Processing and Real-Time Monitoring Strategies of Aflatoxin Reduction in Pistachios: Innovative Nonthermal Methods,...

Area of Science:

• Food Science
• Mycology
• Analytical Chemistry

Background:

• Aflatoxin (AF) contamination in pistachios is a significant food safety and trade concern due to the carcinogenicity of AF-B1 and susceptibility to Aspergillus infection.
• Conventional decontamination methods often compromise pistachio's sensory and nutritional value, necessitating sustainable alternatives.

Purpose of the Study:

• To review advancements in nonthermal interventions for reducing aflatoxin contamination in pistachios.
• To explore the potential of emerging biosensing technologies for rapid and sensitive aflatoxin detection.
• To highlight the integration of artificial intelligence and machine learning for real-time risk assessment.

Main Methods:

• Review of recent literature on nonthermal decontamination strategies (e.g., pulsed light, cold plasma, nanomaterials, bioactive coatings).
• Analysis of progress in biosensing technologies (e.g., electrochemical immunosensors, aptamer-based systems, optical/imaging tools).
• Exploration of AI/ML applications for integrating spectral, sensor, and imaging data for fungal and aflatoxin prediction.

Main Results:

• Nonthermal interventions show significant potential in reducing fungal growth and AF accumulation while preserving pistachio quality.
• Biosensing technologies are enabling rapid, portable, and sensitive detection of aflatoxins.
• AI/ML models can integrate diverse data for real-time prediction of fungal development and AF risk.

Conclusions:

• Emerging nonthermal strategies and advanced biosensing offer effective solutions for pistachio safety management.
• The integration of data-driven approaches like AI/ML is crucial for real-time risk assessment and regulatory compliance.
• These innovative tools have the potential to transform pistachio safety and trade.