<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/5FAFC93E-AEBB-44F2-89CA-309DA7672FB3" ns1:id="5FAFC93E-AEBB-44F2-89CA-309DA7672FB3"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/4183F69A-809A-44EC-8DA0-47D3927BC118" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/98E67DD5-CABE-4E58-84AF-F3944492AF7B" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/98E67DD5-CABE-4E58-84AF-F3944492AF7B" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2024-11-30T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/CAFF878D-DD2B-4AEB-982E-EAB6C8464E00" ns1:rel="FUND" ns1:start="2023-12-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10097216</ns2:identifier></ns2:identifiers><ns2:title>Reducing Consumer Food Waste and Carbon Emissions with Embedded Machine Learning</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Investment Accelerator</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Our planet is grappling with an alarming food waste issue. Astonishingly, a third of all food produced is never consumed. This not only signifies economic losses but also translates into a significant environmental impact in terms of wasted resources and greenhouse gas emissions. Households are among the major contributors to this problem.

Enter Nosh Technologies. Born from a personal journey and driven by a passion for sustainability, our mission has been to harness the power of technology to tackle this global issue. At the heart of our initiative is the Nosh app, which employs embedded machine learning to help households minimise food wastage.

**Objective**:

Our project, &amp;quot;Reducing Consumer Food Waste and Carbon Emissions with Embedded Machine Learning&amp;quot;, aims to take the Nosh app to the next echelon. We plan to further refine our AI algorithms to offer even more personalized and precise insights to users. By analyzing users' consumption patterns, purchase habits, and even regional seasonal produce availability, the app will provide recommendations tailored to individual households. The ultimate goal? Ensuring that food ends up on plates, not in bins.

**Features**:

* Embedded Machine Learning: Integrate state-of-the-art ML models that run locally on user devices. This not only guarantees user data privacy but also allows for faster, real-time insights without relying on internet connectivity.
* Personalised Insights: Provide users with actionable recommendations like when to consume certain items, alternative recipes to use nearly-expiring ingredients, and optimal grocery buying quantities.
* Feedback Loop: Allow users to provide feedback on recommendations, creating a system that continuously learns and adapts to changing habits.

**Community Engagement**:

Recognising that change is a collective effort, part of our project is also to engage communities. Through the Nosh app, we'll foster a space where users can share their success stories, tips, and even recipes that align with the goal of reducing waste.

**Impact**:

The enhanced Nosh app will not just be a tool, but a movement. We envision a future where households are empowered with the knowledge to make informed decisions, reducing their carbon footprint and promoting sustainability. By leveraging the prowess of machine learning, we hope to pave the way for smarter consumption patterns, one household at a time.

By supporting this project, we're not just innovating an app, we're championing a sustainable future for all.</ns2:abstractText></ns2:project>