Zuo Jiewen, Peng Yankun, Li Yongyu, Chen Yahui, Yin Tianzhen
College of Engineering, China Agricultural University, Beijing 100083, China.
J Agric Food Chem. 2025 Jan 8;73(1):85-99. doi: 10.1021/acs.jafc.4c08680. Epub 2024 Dec 2.
Assessing the nutritional value of muscle food (MF) necessitates comprehensive component analysis. Traditional chemical analytical methods are often time-intensive, destructive, and environmentally detrimental, requiring specialized laboratory expertise. Hyperspectral imaging (HSI) emerges as an innovative technique that effectively integrates spectral and spatial information to enable rapid, nondestructive, and multidimensional predictions of nutritional parameters in MF. This Review examines the cutting-edge advancements in HSI technology, elucidating its novel technical and methodological dimensions. It systematically explores the principles and methodologies of HSI, presenting recent research and diverse applications in predicting MF nutritional parameters, and evaluates HSI's significant advantages and current limitations while addressing field-specific challenges and prospective research trends, ultimately positioning HSI as a potentially transformative tool in ensuring meat industry quality and safety.
评估肌肉食品（MF）的营养价值需要进行全面的成分分析。传统的化学分析方法通常耗时、具有破坏性且对环境有害，需要专业的实验室技术。高光谱成像（HSI）作为一种创新技术应运而生，它有效地整合了光谱和空间信息，能够对MF中的营养参数进行快速、无损和多维预测。本综述探讨了HSI技术的前沿进展，阐明了其新颖的技术和方法层面。系统地探究了HSI的原理和方法，介绍了在预测MF营养参数方面的最新研究和多样应用，并评估了HSI的显著优势和当前局限性，同时探讨了特定领域的挑战和未来研究趋势，最终将HSI定位为确保肉类行业质量和安全的潜在变革性工具。
