Ai-driven imaging and spectral analysis for enhanced food safety

Overview

This innovative solution leverages Artificial Intelligence (AI) to enhance food safety by analyzing imaging and spectral data. By utilizing inputs from imaging, spectral imaging, and near-infrared spectrometers, these AI models can detect contaminants, adulterants, and toxins in food products. The technology is designed to improve food security and logistics, offering tailored applications that evolve by incorporating new data and insights from previous models. This adaptability ensures that the AI models remain effective and relevant in addressing food safety challenges.

Technical specifications

• Data Sources: Utilizes RGB, multi/hyperspectral imagery, and near-infrared spectrometer signals.
• AI Models: Over 35 machine learning algorithms, including 17 artificial neural networks (ANN), are tested for optimal performance.
• Detection Targets: Focuses on volatile aromatic compounds, protein content adulterants, and toxins.
• Sensor Integration: Incorporates advanced sensor technologies for enhanced detection accuracy.
• User Interface: AI models are deployed on user-friendly software with graphical interfaces for easy implementation.

Technology readiness level

The technology is currently at TRL 6, indicating a prototype system has been demonstrated in a relevant environment. Future validation steps include further multivariate data analysis and AI model deployment in new food products, ensuring continuous improvement and refinement.