This article focuses on 3D fluorescence spectroscopy. After defining its principle, we then look at the factors which influence the phenomenon of fluorescence. There follows a brief discussion of the principal chemometric techniques implemented to manipulate and exploit fluorescence data, which have different and specific properties. A new article will describe the main endogenous fluorophores found in certain food, biological and environmental matrices, as well as exogenous fluorophores. Frontal fluorescence has seen considerable development during the past 15 years in a wide variety of fields, which is why we shall also be focusing on its principal applications in terms of the objectives of different studies: classification, authentication, quality control or process monitoring indicators. Fluorescence data generate a spectral fingerprint that can characterise samples within a very large space of variability, such as that which is inherent in food samples. Fluorescence spectroscopy can thus be used in a broad range of applications involving biological samples, animal tissues or environmental samples. Most of these applications are still qualitative, although quantitative methods are available. It is generally acknowledged that 3D fluorescence fingerprinting is more suitable for recognition, classification or detection processes when there is a need to save time and achieve optimum sensitivity. With the recent development of big data and BI, frontal fluorescence spectroscopy will soon benefit from the considerable power of artificial intelligence technologies.