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Arko007/walnut-rancidity-predictor

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Walnut Rancidity Predictor

Production-grade Stacked LSTM + Attention model for predicting walnut rancidity probability and remaining shelf life from storage condition time-series.

Trained on 90,000 synthetic sequences simulating Indian storage environments using Arrhenius-based lipid oxidation kinetics.

Model Architecture

Input (30 days × 8 features)
  → LSTM layer 1  (hidden=64, dropout=0.2)
  → LSTM layer 2  (hidden=64, dropout=0.2)
  → LSTM layer 3  (hidden=64)
  → Attention (soft weighted context)
  → Dropout (0.2)
  ┌──────────────────────────────────┐
  │  Head 1: rancidity_probability   │  Linear → ReLU → Linear → Sigmoid
  │  Head 2: shelf_life_remaining    │  Linear → ReLU → Linear
  │  Head 3: decay_curve_value       │  Linear → ReLU → Linear → Sigmoid
  └──────────────────────────────────┘

Property	Value
Parameters	~85 K
Input features	8
Sequence length	30 days
LSTM layers	3
Hidden size	64
Dropout	0.2
Epochs trained	12 / 20
Best val loss	0.3892

Training Data

—Dataset: Arko007/walnut-rancidity-predictor
—90,000 sequences · 5.4 M rows
—4 Indian storage scenarios (cold warehouse, hill region, ambient, hot transport)

Input Features

Feature	Unit	Description
`temperature`	°C	Ambient temperature (2–40 °C)
`humidity`	% RH	Relative humidity (30–85 %)
`moisture`	%	Walnut moisture content (3–8 %)
`oxygen`	fraction	Oxygen exposure (0.18–0.23)
`peroxide_value`	meq/kg	Primary oxidation marker
`free_fatty_acids`	%	FFA content
`hexanal_level`	ppm	Volatile oxidation byproduct
`oxidation_index`	—	Composite oxidation score

Outputs

Output	Type	Description
`rancidity_probability`	[0, 1]	Probability walnuts are rancid
`shelf_life_remaining_days`	float	Days until PV > 5 meq/kg
`decay_curve_value`	[0, 1]	Normalised peroxide value

Rancidity Threshold

Walnuts are rancid when Peroxide Value > 5 meq/kg (FSSAI / Codex standard).

rancidity_probability = sigmoid(PV − 5)

Quick Start

python

from huggingface_hub import hf_hub_download
import torch, joblib, numpy as np

# Download artifacts
model_path  = hf_hub_download("Arko007/walnut-rancidity-predictor", "models/walnut_rancidity_lstm_attention.pt")
scaler_path = hf_hub_download("Arko007/walnut-rancidity-predictor", "models/feature_scaler.pkl")

# Or clone and use the inference helper directly:
# from model.predict import predict_storage_risk

# Build a 30-day sequence: [temp, humidity, moisture, oxygen, PV, FFA, hexanal, ox_index]
seq = np.column_stack([
    np.full(30, 5.0),             # cold storage
    np.full(30, 50.0),
    np.full(30, 4.0),
    np.full(30, 0.20),
    np.linspace(0.5, 1.2, 30),
    np.linspace(0.05, 0.10, 30),
    np.linspace(0.1,  0.3,  30),
    np.linspace(0.2,  0.5,  30),
])

result = predict_storage_risk(seq)
# → {'rancidity_probability': 0.0312, 'shelf_life_remaining_days': 143.7, 'risk_level': 'LOW'}

Risk Classification

`risk_level`	`rancidity_probability`
`LOW`	< 0.30
`MEDIUM`	0.30 – 0.70
`HIGH`	> 0.70

Chemistry Model

k(T)   = A · exp(−Ea / (R·T))    A=1.5×10¹², Ea=80 kJ/mol, R=8.314 J/mol·K
PV(t)  = PV₀ · exp(k · t)

Humidity and moisture accelerate oxidation via empirical correction factors.

Files

models/
  walnut_rancidity_lstm_attention.pt  ← PyTorch checkpoint (epoch 12)
  feature_scaler.pkl                  ← StandardScaler for inference
  metrics.json                        ← Training metrics
model/
  predict.py                          ← Inference API
train.py                              ← Training script (resume from checkpoint)
generate_data.py                      ← Synthetic data generator
requirements.txt

Resume Training

bash

pip install -r requirements.txt
python train.py          # resumes from saved checkpoint automatically

License

MIT