Khoya (khoa or mawa) is a traditional dairy product, prepared by heating and concentrating milk, which is widely used in preparation of indigenous milk sweets. But, challenges such as process variability, quality deterioration, microbial contamination, adulteration, limited shelf life and inefficient supply chain management hinder its production and distribution. New solutions to these challenges are available across the khoya value chain due to recent advancements in artificial intelligence (AI) and Industry 4.0 technologies. This review highlights the applications of AI in khoya processing, packaging, transportation, distribution and quality management. The role of machine learning, deep learning, computer vision, Internet of Things (IoT), digital twins, smart sensors, and blockchain in process optimization, automated quality inspection, adulteration detection, shelf-life prediction, intelligent packaging, cold-chain monitoring, logistics optimization and demand forecasting is explored. We also review AI-enabled analytical tools for rapid and non-destructive quality assessment, such as hyperspectral imaging, electronic nose, and electronic tongue. The review also discusses the contribution of AI to improving food safety, traceability, sustainability and operational efficiency, as well as to reducing post-harvest losses and environmental impacts. Finally, the paper discusses the existing challenges, future research directions, and prospects of AI-enabled smart dairy manufacturing. The review finds that AI can play a significant role in improving the quality, safety, efficiency, and sustainability of the khoya industry and helping its transition to intelligent and data-driven dairy processing.
Meeting the global demand for fresh, minimally processed food requires us to rethink how we monitor food safety. Traditional laboratory methods are often too slow, labor-intensive, and impractical for real-time applications. To overcome these delays, biosensors have emerged as a rapid, highly sensitive, and cost-effective alternative. This study explores how biosensing technology accurately detects pathogens, chemical contaminants like heavy metals and pesticides, and spoilage indicators across dairy, meat, produce, and packaged foods. What makes these tools truly transformative is their seamless integration with modern digital infrastructure. By combining biosensors with the Internet of Things (IoT), artificial intelligence (AI), nanotechnology, edge computing, and blockchain, we can create intelligent, continuous monitoring systems. These interconnected frameworks allow for real-time, farm-to-fork traceability, enabling early hazard detection, extending shelf life, and significantly reducing food waste through data-driven decisions. Despite this immense potential, bringing smart biosensors to the commercial market involves overcoming distinct practical hurdles. We examine current technical barriers, including biofouling, long-term sensor stability, power management, and high manufacturing costs. More importantly, we highlight the emerging innovations actively solving these bottlenecks, such as biodegradable materials, battery-free platforms, advanced printed electronics, and smart packaging technologies.