Kenya Coffee School (KCS)

Version 1.0 – Advanced Roasting Certification Engine

Aligned with structured roast standards referenced by the Specialty Coffee Association and optimized for Kenyan high-density coffees (820–850 g/L).

1️⃣ PURPOSE

The AI Roast Correction Engine (RCE) is designed to:

• Detect roast curve anomalies in real-time or post-roast
• Predict underdevelopment, overdevelopment, or baking
• Recommend corrective gas/airflow adjustments
• Improve DTR accuracy (target 20–23%)
• Stabilize RoR pattern
• Reduce defect frequency
• Increase certification pass probability

2️⃣ SYSTEM INPUT VARIABLES

2.1 Green Coffee Variables

• Density (g/L)
• Moisture %
• Water activity (optional)
• Screen size
• Processing type (Washed, Honey, Natural)

2.2 Roast Process Variables

• Batch size (kg)
• Charge temperature
• Turning point time & temp
• Bean temperature curve (per second/minute)
• RoR curve
• Gas % per time segment
• Airflow level per time segment
• First crack time & temp
• Drop time & temp
• Total roast time
• Development time

2.3 Sensory Validation (Post-Roast)

• Aroma score
• Acidity score
• Sweetness score
• Balance score
• Defect notes

3️⃣ CORE ANALYTICAL MODULES

🔥 MODULE 1: DTR OPTIMIZATION ENGINE

Formula

DTR = \frac{Development Time}{Total Roast Time} \times 100

Logic Rules

IF DTR < 18%
→ Flag: Underdevelopment risk
→ Suggest: +15–30 sec development

IF DTR > 25%
→ Flag: Overdevelopment risk
→ Suggest: Reduce drop temp 2–4°C

IF Density > 840 g/L AND DTR < 20%
→ Suggest extended Maillard (+20 sec before first crack)

📈 MODULE 2: RoR STABILITY ANALYZER

Detection Logic

A. RoR Crash

Condition: RoR drop >5°C/min within 30 sec

Correction:

• Reduce airflow at mid-Maillard
• Maintain gas slightly longer

B. RoR Flick

Condition: RoR spike >4°C/min after first crack

Correction:

• Reduce gas 10–15% at FC onset
• Increase airflow slightly

C. Baking Pattern

Condition: RoR flat <3°C/min for >90 sec

Correction:

• Increase gas early in Maillard
• Shorten total roast time

D. Scorching Risk

Condition: High charge temp + steep early RoR (>20°C/min)

Correction:

• Reduce charge temp by 3–5°C
• Increase drum speed or airflow

4️⃣ DENSITY-BASED PREDICTIVE MODEL

Input: Density (g/L)

IF Density 820–830
→ Base charge profile

IF Density 830–840
→ +2°C charge
→ Extend Maillard 15 sec

IF Density 840–850
→ +3–5°C charge
→ Controlled early gas
→ Maintain airflow balance

5️⃣ SHRINKAGE OPTIMIZATION MODULE

Formula

Shrinkage\% = \frac{Green - Roasted}{Green} \times 100

Target: 14.5–16.5%

IF Shrinkage < 13%
→ Likely underdeveloped

IF Shrinkage > 18%
→ Overdevelopment risk

Algorithm correlates shrinkage with DTR and drop temp.

6️⃣ SENSORY CORRELATION MODEL

The system uses weighted regression mapping:

Machine learning refinement:

• Collect historical roast + sensory data
• Train predictive model to suggest:
  • “Increase development 20 sec for improved sweetness”
  • “Reduce drop temp 2°C to protect acidity”

7️⃣ AI RECOMMENDATION OUTPUT FORMAT

After roast upload, user receives:

📊 Technical Report

• DTR status: 19.2% (Below Target)
• RoR Flick detected at 8:45
• Drop temp 205°C (+3°C above tolerance)

🤖 AI Suggestions

• Reduce gas 12% at 1st crack
• Extend development +18 sec
• Reduce charge by 2°C
• Increase airflow at minute 6

🎯 Predicted Outcome

• Improved sweetness +0.8 sensory points
• Reduced bitterness probability
• DTR adjusted to 21.5%

8️⃣ CERTIFICATION INTEGRATION

The AI engine contributes to:

• 15% RoR Stability score
• 10% DTR Accuracy score
• 20% Profile Accuracy score

System tracks:

• Improvement rate per student
• Consistency index
• Pass probability indicator

9️⃣ MACHINE LEARNING ARCHITECTURE

Phase 1 – Rule-Based Engine

• Deterministic if/then corrections
• Density-driven adjustments
• Static tolerance bands

Phase 2 – Supervised Learning

• Train on KCS roast database
• Regression models:
  • XGBoost
  • Random Forest
• Predict sensory outcome from roast data

Phase 3 – Reinforcement Learning

• System tests micro-adjustments
• Learns optimal roast strategy per density cluster

🔟 ANOMALY SCORING SYSTEM

Score range: 0–100

≥85 = Professional level
≥92 = Master Level Calibration

11️⃣ EDGE CASE HANDLING

• Extremely low density (<780 g/L)
• Very small batches (<1kg)
• Dark roast intentional profiles
• High altitude roastery variations

Algorithm adjusts tolerance bands accordingly.

12️⃣ PERFORMANCE TARGETS

• Real-time anomaly detection <500ms
• Post-roast analysis <3 seconds
• 95% anomaly detection accuracy (target)
• ≥10% improvement in average DTR accuracy within 3 months

13️⃣ DATA STORAGE STRUCTURE

Each roast stores:

• Full temperature time series
• RoR time series
• Correction recommendations
• Instructor override
• Sensory validation feedback

This feeds continuous ML model refinement.

14️⃣ FUTURE EVOLUTION

• IoT live roaster sensor integration
• Auto-gas modulation suggestions
• Roast fingerprint marketplace
• Green lot predictive modeling
• Africa-wide roast benchmark index

STRATEGIC IMPACT

The AI Roast Correction Engine:

• Elevates KCS certification rigor
• Reduces training time
• Improves production consistency
• Strengthens Kenya’s roast-origin leadership
• Builds continental roast intelligence database