> For the complete documentation index, see [llms.txt](https://tomatopy.pizza/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://tomatopy.pizza/docs/tutorials/advanced-flavor-profiling.md).

# Advanced Flavor Profiling

This tutorial covers advanced techniques for analyzing and comparing flavor profiles using the PizzaStack taste endpoints.

## Prerequisites

Before starting, make sure you have:

* A PizzaStack API key
* Python with the `requests` library installed
* Some dishes already created in a session (see [Making Your First Sauce](/docs/tutorials/making-your-first-marinara.md))

## Setup

```python
import requests
import uuid

API_BASE = "https://api.tomatopy.pizza/v1"
HEADERS = {
    "Content-Type": "application/json",
    "X-API-Key": "your-api-key",
    "X-Session-ID": str(uuid.uuid4())
}

def api_post(endpoint, payload):
    """Helper to make API calls."""
    response = requests.post(f"{API_BASE}{endpoint}", headers=HEADERS, json=payload)
    response.raise_for_status()
    return response.json()
```

## Step 1: Create Test Ingredients

Let's create several tomato varieties to compare:

```python
# Acquire different tomato varieties
varieties = [
    {"variety": "San Marzano", "ripeness": 0.9, "weight": 150},
    {"variety": "Roma", "ripeness": 0.85, "weight": 120},
    {"variety": "Cherry", "ripeness": 0.8, "weight": 30}
]

tomatoes = []
for v in varieties:
    tomato = api_post("/tomato/acquire", v)
    tomatoes.append(tomato)
    print(f"Acquired {tomato['variety']} (type: {tomato['type']})")
```

## Step 2: Basic Flavor Analysis

Analyze a single ingredient with comprehensive depth:

```python
# Comprehensive analysis of a San Marzano tomato
profile = api_post("/taste/analyze", {
    "dish_id": tomatoes[0]["id"],
    "depth": "comprehensive",
    "include_aroma": True,
    "include_texture": True
})

print(f"Sweetness: {profile['sweetness']}")
print(f"Acidity:   {profile['acidity']}")
print(f"Umami:     {profile['umami']}")
print(f"Aroma intensity: {profile['aroma']['intensity']}")
print(f"Aroma notes: {profile['aroma']['notes']}")
print(f"Texture score: {profile['texture']['score']}")
print(f"Firmness: {profile['texture']['firmness']}")
```

## Step 3: Compare Tomato Varieties

Use `/taste/compare` to see how different varieties stack up:

```python
# Compare all three varieties
tomato_ids = [t["id"] for t in tomatoes]

comparison = api_post("/taste/compare", {
    "dish_ids": tomato_ids,
    "metrics": ["sweetness", "acidity", "umami", "aroma"]
})

# Print results for each tomato
for result in comparison["results"]:
    print(f"\n{result['id']}:")
    print(f"  Sweetness: {result['sweetness']}")
    print(f"  Acidity:   {result['acidity']}")
    print(f"  Umami:     {result['umami']}")
    print(f"  Aroma:     {result['aroma']}")

# Print rankings
print(f"\nSweetest: {comparison['rankings']['sweetness'][0]}")
print(f"Most acidic: {comparison['rankings']['acidity'][0]}")
print(f"Most umami: {comparison['rankings']['umami'][0]}")
```

## Step 4: Track Flavor Through the Pipeline

Analyze how flavors change from raw tomato to finished sauce:

```python
# Create a sauce from one of the tomatoes
sliced = api_post("/tomato/slice", {
    "tomato_ids": [tomatoes[0]["id"]],
    "method": "dice",
    "size": "medium",
    "consistency": "uniform"
})

sauce = api_post("/cook/simmer", {
    "ingredients": [
        {"id": sliced["id"], "name": "tomato", "amount": 150, "unit": "g"}
    ],
    "temperature": 100,
    "duration": "30m"
})

# Analyze at each stage
raw_profile = api_post("/taste/analyze", {
    "dish_id": tomatoes[0]["id"],
    "depth": "comprehensive",
    "include_aroma": True
})

sauce_profile = api_post("/taste/analyze", {
    "dish_id": sauce["id"],
    "depth": "comprehensive",
    "include_aroma": True
})

print("Raw tomato vs Sauce:")
print(f"  Sweetness: {raw_profile['sweetness']} -> {sauce_profile['sweetness']}")
print(f"  Acidity:   {raw_profile['acidity']} -> {sauce_profile['acidity']}")
print(f"  Umami:     {raw_profile['umami']} -> {sauce_profile['umami']}")
print(f"  Aroma:     {raw_profile['aroma']['intensity']} -> {sauce_profile['aroma']['intensity']}")
```

