Saleem Asima, Imtiaz Aysha, Yaqoob Sanabil, Awais Muhammad, Awan Kanza Aziz, Naveed Hiba, Khalifa Ibrahim, Ercisli Sezai, Mugabi Robert, Alotaibi Saqer S, Nayik Gulzar Ahmad, Qian Jian-Ya, Shen Qing
School of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China.
Panvascular Diseases Research Center, The Quzhou Affiliaed Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000,China.
Food Chem X. 2025 Jul 10;29:102778. doi: 10.1016/j.fochx.2025.102778. eCollection 2025 Jul.
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.
全球对肉类需求的不断增长以及价格波动引发了人们对肉类安全、掺假和可追溯性的担忧。传统方法耗时、 labor-intensive且依赖试剂，限制了它们在快速或现场筛查中的应用。本综述全面概述了用于评估肉质和检测掺假的新兴非侵入性技术，如荧光、近红外、中红外和拉曼光谱。本综述的关键新颖之处在于将文献计量分析与对符合联合国可持续发展目标的先进技术的批判性评估相结合。该综述还强调了将光谱学与化学计量学和机器学习相结合的混合系统在提供准确、实时和可持续肉类认证解决方案方面的潜力。它还突出了一些研究空白，如对多种掺假物检测模型的需求、标准化验证协议和开放获取光谱数据库。通过使创新与监管和可持续性框架保持一致，本综述倡导采用强大、可扩展的解决方案来构建面向未来的肉类供应链。
