An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items.

Area of Science:

• Food Science and Technology
• Public Health
• Biotechnology

Background:

• The COVID-19 pandemic highlights the critical need for effective sanitization of consumables to prevent disease transmission.
• Traditional methods like boiling can degrade the nutritional content and alter the texture of fruits and vegetables.
• Germicidal Ultraviolet C (UVC) light shows promise for pathogen inactivation but risks damaging food components with excessive exposure.

Purpose of the Study:

• To design an intelligent device for sanitizing edible items using Germicidal Ultraviolet C (UVC) light.
• To develop an Artificial Intelligence (AI) system capable of automatically identifying edible items and optimizing UVC exposure.
• To ensure the preservation of nutritional quality and safety of food products during the sanitization process.

Main Methods:

• Development of a novel device integrating Artificial Intelligence (AI) with Germicidal Ultraviolet C (UVC) technology.
• Implementation of an AI model for auto-detection of edible items to determine appropriate UVC dosage.
• Design of a smart architecture for uniform UVC light distribution across the entire surface of food items.

Main Results:

• The proposed device successfully auto-detects edible items, enabling tailored UVC application based on permissible limits.
• Consistent distribution of UVC light ensures comprehensive surface sanitization without compromising food integrity.
• The system effectively inactivates surface pathogens while preserving the nutritional value and quality of edible items.

Conclusions:

• The AI-powered UVC sanitization device offers a safe and effective solution for maintaining food hygiene.
• This technology addresses the limitations of conventional sanitization methods by preserving nutritional content.
• The smart device presents a significant advancement in ensuring the safety and quality of edible goods in public health contexts.