Kang Zhilong, Zhao Yuchen, Chen Lei, Guo Yanju, Mu Qingshuang, Wang Shenyi
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China.
School of Information Engineering, Tianjin University of Commerce, Tianjin, 300134 China.
Food Eng Rev. 2022;14(4):596-616. doi: 10.1007/s12393-022-09322-2. Epub 2022 Sep 6.
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
食品质量与安全是社会关注的重要热点问题。近年来，对实时食品信息的需求日益增长，无损检测正逐渐取代传统的手工感官检测和具有滞后性及破坏性的化学分析方法，在食品供应链中具有强大的应用潜力。随着计算机科学和光谱技术的成熟与发展，机器学习和高光谱成像（HSI）已被广泛证明是高效的检测技术，可用于无损且高效地快速评估食品的感官特性和质量属性。本文首先简要介绍了高光谱成像和机器学习的基本概念，包括高光谱成像的成像过程、机器学习中包含的算法类型以及数据处理流程。其次，基于2017年至2022年的最新文献，本文对机器学习和高光谱成像在食品供应链的分拣、包装、运输、储存和销售中的当前应用进行了客观全面的概述。最后，进一步探讨了该技术的潜力，为实际应用提供优化思路。
