Ultrasound technology has emerged as a transformative tool in modern food science, offering non-destructive, real-time assessment and enhancement of food quality attributes. This review systematically explores the fundamental mechanisms by which ultrasound interacts with food matrices, including mechanical effects such as acoustic cavitation, localized shear forces, and microstreaming, as well as thermal and acoustic attenuation phenomena. Applications of ultrasound in food texture evaluation are discussed across multiple sectors, with particular emphasis on its role in assessing moisture distribution, fat content, structural integrity, and microstructural alterations in meat, dairy, fruits, and vegetables. The versatility of ultrasound—spanning low-intensity quality assessments to high-intensity processing interventions—makes it an invaluable technology for both quality control and product innovation. Moreover, emerging innovations such as ultrasound-assisted extraction, non-thermal pasteurization, and real-time quality monitoring are highlighted, demonstrating the synergy between ultrasound and advanced technologies like AI-driven data interpretation and portable, handheld sensing devices. Despite these advances, challenges related to technical limitations in heterogeneous food systems, high initial investment costs, scalability, and the absence of standardized protocols remain critical barriers to widespread adoption. The future directions emphasize the integration of ultrasound with multi-modal approaches, the development of miniaturized and cost-effective equipment, and the establishment of global regulatory standards to facilitate its broader application. Overall, ultrasound is positioned as a key enabler for sustainable, efficient, and non-invasive quality assurance across the global food industry.

The demand for safe, high-quality, and minimally processed food has intensified interest in non-destructive analytical techniques capable of assessing freshness and safety in real time. Among these, near-infrared (NIR) spectroscopy and biosensors have emerged as leading technologies due to their rapid, reagent-free, and sample-preserving nature. NIR spectroscopy offers a holistic assessment of internal compositional changes, while biosensors provide specific and sensitive detection of biological and chemical contaminants. Recent advances in miniaturization, chemometrics, and deep learning have further enhanced their potential for inline and point-of-need applications across diverse food matrices, including meat, seafood, eggs, fruits, and vegetables. This review critically evaluates the operational principles, instrumentation, and current applications of NIR spectroscopy and biosensors in food freshness and safety monitoring. It also explores their integration, highlights practical challenges such as calibration transfer and regulatory hurdles, and outlines emerging innovations including hybrid sensing, Artificial Intelligence (AI) integration, and smart packaging. The scope of this review is to provide a comprehensive understanding of these technologies, and its objective is to inform future research and industrial deployment strategies that support sustainable, real-time food quality control. These techniques enable near real-time monitoring under laboratory and pilot-scale conditions, showing strong potential for industrial adaptation. The nature of these targets often determines the choice of transduction method.

Frozen seafood plays an important role in the global food supply, but maintaining its quality during frozen storage and cold-chain distribution remains a significant challenge. Although freezing effectively slows microbial growth and enzymatic activity, it cannot completely prevent quality deterioration. During frozen storage, seafood undergoes a series of interconnected physicochemical changes, including ice crystal growth, protein denaturation and oxidation, lipid oxidation, water redistribution, and texture deterioration. These changes gradually reduce sensory quality, nutritional value, and overall commercial acceptability. Conventional quality assessment methods, including destructive laboratory analyses and sensory evaluation, are still widely used. However, they are often labor-intensive, time-consuming, and unsuitable for rapid or real-time monitoring in modern cold-chain systems. As a result, increasing attention has been given to non-destructive sensing technologies that can evaluate seafood quality quickly and objectively. This review summarizes the major mechanisms responsible for quality deterioration in frozen seafood, together with recent advances in sensing technologies used to monitor these changes. The sensing approaches discussed include near-infrared (NIR) and Raman spectroscopy, hyperspectral and fluorescence imaging, low-field nuclear magnetic resonance (LF-NMR), electronic nose (E-nose), electronic tongue (E-tongue), colorimetric sensor arrays (CSAs), and biosensors. This review also discusses the growing role of artificial intelligence in frozen seafood quality assessment, including chemometrics, machine learning, deep learning, and multi-sensor data fusion. Particular attention is given to their applications in quality prediction, industrial implementation, and decision support. Finally, current challenges and future research needs are highlighted, with emphasis on the development of interpretable, transferable, and real-time monitoring systems that can support more reliable quality assurance throughout the frozen seafood supply chain.