handle with traditional methods. It has demonstrated significant application value in fields such as the environment, agricultural products/food, and traditional Chinese medicinal materials. The research progress of this technology in environmental monitoring, the quality and safety of agricultural products, and the quality control of traditional Chinese medicinal materials is systematically reviewed, aiming to provide references for further research in this cross-disciplinary field.更多还原