WWhat's On My Food
Graduate Capstone

Understand what's in your food.

Scan food products, analyze ingredients, and receive clear, source-grounded explanations when you need them.

Explore the App View the Research
Barcode ScanningIngredient AnalysisAI-Assisted Explanations
What's On My Food barcode scanner screen.
What's On My Food home screen with Daily Health Insight and Quick Access cards.
Additive and ingredient database screen.

The Problem

When a database is missing, so is your answer.

Many food-scanning apps depend on incomplete static databases. When a product is missing or awaiting review, shoppers are left without useful information at the moment of purchase.

Traditional approach

  1. 1Scan
  2. 2Search static database
  3. Missing or incomplete result

What's On My Food

  1. 1Scan product
  2. 2Check product data
  3. 3Read ingredient label (OCR)
  4. 4Apply analysis rules
  5. Generate source-grounded explanation

Features

Everything you need at the shelf.

Ten focused capabilities that turn a barcode into a decision you can trust.

Barcode Scanning

Fast barcode capture powered by CameraX + ML Kit for on-device scanning.

Ingredient OCR

Read printed ingredient labels directly from the package when data is missing.

Ingredient & Additive Analysis

Transparent, rule-based analysis of additives, sugars, sodium, oils, and allergens.

AI-Assisted ExplanationsAI

Plain-language explanations grounded in the evidence collected during the scan.

Scientific Sources

Every explanation links back to authoritative references and cited studies.

Personal Pantry

Save scanned products, tag favorites, and track what you keep at home.

Pantry Insights

Visualize patterns across your pantry: additives, sugar levels, allergen exposure.

Product Alternatives

See similar products with cleaner ingredient profiles or better nutrition.

Retailer Availability

Discover where alternatives are sold so you can act on the recommendation.

Multilingual Support

Full experience in English, Spanish, and French.

How It Works

Six steps from shelf to insight.

  1. 01

    Scan a product

    Use the barcode scanner or capture the ingredient label with your camera.

  2. 02

    Retrieve product data

    Look up available nutrition and product information from trusted databases.

  3. 03

    Analyze ingredients

    Apply transparent, deterministic rules for additives, sugars, sodium, and allergens.

  4. 04

    Explain with AI

    Generate a plain-language explanation grounded in the collected evidence.

  5. 05

    Review sources

    See references, limitations, and where the analysis is uncertain.

  6. 06

    Save or explore

    Add to your pantry or browse cleaner alternatives at nearby retailers.

Graduate Capstone Research

Improving customer satisfaction through AI-assisted food analysis.

Research Question

Do real-time AI-assisted food explanations produce higher user satisfaction and stronger recommendation intention than static database results?

Condition A

Static database results

Condition B

Real-time AI-assisted explanations

Both conditions are grounded in the same available product evidence, allowing a direct within-participant comparison.

Study Status

[Planning]

Update this field as the project moves through IRB review, recruiting, data collection, and completion.

This application provides general food-label information and is not a medical diagnostic tool.

The study examines:

SatisfactionUsefulnessCompletenessTrustClarityDecision confidenceRecommendation intentionObjective accuracy

Development Process

From problem discovery to publication.

  1. Problem discovery

    Complete

  2. Competitor research

    Complete

  3. Prototype development

    Complete

  4. Barcode & OCR implementation

    Complete

  5. Ingredient-analysis rules

    In Progress

  6. AI integration

    In Progress

  7. Pilot study

    Planned

  8. Committee feedback

    Planned

  9. IRB preparation

    Planned

  10. Final user study

    Planned

  11. Results & future improvements

    Planned

Technology

Built on tools chosen for transparency and trust.

Mobile

  • Java
  • Android Studio
  • AndroidX
  • CameraX
  • Google ML Kit
  • Room Database
  • Retrofit + OkHttp
  • Firebase Authentication

Backend

  • Node.js
  • REST APIs
  • Secure AI service
  • Google Gemini
  • Rate limiting & validation

Data & Integrations

  • Open Food Facts
  • Food product APIs
  • Ingredient & additive databases
  • Retailer integrations

Research

  • Google Forms
  • Quantitative surveys
  • Within-participant comparison
  • Statistical analysis

Design & Safety Principles

Responsible by design.

Source transparency

Every claim links to a citable source.

Plain-language explanations

Written for real shoppers, not experts.

Rules before generation

Deterministic analysis runs before any AI output.

Uncertainty communicated

Missing data and limits are shown, not hidden.

Privacy-conscious

Minimal, purposeful data collection.

Guardrails against misleading AI

Grounded responses with refusal paths.

Accessible & multilingual

English, Spanish, French — with accessibility in mind.

Not a medical tool

General food-label information only.

CB

Developer

Caleb Barranco

Mobile Application Developer & Graduate Researcher · Full Sail University, M.S. Computer Science · Florida

I build mobile experiences that put clear, trustworthy information in people's hands. What's On My Food is my capstone project — combining Android, computer vision, and AI to help everyday shoppers understand what's actually in the food they buy.

GitHub LinkedIn ciblorenzo@gmail.com(954) 588-2370

Contact

Have a question or opportunity?

I'd love to hear from employers, collaborators, and academic reviewers.

ciblorenzo@gmail.com(954) 588-2370 LinkedInFlorida, USA