Chapter 1: Fundamentals of Food Processing and AI

# Requirements:
# - Python 3.9+
# - matplotlib>=3.7.0
# - seaborn>=0.12.0

"""
Example: Key Features of Food Processing

Purpose: Demonstrate data visualization techniques
Target: Intermediate
Execution time: 5-15 seconds
Dependencies: None
"""

            <div class="code-header">📊 Code Example 1: Simulation of Raw Material Quality Variation</div>
            <pre><code class="language-python">import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Simulation of seasonal variation in agricultural products
np.random.seed(42)
months = np.arange(1, 13)
seasons = ['Winter', 'Winter', 'Spring', 'Spring', 'Spring', 'Summer',
           'Summer', 'Summer', 'Fall', 'Fall', 'Fall', 'Winter']

# Seasonal variation in sugar content (high in summer, low in winter)
sugar_content = 12 + 3*np.sin(2*np.pi*(months-3)/12) + np.random.normal(0, 0.5, 12)

# Seasonal variation in moisture content
moisture_content = 85 - 5*np.sin(2*np.pi*(months-6)/12) + np.random.normal(0, 1, 12)

# Visualization
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))

# Sugar content plot
ax1.plot(months, sugar_content, marker='o', linewidth=2, color='#11998e', label='Sugar Content (Brix)')
ax1.axhline(y=12, color='gray', linestyle='--', alpha=0.5, label='Annual Average')
ax1.fill_between(months, sugar_content - 1, sugar_content + 1, alpha=0.2, color='#11998e')
ax1.set_xlabel('Month', fontsize=12)
ax1.set_ylabel('Sugar Content (°Brix)', fontsize=12)
ax1.set_title('Seasonal Variation in Sugar Content of Agricultural Products', fontsize=14, fontweight='bold')
ax1.grid(True, alpha=0.3)
ax1.legend()
ax1.set_xticks(months)

# Moisture content plot
ax2.plot(months, moisture_content, marker='s', linewidth=2, color='#38ef7d', label='Moisture Content (%)')
ax2.axhline(y=85, color='gray', linestyle='--', alpha=0.5, label='Annual Average')
ax2.fill_between(months, moisture_content - 2, moisture_content + 2, alpha=0.2, color='#38ef7d')
ax2.set_xlabel('Month', fontsize=12)
ax2.set_ylabel('Moisture Content (%)', fontsize=12)
ax2.set_title('Seasonal Variation in Moisture Content of Agricultural Products', fontsize=14, fontweight='bold')
ax2.grid(True, alpha=0.3)
ax2.legend()
ax2.set_xticks(months)

plt.tight_layout()
plt.savefig('seasonal_variation.png', dpi=300, bbox_inches='tight')
plt.show()

print("=== Seasonal Variation Statistics ===")
print(f"Sugar Content: Mean {sugar_content.mean():.2f}°Brix, Std Dev {sugar_content.std():.2f}°Brix")
print(f"Moisture Content: Mean {moisture_content.mean():.2f}%, Std Dev {moisture_content.std():.2f}%")
print(f"Coefficient of Variation: Sugar Content {(sugar_content.std()/sugar_content.mean()*100):.2f}%")

# Requirements:
# - Python 3.9+
# - matplotlib>=3.7.0

"""
Example: Key AI Application Areas

Purpose: Demonstrate data visualization techniques
Target: Advanced
Execution time: 30-60 seconds
Dependencies: None
"""

            <div class="code-header">📊 Code Example 2: Building a Quality Prediction Model (Raw Materials → Final Product Quality)</div>
            <pre><code class="language-python">import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt

# Generate food manufacturing data (raw material properties → final product quality)
np.random.seed(42)
n_samples = 200

# Raw material properties
data = pd.DataFrame({
    'Sugar_Brix': np.random.uniform(10, 15, n_samples),
    'Moisture_%': np.random.uniform(80, 90, n_samples),
    'Acidity_pH': np.random.uniform(3.0, 4.5, n_samples),
    'Heating_Temp_C': np.random.uniform(85, 95, n_samples),
    'Heating_Time_min': np.random.uniform(10, 30, n_samples),
})

