Advanced food contaminant detection through multi-source data fusion: Strategies, applications, and future perspectives

Background: The globalization of food supply chains and increasing demands for food safety assurance have highlighted the limitations of traditional analytical methods in detecting contaminants. These conventional approaches often struggle to capture the inherent complexities of food matrices, which are characterized by heterogeneity and dynamic processes. Multi-source data fusion (MSDF) has emerged as a promising solution, offering enhanced capabilities for comprehensive food safety analysis through the integration of multiple analytical techniques. Scope and approach: This review provides a systematic examination of MSDF strategies and applications in food contaminant detection, focusing on the integration of key analytical techniques including spectroscopic methods (near-infrared, mid-infrared, Raman), chromatographic analysis, hyperspectral imaging, electronic noses, and chemical analyses. It analyzes various fusion architectures and levels, preprocessing requirements, and advanced data analysis techniques, including machine learning and chemometrics. Through detailed case studies and comparative analyses, the review evaluates MSDF's effectiveness across different applications in food safety monitoring. Key findings and conclusion: MSDF demonstrates superior performance compared to single-sensor approaches, achieving enhanced sensitivity, specificity, and reliability in detecting various contaminants including pesticides, mycotoxins, pathogens, and adulterants. The review identifies critical challenges including data integration complexity, computational demands, sensor drift, and model interpretability. Emerging solutions through artificial intelligence, edge computing, and IoT technologies show promise in addressing these limitations. The successful implementation of MSDF requires standardized protocols and cross-disciplinary collaboration. As food supply chains become increasingly complex, MSDF's role in ensuring food safety will become more crucial, supported by continuous innovations in sensing technologies, data analytics, and artificial intelligence.