Next-generation microfluidics based on artificial intelligence: Applications for food sample analysis

Abstract

Background

Microfluidics has transformed research across science, offering advantages like reduced sample waste and costs over traditional methods. Despite these benefits, microfluidics generates large datasets, posing analysis challenges with conventional tools. To address this, researchers integrate artificial intelligence (AI) with microfluidics. In food safety research, a critical area for human health, precise and reliable platforms are essential. AI-integrated microfluidics platforms show promise, attracting attention for their unique advantages in food sample analysis.

Scope and approach

This review explores recent advancements in integrating artificial intelligence (AI) with microfluidics for food sample analysis. It introduces AI and microfluidics principles, discusses their synergistic applications, and examines various algorithms and microfluidic chip designs. It highlights AI-microfluidics integration to enhance food analysis through data processing, pattern recognition, and predictive modeling. It then discusses progress, challenges, and opportunities in this interdisciplinary approach and its potential impact on food analysis.

Key findings and conclusions

Integrating AI and microfluidics creates a powerful platform for rapid detection in food analysis, enhancing accuracy, sensitivity, and real-time data processing. This interdisciplinary approach unlocks new possibilities in food safety, quality control, and environmental assessment. Future research should prioritize refining AI algorithms, integrating advanced sensors, addressing scalability, and developing regulatory frameworks to support widespread adoption.