Predictive food spoilage recommendations using gans
A GAN-based system to provide personalized food storage and handling recommendations, reducing spoilage risk by considering consumer demographics and storage conditions.
Overview
This innovative solution leverages Generative Adversarial Networks (GANs) to provide personalized food safety recommendations, aimed at minimizing food spoilage. By analyzing data on food contaminants, consumer demographics, and household storage environments, the system generates tailored visualizations of spoilage risks. This approach enhances consumer awareness and promotes safer food handling practices, thereby reducing the risk of food spoilage and waste.
Technical specifications
Key features:
- Utilizes GANs to generate synthetic data mimicking real contaminated samples, enhancing dataset size and diversity.
- Generates personalized visualizations of food spoilage risk by considering individual factors such as storage conditions and consumer demographics.
- Collects comprehensive data on spoilage rates, visual indicators, consumer demographics, and typical home storage conditions.
Technology readiness level
Currently, this technology is at TRL 2, indicating that the concept and application of GANs for food safety recommendations have been formulated and initial studies are underway.
About SRM Institute of Science and Technology
SRM Institute of Science and Technology is a comprehensive, private deemed‑to‑be university serving 50,000+ students across multiple campuses, anchored at Kattankulathur in the Chennai region. Industry links are enabled by the Sir C.V. Raman Research Park, which consolidates high‑end instrumentation, and by shared core facilities. Integration with SRM Medical College Hospital & Research Centre (1,500+ beds) supports clinical studies; a BIRAC‑supported BioNEST and the SRM Innovation & Incubation Centre provide startup pathways. Research is backed by competitive national funding from agencies such as DST, DBT, ICMR, CSIR, and DRDO. A dedicated technology transfer office and an IPR/startup policy streamline IP management, licensing, and spin‑out formation.
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