INTELLIGENT MILK QUALITY DASHBOARD
IoT + Mathematical Model + ML Predictor · Live Monitoring
System Status
OPERATIONAL
Vehicles Tracked
7
Active Alerts
2
Live Time
--:--:--
Avg Transport Temp 🌡️
3.71°C
All routes · Jan 1–6, 2025
Avg Microbial Load 🦠
25.0k CFU/mL
Initial load baseline N₀
Total Shipments 📦
875
Jan 2025 dataset
Spoilage Events
0
All batches clear
Avg Distance 📍
261 km
Across 7 cities
SELECTED CITY
Bengaluru
13.724°N, 77.645°E
TEMP
3.76°C
MICROBIAL
25.5k CFU/mL
DISTANCE
260 km
TRANSIT
9.97 hrs
🌡️ TEMPERATURE TREND — Jan 2025
🦠 MICROBIAL LOAD N(t) = N₀·e^(μt)
📍 CITY-WISE TEMPERATURE COMPARISON
🥛 MILK FRESHNESS & SHELF LIFE
FRESHNESS
--%
SHELF LIFE
-- hrs
📋 DATA LOGS — Shipment Records
Shipment IDCityTemperature Microbial LoadDistanceStatus
🧬 ML PREDICTOR — Q10 Microbial Growth Model
Enter sensor readings below. The model uses the Q10 temperature coefficient to compute microbial growth rate μ, then applies the exponential growth equation to predict final CFU/mL and freshness index.
Typical range: 1,000 – 150,000 CFU/mL
Cold chain optimal: 2 – 4°C
Enter time in minutes (e.g. 600 = 10 hrs)
Used in enhanced growth model (4.5–7.0)
🟢 FRESH
Growth Rate μ
/hr
Final N(t)
CFU/mL
Freshness
%
FRESHNESS INDEX 0%
SPOILED WARNING FRESH
MICROBIAL GROWTH CURVE — N(t) = N₀ · e^(μt)
⚡ WHAT-IF SCENARIOS (impact on current prediction)
Model: N(t) = N₀ · e^(μ · t)
Growth rate: μ = μ_ref · Q10^((T − T_ref) / 10)
📐 MODEL PARAMETERS
μ_ref (reference growth rate)
0.03 /hr at 4°C
Q10 (temperature coefficient)
2.0 (doubles per 10°C)
T_ref (reference temperature)
4°C
Spoilage threshold
> 100,000 CFU/mL
FRESHNESS ZONES
🟢 FRESH — Score ≥ 90% · N(t) < 20,000 CFU/mL
🟡 OPTIMAL — Score ≥ 70% · N(t) < 30,000 CFU/mL
🟠 WARNING — Score ≥ 30% · N(t) < 100,000 CFU/mL
🔴 SPOILED — Score < 30% · N(t) ≥ 100,000 CFU/mL