Machine Learning-Enhanced Fluorescence Spectroscopy for Food Safety and Environmental Monitoring: Recent Advances, Applications, and Future Perspectives

Abstract

Fluorescence spectroscopy (FS) has gained increasing attention as a rapid, sensitive, and non-destructive analytical technique for food safety evaluation and environmental monitoring. However, its practical applications are often constrained by complex sample matrices, spectral overlap, high-dimensional data structures, and nonlinear relationships between fluorescence features and target analytes. Recent advances in machine learning (ML) provide powerful strategies for extracting informative patterns from complex spectral datasets. When appropriately trained and validated, ML may also improve the efficiency and predictive performance of fluorescence-based analysis. In this review, we systematically summarize the recent progress in ML-assisted FS, with particular emphasis on the integration of chemometric methods, classical ML, and deep learning (DL) approaches for spectral preprocessing, feature extraction, classification, quantitative prediction, and data interpretation. Representative applications in food contaminant detection, authenticity assessment, quality evaluation, and environmental pollutant monitoring are discussed. Furthermore, we critically analyze the challenges associated with dataset limitations, model interpretability, overfitting, and real-world deployment. Finally, emerging directions, including explainable artificial intelligence, transfer learning, multimodal data fusion, and portable intelligent sensing platforms, are highlighted to promote the development of reliable and scalable ML-enhanced fluorescence analytical technologies.