Navigating Regulatory Complexity to Provide Accurate Nutritional Indicators

The Challenge: Calculating NutriScore at Scale

Calculating health scores for a multi-category portfolio of 782 products is a substantial undertaking when the underlying data is maintained in inconsistent formats and fragmented systems. Beyond resolving these technical gaps, Conagra also required a Nutri-Score classification to benchmark product performance against existing Health Star Rating (HSR) metrics, providing a comparative view of how their portfolio translates across different international nutritional frameworks.

Complexity was compounded by the transition between 2019 and 2023 Nutri-Score updates. In the absence of a consolidated official policy, interpreting shifting Fruit, Vegetable, and Legume (FVL) criteria for brands like Birds Eye and Gardein created significant technical friction. Because FVL thresholds vary by food category, a “one-size-fits-all” approach was impossible, requiring a nuanced categorisation strategy to maintain regulatory integrity.

The Solution: Automated Logic Guided by Expert Validation

Spoon Guru deployed a scalable framework to ingest and standardise Conagra’s product data, applying the Nutri-Score algorithm through an automated digital architecture. 

Recognising that systems require context for culinary nuance, the process integrated “human-in-the-loop” expertise to ensure absolute precision. Nutritionists conducted manual audits and developed a specialised “FVNL oils” classification, ensuring the resulting scores were scientifically robust and accurately reflected complex ingredient profiles.

The Application: Enabling Precise Choices at the Digital Shelf

Spoon Guru deployed a multi-layered approach to standardise Conagra’s product data, beginning with the precise categorisation of the portfolio into appropriate Nutri-Score food groups. We delivered manual Nutri-Score calculations using the updated 2023 methodology, leveraging our internal systems to estimate Fruit, Vegetable, and Legume (FVL) content with high accuracy. 

To support this, we expanded our smart ingredient library with new mappings and developed a bespoke “FVNL oils” category to satisfy specific Nutri-Score group requirements. By identifying and resolving deep-seated data inconsistencies and formatting issues, the project not only ensured immediate accuracy but also generated critical system learnings to enhance future automation and large-scale data ingestion.

Bridging the Gap: Technical Rigour for Global Health Standards

By resolving complex data fragmentation and aligning dual nutritional frameworks through expert validation, this partnership transforms raw product specifications into a high-integrity data asset that secures regulatory alignment and strengthens Conagra’s competitive positioning across the global retail landscape.