Data fusion strategies for the integration of diverse non-destructive spectral sensors (NDSS) in food analysis

Abstract

The evolving landscape of agri-food systems, driven by climate change and population growth, necessitates innovative approaches to ensure food integrity, safety, and sustainability. This review explores the role of data fusion strategies, particularly focusing on non-destructive spectroscopic sensors (NDSS) in three key application contexts: in-field monitoring, on/in-line food processing, and food quality authentication. Various data fusion scenarios, including fusing spectra from different spectroscopic platforms, integrating images and spectra, and combining non-spectroscopic sensor data with spectroscopic ones are reviewed. Focus is set on practical considerations, such as selecting the level of data fusion, defining blocks, variable selection, and validation methods, highlighting the importance of tailored approaches based on research aims and data characteristics. While combining information from diverse sensors generally enhances information extraction and modelling performance, their implementation in routine applications is still limited and especially studies focused on data fusion models’ performance over time and their maintenance are lacking.