Ml-enabled platform for enhanced food safety using deep learning
An easy-to-use, expandable platform using deep learning to improve food safety by analyzing hyperspectral and NIR images. Designed for food scientists with basic training materials.
Overview
The ML-enabled platform is designed to improve food safety through advanced deep learning techniques. By leveraging machine learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the platform analyzes hyperspectral and near-infrared (NIR) images along with other digital data. This solution empowers food scientists, even those without extensive data science training, to make informed decisions regarding food safety and quality.
Technical specifications
Key features:
- Utilizes deep learning models for image analysis, including CNNs and RNNs.
- Capable of processing large datasets from hyperspectral and NIR imaging.
- Provides a user-friendly interface with expandable capabilities.
- Includes basic training materials for ease of use by non-specialists.
- Supports classification, regression, and optimization tasks.
Technology readiness level
Currently at Technology Readiness Level 4, the platform has undergone initial testing with experimental validation expected through further development. The platform is in the stage of defining machine learning strategies and validating models with real-world datasets.
About University of Tennessee, Knoxville
UT offers a partnership model that reduces risk, accelerates time-to-value, and supports long-term growth strategies for industry—providing an attractive platform for companies seeking to formalize collaboration and establish a long-term presence in the East Tennessee region. By aligning institutional investment with regional and national priorities, UT provides a stable and effective platform for industry collaboration, technology deployment, and sustained economic impact.
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