AI-based modified atmosphere packaging for fruits and vegetables preservation: research progress and prospects

Area of Science:

• Food Science
• Agricultural Engineering
• Computer Science

Background:

• Modified atmosphere packaging (MAP) is crucial for preserving fruits and vegetables (F&Vs).
• Traditional methods face challenges with diverse F&Vs, packaging materials, and gas compositions.
• Existing mathematical tools are insufficient for accurate MAP design, monitoring, and prediction.

Purpose of the Study:

• To review the application of artificial intelligence (AI) in MAP for F&Vs.
• To analyze the literature and research landscape of AI in F&V MAP using bibliometric analysis.
• To explore the classifications and joint applications of MAP and AI in F&V preservation.

Main Methods:

• Bibliometric analysis of AI in F&V MAP literature.
• Review of AI applications in MAP for F&Vs, including mechanisms, design, optimization, monitoring, and prediction.
• Classification and analysis of joint MAP and AI strategies.

Main Results:

• AI shows promise in exploring mechanisms, packaging design, parameter optimization, quality monitoring, and shelf-life prediction for F&V MAP.
• Preliminary progress has been made in applying AI-based MAP for F&Vs.
• Bibliometric analysis provides an overview of AI's role and research trends in this field.

Conclusions:

• AI-based MAP for F&Vs requires further development towards high precision, throughput, automation, and cost-effectiveness.
• Challenges include data scarcity, model complexity, high learning costs, and output verification.
• Specific issues like sample variability, material selectivity, and gas-sample-film interactions need more research.