Xie Chuanqi, Zhou Weidong
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, The Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
Foods. 2023 Jun 5;12(11):2266. doi: 10.3390/foods12112266.
Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.
传统的食品危害检测方法耗时、低效且具有破坏性。光谱成像技术已被证明在检测食品危害方面能够克服这些缺点。与传统方法相比，光谱成像还可以提高检测的通量和频率。本研究综述了用于检测食品中生物、化学和物理危害的技术，包括紫外可见近红外（UV-Vis-NIR）光谱、太赫兹（THz）光谱、高光谱成像和拉曼光谱。讨论并比较了这些技术的优缺点。还总结了有关用于检测食品危害的机器学习算法的最新研究。可以发现，光谱成像技术在食品危害检测中很有用。因此，本综述提供了有关食品行业可使用的光谱成像技术的最新信息，并为进一步研究奠定了基础。
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