# Final product quality (flavor score: complex nonlinear relationship)
data['Flavor_Score'] = (
    5 * data['Sugar_Brix'] +
    0.5 * data['Moisture_%'] -
    10 * (data['Acidity_pH'] - 3.5)**2 +
    0.3 * data['Heating_Temp_C'] -
    0.1 * data['Heating_Time_min']**2 +
    np.random.normal(0, 5, n_samples)
)

# Data split
X = data.drop('Flavor_Score', axis=1)
y = data['Flavor_Score']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Build Random Forest model
model = RandomForestRegressor(n_estimators=100, max_depth=10, random_state=42)
model.fit(X_train, y_train)

# Prediction and evaluation
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')

print("=== Quality Prediction Model Performance ===")
print(f"R² Score: {r2:.4f}")
print(f"RMSE: {np.sqrt(mse):.4f}")
print(f"Cross-Validation R² (Mean ± Std Dev): {cv_scores.mean():.4f} ± {cv_scores.std():.4f}")

# Feature importance
feature_importance = pd.DataFrame({
    'Feature': X.columns,
    'Importance': model.feature_importances_
}).sort_values('Importance', ascending=False)

print("\n=== Feature Importance ===")
print(feature_importance.to_string(index=False))

# Plot predicted vs actual values
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))

# Predicted vs actual
ax1.scatter(y_test, y_pred, alpha=0.6, s=50, color='#11998e')
ax1.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()],
         'r--', lw=2, label='Ideal Line')
ax1.set_xlabel('Actual Value', fontsize=12)
ax1.set_ylabel('Predicted Value', fontsize=12)
ax1.set_title(f'Quality Prediction Model (R²={r2:.4f})', fontsize=14, fontweight='bold')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Feature importance
ax2.barh(feature_importance['Feature'], feature_importance['Importance'], color='#38ef7d')
ax2.set_xlabel('Importance', fontsize=12)
ax2.set_title('Feature Importance Ranking', fontsize=14, fontweight='bold')
ax2.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('quality_prediction_model.png', dpi=300, bbox_inches='tight')
plt.show()

# Requirements:
# - Python 3.9+
# - matplotlib>=3.7.0

"""
Example: 🔍 HACCP 7 Principles

Purpose: Demonstrate data visualization techniques
Target: Beginner to Intermediate
Execution time: 5-15 seconds
Dependencies: None
"""

            <div class="code-header">📊 Code Example 3: HACCP Temperature Monitoring System Simulation</div>
            <pre><code class="language-python">import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

# Temperature monitoring simulation for heat sterilization process
np.random.seed(42)
time_points = 100
time = np.arange(time_points)

# Temperature profile (target: 85°C, critical limit: 83-87°C)
target_temp = 85
temp_profile = target_temp + np.random.normal(0, 1.5, time_points)

# Insert anomaly events
temp_profile[30:35] = 80  # Temperature drop anomaly
temp_profile[70:75] = 90  # Temperature rise anomaly

# Critical limits
CL_lower = 83  # Lower critical limit
CL_upper = 87  # Upper critical limit

# Anomaly detection
violations = (temp_profile < CL_lower) | (temp_profile > CL_upper)
violation_indices = np.where(violations)[0]

# Visualization
fig, ax = plt.subplots(figsize=(14, 6))

# Temperature plot
ax.plot(time, temp_profile, linewidth=2, color='#11998e', label='Measured Temperature')
ax.axhline(y=target_temp, color='green', linestyle='--', label='Target Temperature (85°C)', linewidth=2)
ax.axhline(y=CL_upper, color='red', linestyle='--', label='Upper Critical Limit (87°C)', linewidth=1.5)
ax.axhline(y=CL_lower, color='red', linestyle='--', label='Lower Critical Limit (83°C)', linewidth=1.5)

