The Accuracy Promise

Eatomate combines database matching (text search across 2M+ barcodes, 50K+ recipes, and 100K+ aliases) + precise weighing (kitchen scale for exact portions) to achieve:

•Week 1: ~85% nutrition accuracy (physics-based reconciliation provides strong baseline)
•Week 4: ~95% nutrition accuracy (system learned your recipes and portions)
•Meal identity: 100% accuracy with user-confirmed database match

What "~95% Accuracy" Means

95% nutrition accuracy means your daily calorie, protein, carb, and fat totals are within ±5% of ground truth. For a 2000 calorie diet, that's ±100 calories—more accurate than food labels (legally allowed ±20% variance).

The Two-Stage System

Every meal log involves two stages that combine for highly accurate nutrition tracking:

Stage 1: Database Matching (95% Automated)

You search for your food item and the system matches across three databases:

•Barcode Database: 2M+ barcoded products for instant lookup
•Recipe Database: 50K+ recipes with full nutrition data
•Alternative Names: 100K+ aliases ("dal" = "yellow lentil dal")

Output: Top few candidates ranked by relevance for you to select.

Stage 2: Precise Weighing

You weigh the food on your kitchen scale:

•Exact weight: Kitchen scale measures to the gram
•Per-gram nutrition: Precise calories and macros from database
•No estimation: Real weight eliminates portion guessing entirely

Output: Exact nutrition data based on actual weight.

Combined Result: Highly Accurate Nutrition

You select the correct food from the top candidates (100% meal identity) and enter the exact weight from your scale (precise portions). When the system has multiple similar matches (e.g., semi-skimmed vs whole milk), you pick the right one.

Accuracy Improvement Timeline

Week 1: ~85% Accuracy

Physics-based reconciliation provides strong baseline even with limited data. System uses:

•Mass conservation (pantry in = meals out + waste)
•Standard recipe ratios from 100K+ database
•Population-level portion sizes (adjusted for BMR/TDEE)

Variance: ±15% on individual meals, but Central Limit Theorem reduces daily totals to ~±5-7%.

Week 4: ~95% Accuracy

System learned YOUR specific recipes and portions through reconciliation:

✓Recipe-specific ingredient ratios (your spaghetti vs database spaghetti)
✓Personal portion calibrations (4-layer: Global, Food-type, User, Recipe)
✓Cooking method adjustments (your "fried" vs standard "fried")

Variance: ±5% on individual meals, ±3% on daily totals (research-grade accuracy).

What Affects Accuracy

Positive Factors (Higher Accuracy)

✓Complete pantry tracking: Scan all receipts
✓Consistent cooking: Repeating recipes teaches system faster
✓Home cooking: Reconciliation works best with pantry-based meals
✓Accurate weighing: Kitchen scale for precise gram measurements

Negative Factors (Lower Accuracy)

✗Restaurant meals: ~70% if weighed, ~50% if Mistral estimates (no pantry reconciliation)
✗Missing pantry data: Predictions degrade without ingredient knowledge
✗Extreme recipe variation: Cooking same dish very differently each time
✗Estimated portions: Not using a kitchen scale reduces accuracy

Meal Identity vs Nutrition Accuracy

These are different metrics:

Meal Identity (100% Accurate)

Did the system correctly identify "spaghetti bolognese" vs "carbonara"? Meal identity is 100% because you select it (90% single-tap from predictions, 9% search, 1% weigh).

Nutrition Accuracy (~95%)

Is the calorie/macro estimate within ±5% of ground truth? Requires correct portions, ingredient ratios, and recipe learning.

Example: Week 1 vs Week 4

Meal: Your homemade spaghetti bolognese

Week 1:

•Meal identity: ✓ Correct (you selected it)
•Nutrition: 750 kcal estimated, 700 kcal actual (±7% error)
•Why: Used standard bolognese ratios, not YOUR recipe

Week 4:

✓Meal identity: ✓ Correct (single-tap from predictions)
✓Nutrition: 705 kcal estimated, 700 kcal actual (±0.7% error)
✓Why: Learned you use 20% more beef, 10% less pasta than standard recipe

Confidence Intervals

All nutrition estimates include statistical confidence intervals using Gaussian error propagation:

Week 1:250g ± 37.5g pasta (±15% variance)

Week 4:250g ± 12.5g pasta (±5% variance)

Central Limit Theorem (σ/√N) means daily totals have tighter bounds than individual meals.

How to Improve Your Accuracy

1.Scan all grocery receipts — Complete pantry data = better predictions
2.Correct predictions when wrong — Teaches system your recipes faster
3.Weigh accurately — Follow meal logging best practices
4.Log meals consistently — More data = more learning
5.Wait for reconciliation — Physics model corrects historical errors automatically

Next Steps

→Learn meal logging best practices for accurate tracking
→Understand physics-based reconciliation

Understanding Accuracy