Synergistic approaches of AI and NMR in enhancing food component analysis: A comprehensive review

Background

The integration of artificial intelligence (AI) with nuclear magnetic resonance (NMR) spectroscopy is emerging as a transformative approach in food component analysis, offering innovative solutions for ensuring food quality and safety. Leveraging AI algorithms with NMR spectroscopy has the potential to improve the precision and efficiency of structural analysis in food science, addressing challenges in complex data interpretation and advancing methodologies for food authenticity, origin traceability, and contamination detection.

Scope and approach

This review examined the applications of AI techniques, particularly machine learning (ML) models, including supervised learning (SL), unsupervised learning (UL), and deep learning (DL), in enhancing NMR data interpretation and analysis. We discuss foundational NMR principles and explore the integration of AI in resolving spectral complexity and improving food component characterization. This structured approach assesses role of AI in spectral prediction, pattern recognition, and data-driven model development in the context of food science.

Key findings and conclusions

AI-NMR integration demonstrates significant potential in various food chemistry applications, from predicting structural properties to detecting adulterants and contaminants. SL models offer accuracy in spectral prediction, UL models enhance pattern recognition, and DL approaches enable the analysis of complex NMR spectra. However, challenges remain, particularly in model interpretability, data availability, and robustness under real-world conditions. Addressing these challenges requires interdisciplinary collaboration between AI experts and food chemists to unlock full potential of AI-NMR in food analysis and quality control, potentially revolutionizing methodologies in the field.