Nutritional quality is read differently by every label.
Front-of-pack nutrition labels were built independently by different national bodies, on different evidence bases, for different food environments. The same yoghurt can read as a green-light food on one system, a mid-grade letter on another, and an ultra-processed item on a third. For someone trying to compare two foods on a shelf, the labels do not agree, and most consumers see only one of them at a time.
The problem compounds when the food is not a Western retail package. Existing rating tools were built around supermarket labels with full nutrient panels and recognizable brand names. They do not handle home-cooked South Asian dishes, regional staples, or foods that move through the supply chain without a barcode. NutriScan is a classifier that brings three already-public rating systems together over a database that includes those foods.
Public food composition databases.
The classifier draws from public food composition databases that publish nutrient-per-100g values for raw and prepared foods. The reference set spans Western retail packaging data, US public nutrient data, and Indian food composition tables, with global open-product coverage as a fallback. Specific dataset identifiers and the curated fallback set are held for direct conversations with collaborators.
Each food is looked up against the available sources, with conflicts resolved through a fixed precedence and a curated fallback database for foods that are not well-covered by any single source.
Three published rating systems, applied in parallel.
NutriScan does not introduce a new scoring method. It combines three peer-reviewed and publicly documented ones over a broader food database than any single tool currently covers:
- Nutri-Score (A to E). Front-of-pack nutrition grade developed by Sante Publique France. A is best, E is worst. The algorithm is published.
- UK FSA traffic-light indicators. Red, amber, and green markers per serving for fat, saturated fat, sugar, and salt. The thresholds are published by the UK Food Standards Agency.
- NOVA processing level (1 to 4). Classification of foods by degree of industrial processing, originally developed at the University of Sao Paulo.
For each food, NutriScan emits all three readings side by side. Where the three disagree, the user sees the disagreement rather than a forced consensus. The classifier's contribution is integration and coverage, not a new judgment about what a food is.
A classifier that works on dal as cleanly as on cereal.
Coverage of Indian and home-cooked foods is a deliberate scope choice. Most existing nutrition-rating tools were built around Western retail packaging, and they fall back to "unknown" or guess wrong on dishes that do not arrive in a box. NutriScan is built to close that gap by combining multiple food composition sources and a curated fallback set for common dishes that are otherwise poorly covered.
The tool is designed to be used at the point of decision: search a food by name, scan a barcode where one exists, and see a composite reading that names which system said what. Where a food is not covered by any source, the tool says so rather than guessing.
For the scientific community.
A multi-source view of nutritional quality is useful in three ways. For public-health researchers, it makes the disagreements between national labeling regimes legible at the food level rather than only in policy discussion. For nutrition educators working in mixed-cuisine populations, it provides a tool that does not silently fail on home-cooked dishes. For consumers, it puts three independent readings on the same screen, which is closer to what published evidence says about how food quality should be assessed.
The methods are all already published. NutriScan's contribution is integration, coverage, and a public interface that makes the comparison possible.
Where things stand.
In preparation. Working prototype under development with faculty collaborators at an international research university. Public release pending.