Artificial Intelligence-Driven Dairy Quality Assessment: From Advanced Sensing Technologies to Explainable Intelligence

Artificial intelligence (AI) is transforming dairy quality assessment by enabling rapid, non-destructive, and data-driven monitoring across the dairy supply chain. Conventional analytical methods, although accurate, are often labor-intensive, time-consuming, and unsuitable for real-time applications. The integration of advanced sensing technologies with AI has therefore emerged as a promising approach for improving dairy quality control and food safety. This review examines recent advances in AI-enabled dairy quality assessment, covering spectroscopy-based sensing technologies, biomimetic sensing systems, machine vision, and multimodal data fusion. Particular attention is given to their applications in major dairy products, including raw milk, milk powder, cheese, and yogurt. Comparative analysis shows that AI-assisted sensing significantly enhances the accuracy, efficiency, and automation of compositional analysis, adulteration detection, microbial screening, freshness evaluation, and defect inspection, while multimodal approaches offer new opportunities for comprehensive quality assessment. The review further highlights the emerging role of explainable artificial intelligence (XAI) in improving the transparency and trustworthiness of dairy detection systems. Recent advances in feature attribution, decision interpretation, and uncertainty quantification are discussed, together with challenges related to data scarcity, model generalization, cross-instrument transferability, and explainability evaluation. Future developments in multimodal foundation models, standardized datasets, and explainable intelligent systems are expected to accelerate the deployment of trustworthy and scalable dairy quality assurance frameworks.