This review highlights the latest research on dietary fibre (DF) applications in meat and meat analogues, providing insights into their role in shaping future food innovations. DFs provide significant long-term health benefits, such as better gut health, lower cholesterol levels, and possible protection from metabolic diseases. They also enhance the texture, juiciness, and overall quality of plant-based meat alternatives (PMAs) and traditional meat products. Among the most effective fibres, cereal-derived fibres, fruit- and vegetable-derived fibres, and legume-based fibres have been shown to improve water-holding capacity (WHC) and emulsification properties, enhancing mouthfeel and juiciness. New processing methods, such as enzymatic hydrolysis and extrusion, can change how fibres work. By combining various fibre sources with innovative processing methods, the food industry can create meat and PMA products that are not only healthier but also tastier and more sustainable.

Food fraud in meat products presents serious economic and public health challenges, underscoring the need for rapid and reliable detection methods. This study investigates the potential of Raman spectroscopy combined with machine learning to accurately discriminate between pure and mixed minced meat preparations. We evaluated three classification algorithms: Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forests (RFs). Raman spectra were collected from 19 distinct samples consisting of different ratios of pork, beef, and lamb minced meat. Our findings suggest that homogenization markedly enhances spectral consistency and classification accuracy. In the pure meat samples case, all three models (SVM, ANN, and RF) achieved notable increases in classification accuracies (from 0.50–0.70 to above 0.85), a dramatic improvement over unhomogenized samples. In more complex homogenized mixtures, SVM delivered the highest performance, achieving an accuracy of up to 0.88 for 50:50 mixtures and 0.86 for multi-ratio samples, often outperforming both ANN and RF. While the underlying interpretation of the classification models remains complex, the findings consistently underscore the critical role of homogenization on model performance. This work demonstrates the robust potential of the Raman spectroscopy-coupled machine learning approach for the rapid and accurate identification of minced meat species.

This comprehensive literature review synthesizes findings from the latest empirical studies examining the effects of microwave thawing on meat quality and safety across multiple species (pork, beef, and poultry). Microwave thawing offers substantial advantages in processing efficiency, reducing thawing time by up to 100-fold compared to conventional refrigeration methods. However, the literature reveals complex, multifaceted, and sometimes contradictory effects on meat quality parameters. While optimized microwave protocols can maintain quality comparable to fresh meat, suboptimal conditions result in substantial deterioration including increased drip loss, protein denaturation, lipid oxidation, and structural damage to muscle tissue. The evidence demonstrates that microwave power level, endpoint temperature control, and hybrid approaches combining microwave with other technologies are critical determinants of quality outcomes. Emerging technologies including solid-state microwave systems and microwave-air convection combinations show promise in addressing non-uniform heating challenges. The field exhibits considerable disagreement regarding optimal protocols, with substantial variations in power levels, endpoint temperatures, and species-specific responses. Microbiological safety data, while limited, generally indicate that microwave thawing does not compromise food safety, despite the issues of temperature abuse and hot/cold spots, and may reduce microbial loads compared to slower methods. Significant research gaps remain in understanding industrial scalability, species-specific optimization, interactions between freezing and thawing protocols, and long-term effects on nutritional quality.