## Step 5: Compare Raw vs Cooked

Use the compare endpoint to see the changes side by side:

```python
# Direct comparison of raw tomato and sauce
comparison = api_post("/taste/compare", {
    "dish_ids": [tomatoes[0]["id"], sauce["id"]],
    "metrics": ["sweetness", "acidity", "umami"]
})

for result in comparison["results"]:
    print(f"{result['id']}:")
    print(f"  Sweetness: {result['sweetness']}")
    print(f"  Acidity:   {result['acidity']}")
    print(f"  Umami:     {result['umami']}")
```

## Step 6: Analyze a Finished Pizza

Make a complete pizza and analyze it:

```python
# Complete the pizza
base = api_post("/pizza/base", {
    "thickness": "medium",
    "size": "12inch",
    "style": "neapolitan",
    "hydration": 0.65
})

pizza = api_post("/pizza/assemble", {
    "base_id": base["id"],
    "sauce_id": sauce["id"],
    "toppings": [
        {"name": "mozzarella", "amount": 200, "unit": "g"},
        {"name": "basil", "amount": 10, "unit": "leaves"}
    ]
})

baked = api_post("/pizza/bake", {
    "pizza_id": pizza["id"],
    "temperature": 450,
    "duration": "2m"
})

# Full analysis of the finished pizza
pizza_profile = api_post("/taste/analyze", {
    "dish_id": baked["id"],
    "depth": "comprehensive",
    "include_aroma": True,
    "include_texture": True
})

print(f"\n--- Finished Pizza Profile ---")
print(f"Quality score: {baked['quality_score']}")
print(f"Sweetness: {pizza_profile['sweetness']}")
print(f"Acidity:   {pizza_profile['acidity']}")
print(f"Umami:     {pizza_profile['umami']}")
print(f"Aroma:     {pizza_profile['aroma']['intensity']}")
print(f"Texture:   {pizza_profile['texture']['score']}")
```

## Step 7: Compare Multiple Finished Dishes

Compare sauce vs finished pizza to see how baking affects the flavor:

```python
# Compare sauce to baked pizza
comparison = api_post("/taste/compare", {
    "dish_ids": [sauce["id"], baked["id"]],
    "metrics": ["sweetness", "acidity", "umami", "aroma", "texture"]
})

print("\nSauce vs Baked Pizza:")
for result in comparison["results"]:
    print(f"\n{result['id']}:")
    for metric in ["sweetness", "acidity", "umami", "aroma", "texture"]:
        print(f"  {metric}: {result[metric]}")
```

## Best Practices

1. **Use Comprehensive Depth for Final Products**

   For intermediate ingredients, `"basic"` is usually sufficient. Save `"comprehensive"` with aroma and texture for finished dishes.
2. **Compare at the Same Pipeline Stage**

   Comparing a raw tomato to a baked pizza is possible but the results are more meaningful when comparing items at the same stage (e.g., two different sauces).
3. **Check Response Quality Fields**

   ```python
   profile = api_post("/taste/analyze", {
       "dish_id": dish_id,
       "depth": "comprehensive"
   })
   if profile.get("sweetness", 0) < 0.3:
       print("Low sweetness - check ingredient ripeness")
   ```

## Next Steps

* [API Usage Best Practices](/docs/best-practices/code-efficiency.md) - Optimize your API usage
* [Session Management](/docs/best-practices/virtual-kitchen-management.md) - Manage sessions effectively
* [Core API Endpoints](/docs/core-modules.md) - Explore all endpoints


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