A systematic study of conventional, sensor-induced, and machine learning-based methods for milk quality analysis and...

Area of Science:

• Food Science and Technology
• Sensor Technology
• Machine Learning Applications

Background:

• Milk quality is globally compromised by adulteration, contamination, and poor storage.
• Current milk quality testing is centralized, lab-based, slow, and costly.
• There is a critical need for portable, accurate, and rapid milk assessment solutions.

Purpose of the Study:

• To analyze recent advancements in sensor-based milk testing.
• To integrate Machine Learning (ML) for milk quality classification.
• To identify a reliable, low-cost, portable, real-time milk quality testing solution.

Main Methods:

• Review of sensor-based techniques for milk analysis.
• Application of Machine Learning algorithms for data classification.
• Exploration of Internet of Things (IoT) integration for real-time data.

Main Results:

• Sensor-infused ML solutions outperform traditional lab methods.
• Portable, real-time milk testing kits are becoming feasible.
• ML provides robust backend support for sensor-based testing.

Conclusions:

• Sensor and ML integration offers a superior alternative to conventional testing.
• IoT and sensor technology enable development of portable milk testing kits.
• This technology has significant potential for food safety and rural empowerment.