Unlocking Value: Decision Support Systems and Digital Platforms for Side Streams and Upcycled Ingredients.  

In the move toward a circular and autonomous food system, smarter use of side streams is key. The SIDEAID project, led by Wageningen Food Biobased Research, is developing AI-based decision support to help industry players make data-driven valorisation choices. During a recent online Acceleration Session with about 30 partners of The Upcycling Community, researchers, innovators and platform providers shared progress, insights, and needs for the next phase of digital transformation in upcycling. 

1. SIDEAID Project Update  

By Martijntje Vollebregt, Wageningen University & Research 

The SIDEAID project (AI-based Decision Support for Side Stream Valorization) is building a general AI-driven framework that supports the valorization of food processing side streams and the application of natural compounds across sectors. By leveraging artificial intelligence and a structured knowledge graph, the project connects key elements, such as side stream components, their functional properties, relevant processing technologies, and potential applications, into a unified decision-making tool. 

This approach enables companies in the consortium of SIDEAID to navigate complex data landscapes and uncover opportunities for sustainable innovation. A crucial part of the framework is ensuring data quality: SIDEAID integrates expert-curated data sources to guarantee reliability and relevance, making the insights both actionable and trustworthy. 

Martijntje Vollebregt: “SIDEAID is about bringing together different kinds of data to uncover real opportunities for using side streams for different business of the value chain. Working with The Upcycling Community helps us create tools that actually matter to businesses, making it easier to choose sustainable and smart ways to reuse ingredients.” 

2. Demo: Digital Matchmaking in Practice 

By Christian van Maaren, Excess Materials Exchange (EME) 

Christian van Maaren showcased how the Excess Materials Exchange (EME) platform connects supply and demand across industries through smart data and AI. A central component of EME’s approach is the “knowledge graph”, which maps how different nodes in a dataset are related. Once these connections are clear, information can easily be syndicated and shared securely. When the data in a digital product passport matches a buyer’s requirements, a transactional match is automatically created. To build trust and mitigate risk, the system also offers insurance coverage for financial and logistical uncertainties.  

Our Upcycling Community partners highlighted the importance of the ability to react swiftly to changes in the market and environment. AI can provide matchmaking capability.  Companies who want to implement AI to facilitate side stream valorisation need to have a good vision and good return of investment since the applicability of AI for side stream valorisation needs financial investments.  

3. Insights from Interviews and Brainstorm with Upcycling Community  

By Marta Rodriguez-Illera, Wageningen University & Research 

Both the interviews and the plenary brainstorm showed that, despite differing motivations and company profiles, organisations across the upcycling value chain face similar operational bottlenecks in valorizing side streams. Participants expressed a shared ambition to make better use of data, but noted that fragmented information, inconsistent quality, and lack of interoperability still hinder progress. At the same time, there was a clear readiness to engage with new tools, especially those offering data-driven matchmaking and traceable validation that can simplify compliance and technical evaluation rather than add extra complexity. 

The brainstorm session reflected this diversity in digital maturity: some participants rated their organization’s use of data tools as modest, while others were already working with own datasets and predictive models or external scientific datasets . Despite these differences, the conversation converged on several shared priorities: 

• The need for a trusted and secure data environment, where organisations retain control over what information they share. 

• The importance of hybrid intelligence, combining AI-driven insights with human expertise to ensure reliability. 

• The call for economic clarity, demonstrating tangible business value and cost savings as a key driver for adoption. 

• Interest in open, collaborative systems that democratize access to data and allow the best possible reuse and integration of available resources. 

Looking ahead, participants saw the SIDEAID project and its future extensions as a potential bridge between these needs. By integrating technical, sustainability, and market dimensions into one accessible digital environment, SIDEAID could connect fragmented knowledge and help accelerate the transition toward a more circular and data-driven food economy. 

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