Abstract
The growing global demand and price fluctuations in meat have raised concerns over safety, adulteration, and traceability. Conventional methods are time-consuming, labor-intensive, and reagent-dependent, limiting their use for rapid or on-site screening. This review provides a comprehensive overview of emerging non-invasive techniques-such as fluorescence, near-infrared, mid-infrared, and Raman spectroscopy-for assessing meat quality and detecting adulteration. The key novelty of this review is its integration of bibliometric analysis with a critical evaluation of advanced technologies aligned with the UN Sustainable Development Goals. The review also highlights the potential of hybrid systems that integrate spectroscopy with chemometrics and machine learning to provide accurate, real-time, and sustainable meat authentication solutions. It also highlights research gaps such as the need for multi-adulterant detection models, standardized validation protocols, and open-access spectral databases. By aligning innovation with regulatory and sustainability frameworks, this review advocates for robust, scalable solutions to build future-ready meat supply chains.