# Fill critical limit range
ax.fill_between(time, CL_lower, CL_upper, alpha=0.2, color='green', label='Critical Limit Range')

# Highlight anomalies
if len(violation_indices) > 0:
    ax.scatter(violation_indices, temp_profile[violation_indices],
               color='red', s=100, marker='x', linewidths=3,
               label=f'Anomalies Detected ({len(violation_indices)} cases)', zorder=5)

ax.set_xlabel('Time (minutes)', fontsize=12)
ax.set_ylabel('Temperature (°C)', fontsize=12)
ax.set_title('HACCP Temperature Monitoring System - Heat Sterilization Process', fontsize=14, fontweight='bold')
ax.legend(loc='upper right')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('haccp_monitoring.png', dpi=300, bbox_inches='tight')
plt.show()

# Anomaly report
print("=== HACCP Temperature Monitoring Report ===")
print(f"Monitoring Period: {time_points} minutes")
print(f"Target Temperature: {target_temp}°C")
print(f"Critical Limit Range: {CL_lower}-{CL_upper}°C")
print(f"Anomalies Detected: {len(violation_indices)} cases ({len(violation_indices)/time_points*100:.1f}%)")
print(f"Average Temperature: {temp_profile.mean():.2f}°C")
print(f"Temperature Variation (SD): {temp_profile.std():.2f}°C")

if len(violation_indices) > 0:
    print("\n=== Anomaly Times and Temperatures ===")
    for idx in violation_indices[:10]:  # Display first 10 cases
        status = "Low Temp" if temp_profile[idx] < CL_lower else "High Temp"
        print(f"  Time {idx} min: {temp_profile[idx]:.2f}°C ({status})")

# Requirements:
# - Python 3.9+
# - matplotlib>=3.7.0
# - seaborn>=0.12.0

"""
Example: Data acquisition in food processing has dramatically improve

Purpose: Demonstrate data visualization techniques
Target: Intermediate
Execution time: 2-5 seconds
Dependencies: None
"""

            <div class="code-header">📊 Code Example 4: Visualization of Multivariate Process Data</div>
            <pre><code class="language-python">import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Generate multivariate data for food manufacturing process
np.random.seed(42)
n_samples = 200

process_data = pd.DataFrame({
    'Temperature_C': np.random.normal(85, 3, n_samples),
    'Pressure_kPa': np.random.normal(150, 10, n_samples),
    'Flow_Rate_L/min': np.random.normal(50, 5, n_samples),
    'pH': np.random.normal(4.0, 0.3, n_samples),
    'Sugar_Brix': np.random.normal(12, 1.5, n_samples),
    'Quality_Score': np.random.normal(80, 10, n_samples)
})

# Correlation matrix
correlation_matrix = process_data.corr()

# Heatmap
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))

# Correlation matrix heatmap
sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='RdYlGn',
            center=0, ax=ax1, square=True, linewidths=1,
            cbar_kws={'label': 'Correlation Coefficient'})
ax1.set_title('Correlation Between Process Variables', fontsize=14, fontweight='bold')

# Pair plot (key 3 variables)
selected_cols = ['Temperature_C', 'Sugar_Brix', 'Quality_Score']
for i, col1 in enumerate(selected_cols):
    for j, col2 in enumerate(selected_cols):
        if i < j:
            ax2.scatter(process_data[col1], process_data[col2],
                       alpha=0.5, s=30, color='#11998e')
            ax2.set_xlabel(col1, fontsize=10)
            ax2.set_ylabel(col2, fontsize=10)

ax2.set_title('Relationships Among Key Process Variables', fontsize=14, fontweight='bold')
ax2.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('process_data_visualization.png', dpi=300, bbox_inches='tight')
plt.show()

print("=== Process Data Statistics ===")
print(process_data.describe())