food-ai-nexus/pasteurized-milk-spoilage-classifier

Milk Spoilage Classification Model

A Random Forest classifier for predicting milk spoilage type based on microbial count data.

Model Description

This model classifies milk samples into three spoilage categories based on Standard Plate Count (SPC) and Total Gram-Negative (TGN) bacterial counts measured at days 7, 14, and 21 of shelf life.

Classes

• PPC: Post-Pasteurization Contamination
• no spoilage: No spoilage detected
• spore spoilage: Spore-forming bacteria spoilage

Input Features

Feature	Description
SPC_D7	Standard Plate Count at Day 7 (log CFU/mL)
SPC_D14	Standard Plate Count at Day 14 (log CFU/mL)
SPC_D21	Standard Plate Count at Day 21 (log CFU/mL)
TGN_D7	Total Gram-Negative count at Day 7 (log CFU/mL)
TGN_D14	Total Gram-Negative count at Day 14 (log CFU/mL)
TGN_D21	Total Gram-Negative count at Day 21 (log CFU/mL)

Performance

• Test Accuracy: 95.76%

Usage

Using the Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/chenhaoq87/MilkSpoilageClassifier"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

# Input: [SPC_D7, SPC_D14, SPC_D21, TGN_D7, TGN_D14, TGN_D21]
payload = {"inputs": [[4.5, 5.2, 6.1, 3.2, 4.0, 4.8]]}

response = requests.post(API_URL, headers=headers, json=payload)
print(response.json())

Local Usage

import joblib
import numpy as np

# Load the model
model = joblib.load("model/model.joblib")

# Prepare input features
# [SPC_D7, SPC_D14, SPC_D21, TGN_D7, TGN_D14, TGN_D21]
features = np.array([[4.5, 5.2, 6.1, 3.2, 4.0, 4.8]])

# Make prediction
prediction = model.predict(features)
probabilities = model.predict_proba(features)

print(f"Predicted class: {prediction[0]}")
print(f"Class probabilities: {dict(zip(model.classes_, probabilities[0]))}")

Model Details

• Model Type: Random Forest Classifier
• Framework: scikit-learn
• Number of Estimators: 100
• Max Depth: None (unlimited)
• Min Samples Split: 5
• Min Samples Leaf: 1

Citation

If you use this model, please cite the original research on milk spoilage classification.

Repository Structure

MilkSpoilageClassifier/
├── apps/
│   ├── fastapi/        # REST API application
│   ├── gradio/         # Interactive web interface
│   └── huggingface/    # HF Inference handler
├── data/               # Training and test datasets
├── docs/               # Documentation files
├── model/              # Trained model artifacts
├── notebooks/          # Jupyter notebooks for analysis
├── scripts/            # Utility scripts
└── README.md           # This file

See individual README files in each directory for more details.

Quick Start

Train the Model

python scripts/prepare_model.py

Run Gradio Interface

cd apps/gradio
pip install -r requirements.txt
python app.py

Run FastAPI Server

cd apps/fastapi
pip install -r requirements.txt
python app.py

Deploy to Hugging Face

python scripts/upload_to_hf.py

License

MIT License