MFP — Mathematical Flavor Profile Engine

> Deterministic, auditable flavor intelligence in 20 dimensions


Overview

MFP (Mathematical Flavor Profile) is a computational engine for analyzing and optimizing recipes using a 20-dimensional flavor space. It provides deterministic, auditable scoring of dishes against cuisine-specific targets, along with actionable recommendations for improvement.

Features

- 20-dimensional flavor space — UMAMI, SALT, SWEET, SOUR, BITTER, HEAT_PEPPER, WARM_SPICE, SMOKE, ROASTED, FAT_RICH, CREAMY, HERBAL, CITRUS, ALLIUM, FERMENT, EARTHY, NUTTY, FLORAL, TEXTURE_CRISP, TEXTURE_TENDER
- Role-based normalization — PRIMARY / SUPPORT / FINISH categories with dish-type-specific constants
- Method kernels — 12 cooking methods with heat/volatility transforms (GRILL, ROAST, SAUTE, etc.)
- Scoring system — Similarity, balance, structural coverage, clash penalty with weighted final formula
- Recommendations — Add-ins, fixes, substitutions, method adjustments
- Cultural libraries — AA Foodways (Midwest + Gulf States), with more to come

Packages

| Package | Description |
|---------|-------------|
| @studio46/mfp-core | Types, math primitives, and scoring algorithms |
| @studio46/mfp-data | Ingredient libraries and cuisine style targets |
| @studio46/mfp-engine | Complete MFP engine with recommendations |

Installation

``bash
npm install @studio46/mfp-engine
`

Or install individual packages:

`bash
npm install @studio46/mfp-core @studio46/mfp-data
`

Quick Start

`typescript
import {
runMFPEngine,
getIngredient,
CookingMethod,
DishType,
} from '@studio46/mfp-engine';

const result = runMFPEngine({
ingredients: [
{ card: getIngredient("chicken_breast"), quantity: 200 },
{ card: getIngredient("garlic"), quantity: 10 },
{ card: getIngredient("olive_oil"), quantity: 30 },
{ card: getIngredient("lemon_juice"), quantity: 15 },
{ card: getIngredient("basil"), quantity: 8 },
],
method: CookingMethod.SAUTE,
heatLevel: 0.7,
dishType: DishType.COMPLETE_PLATE,
styleTargetId: "italian",
});

console.log("Final Score:", result.scored.score.toFixed(2));
console.log("Components:", result.scored.components);
console.log("Top Recommendations:", result.recommendations.slice(0, 3));
`

Style Targets

| ID | Name | Characteristics |
|----|------|-----------------|
| italian | Italian (Classic) | Umami, herbal, allium, fat-rich |
| japanese | Japanese (Washoku) | Umami, ferment, delicate |
| mexican | Mexican (Traditional) | Heat, citrus, earthy |
| thai | Thai | Sweet-sour-heat balance |
| french | French (Classic) | Fat, cream, roasted |
| indian | Indian (North) | Warm spice, heat, allium |
| bbq | American BBQ | Smoke, sweet, fat, tender |
| mediterranean | Mediterranean | Herbal, citrus, olive oil |
| soul_food | Soul Food (AA Core) | Smoke, bitter greens, fat, tender |
| gulf_creole | Gulf Creole (Louisiana) | Heat, roux, seafood, allium |

Scoring Formula

`
Score = 0.45×S_sim + 0.35×S_bal + 0.20×S_struct - 0.40×P_clash

Where:
S_sim = cosine similarity to target profile
S_bal = weighted balance score
S_struct = structural role coverage
P_clash = ingredient class clash penalty
`

Calibration Status

Ingredient vectors have calibration status indicators:

| Status | Meaning | Count |
|--------|---------|-------|
| A | Sensory-validated (blind tasting) | 0 |
| B | Database-grounded | ~37 |
| C | Prior estimate (needs calibration) | ~100 |

Documentation

- Specification (MFP v1.1)
- API Reference
- Calibration Guide
- Contributing

Development

`bash


Install dependencies

pnpm install

Build all packages

pnpm build

Run tests

pnpm test

Type check

pnpm typecheck
``

License

MIT © Studio46-Go