SIH 2026 · PS 26236 · Food packaging decision support
Every food breathes, sweats or spoils differently. Its packaging should know.
FoodPack AI looks at your food and the conditions it will be stored and moved in, then recommends packaging materials — using measured properties from published sources, and showing the reasoning behind every answer.
- O₂Oxygen
- CO₂Carbon dioxide
- H₂OWater vapour
Live illustration: oxygen, carbon dioxide and water vapour meeting a packaging film. Not a measurement.
Fresh produce keeps breathing
After harvest, fruit and vegetables still take in oxygen and give off carbon dioxide. Seal them in a film that is too tight and the pack runs out of oxygen; too open, and they dry out and age.
Dry foods fight moisture
Powders, snacks and spices spoil when water vapour gets in. For them the film has to hold water vapour back for the whole shelf life.
The answer is a window, not a maximum
For respiring produce the film must let enough oxygen in and enough carbon dioxide out, but not too much. FoodPack AI computes that window from published respiration data, then finds the films whose measured properties fall inside it.
Too tight: the pack goes anaerobic
Drag the film to see where it lands. Illustration only: real windows are computed per food in the app.
api.foodpackai.site
Try the engine, live
Pick a food. This runs the real engine on the published storage conditions for that food and shows its top packaging candidates.
Live result from the FoodPack engine. Pack mass and film area are the example values shown; in the app you set your own.
Anatomy of a flexible pack
Most flexible packs are laminates, and each layer does one job. FoodPack AI reasons about the barrier each job needs.
- Outer layer: print and abrasion resistance
- Barrier layer: holds back oxygen and water vapour
- Sealant layer: makes the heat seal and touches the food
Illustration of a typical structure, not a recommendation.
How it works
- 01
Food and conditions
Tell us about the food
- 02
Scientific requirements
We work out what the package must protect it from
- 03
Material screening
We compare materials that can do it
- 04
Evidence-backed recommendation
We explain why this one fits
Where is the AI in this?
The machine-learning model predicts a polymer's oxygen and carbon-dioxide permeability from its chemical structure. It was trained on published measurements for several hundred polymers and tested on polymers it never saw, where it clearly beats a baseline that ignores the chemistry. The product uses it to screen films that are not in the database and to cross-check sourced values, always labelled as a model prediction with its range. It never overrides a sourced value and never chooses the packaging.
See the model and its test results →Limitations and scientific disclaimer
This is a decision-support tool, not a certification or food-safety compliance service. Film properties are used at the temperature they were measured at and are not temperature-corrected. Scores express relative engineering suitability under stated conventions; they are not a validated prediction of shelf life.