AI-Driven Approaches for Milk Adulteration Detection: A Comprehensive Review

Milk adulteration is a critical issue affecting public health, necessitating the development of accurate and efficient detection techniques. This study presents a detailed review and comparative analysis of recent advancements in deep learning and machine learning approaches for detecting milk adulteration. The research examines algorithms such as CNNs, LSTM, Artificial Neural Networks, Gated Recurrent Units (GRUs), and Hybrid Blended Deep Learning Models (HyBDL) which integrate CNN, CNN-LSTM, and CNN-GRU. Furthermore, machine learning models such as XGBoost, RandomForestAlgorithm(RF), K-Nearest Neighbors (KNN), and CatBoost are analyzed in conjunction with hyperspectral imaging, PCAM-ResNet50, and MALDI-MS-based methods. The comparative study highlights that CNN and hybrid deep learning architectures achieve high accuracy in image-based adulteration detection, while machine learning models effectively analyze tabular data derived from spectroscopy and sensor-based techniques. Raman spectroscopy, although not AI-based, is essential for detecting microplastics in dairy products. The findings indicate that combining techniques in deep learning and machine learning. can enhance adulteration detection accuracy, offering a scalable and robust solution for ensuring dairy quality and consumer safety. Future research should focus on integrating AI with portable sensor-based solutions for real-time monitoring of milk adulteration.