Shi Shijie, Zhang Kaidi, Tian Nina, Jin Zhaoqiang, Liu Ke, Huang Liying, Tian Xiaohai, Cao Cougui, Zhang Yunbo, Jiang Yang
College of Agriculture, Yangtze University, Hubei, China; MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River, Yangtze University, China.
College of Plant Science & Technology, Huazhong Agricultural University, Hubei, China.
Food Res Int. 2025 Jun;211:116459. doi: 10.1016/j.foodres.2025.116459. Epub 2025 Apr 20.
Food adulteration is an important threat to food safety and can be difficult to detect. Some analytical methods are complex and difficult to meet the needs of large numbers of samples. In this study, we introduced the application of six spectroscopic techniques (NIR, FTIR, HSI, Raman, UV-Vis, and FS) and chemometric methods in common food adulteration (powdered food, meat, honey, drink, edible oil, and dairy product) over the last three years. We introduced the consequences of food adulteration, the principles, advantages, and limitations of spectroscopic techniques, spectral data preprocessing and key wavelength selection methods, chemometrics methods, dataset division methods, and evaluation methods for models. Moreover, it provided a perspective for the future application of spectroscopic techniques in food adulteration. The results showed that linear chemometric methods were still the main method used by many researchers, which may limit the application potential of spectroscopic techniques. Therefore, deep learning-based chemometrics methods and their interpretability should be further explored in food adulteration. Secondly, data fusion and ensemble models based on multiple spectroscopic techniques and chemometrics can further improve the accuracy of the models. Future research should select appropriate spectroscopic techniques based on food type and spectroscopic principles, and consider portable technical solutions wherever possible to improve the application scenarios of spectroscopic techniques.
