Atomic force microscopy in food industry: Bridging nanoscale analysis and real-world applications
Year: 2026
Type: article
Abstract: Background Food's physical, chemical, and mechanical properties originate from its molecular-level organization, yet analyzing these in complex, hydrated systems is challenging. Nanoscale phenomena influence macroscopic texture, stability, and digestibility. Atomic Force Microscopy (AFM) uniquely bridges this gap by visualizing and probing materials in near-native liquid environments without destructive preparation. Scope and Approach This review critically analyzes AFM’s expanding role across the food production chain, from biopolymer characterization to complex systems like emulsions and foams. We highlight emerging applications in the nanomechanics of mouthfeel, active packaging, and sustainable agriculture. Unlike reviews focused on static imaging, this article emphasizes AFM's transition to a predictive tool, connecting nanomechanical data (elasticity, adhesion) to sensory perception and processing behavior. Key Findings and Conclusions AFM is evolving into a predictive analytical platform for controlling textural attributes and ingredient performance. Key advancements, relevant for process efficiency, include quantifying pico-Newton forces to model texture and detecting microbial biofilms for safety assurance. Integration with techniques like Raman spectroscopy offers simultaneous chemical-mechanical mapping. Finally, AI and automation are transforming AFM into a high-speed standard for industrial formulation, quality control, and shelf-life prediction.
Authors: Rahul Singh, Shubhangi Srivastava, Aberham Hailu Feyissa, Danylo Komisar, Christian Vinther Bertelsen, Winnie Edith Svendsen, Maria Dimaki, Jyoti Shanker Pandey
Institutions: Technical University of Denmark, University of Hohenheim
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $5,440
Awards: G2059263106, G4457329854
OpenAlex ID: W7129090112
