Multimodal machine learning models for real-time food safety analysis
This solution leverages multimodal machine learning models for analyzing food safety by integrating diverse sensor outputs. It aims to provide real-time analysis and predictions using neural networks capable of processing various data inputs, enhancing food safety monitoring.
Overview
This innovative solution utilizes multimodal machine learning models to enhance food safety analysis. By integrating one-dimensional and two-dimensional outputs from various sensors and devices, the system establishes neural networks for real-time monitoring and prediction of food safety statuses. This technology capitalizes on the ability to decode and predict conditions by analyzing diverse sensor data, enhancing the accuracy and reliability of food safety assessments.
Technical specifications
The core technology involves the development of bespoke multimodal neural networks capable of processing and analyzing data from various modalities. These models combine:
- One-dimensional time-series signals from sensors
- Two-dimensional image inputs from diagnostic test kits
- Multimodal inputs that integrate different data types for comprehensive analysis
The system employs Explainable AI (XAI) techniques such as SHAP, LIME, and CAM to visualize and elucidate how different data components impact food safety, ensuring transparency and unbiased results.
Technology readiness level
Currently at Technology Readiness Level 4, the solution has been validated in a laboratory setting. The next steps involve further literature review and dataset analysis to design specific neural networks for enhanced food safety monitoring, leveraging the complementarity of different modalities.
About University of Cambridge
Cambridge is a collegiate, comprehensive research university that combines broad academic depth with world-class facilities and strong translational activity. Industry engages through co-located labs, a major biomedical campus integrated with an NHS hospital system, and research and technology parks across the Cambridge cluster. Its location in a mature startup and scale-up ecosystem provides ready access to venture funding, prototyping resources, and collaboration with global R&D sites. Research is backed by competitive UKRI council funding and other national sources, with additional support from major charities and European programs. A dedicated technology transfer office supports IP, licensing, and seed investment to accelerate commercialization.
delivered to your inbox.