Potentiality of Synchronous Fluorescence Technology for Identification of Pork Adulteration in Beef
LI Yue1, 2, 3, LIN Yi-li4, ZHOU Yun-yun1, 2, 3, YANG Xin-ting2, 3, WANG Zeng-li1*, LIU Huan2, 3*
1. Department of Food Nutrition and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
2. Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
3. National Engineering Research Center for Agri-Product Quality and Safety Traceability，Beijing 100097, China
4. Pinggu Garden Management Committee of Zhongguancun Science and Technology Park, Beijing 101200, China
Abstract：Beef is an important edible meat in China. In recent years, with the increasing demand for beef, the phenomenon of pork being impersonated or added to beef for sale has become increasingly serious, and there is an urgent need for simple and rapid detection methods to monitor adulteration behavior. This study analyzed the three-dimensional fluorescence spectra of beef and pork to determine the wavelength difference of synchronous fluorescence. A synchronous fluorescence spectrum with a fixed wavelength difference of 160 nm was used to qualitatively distinguish and quantitatively analyze the doping of beef with pork. The discriminant accuracy of the test set, verification set, and prediction set samples are taken as the evaluation index of the qualitative Discriminative model: Correlation coefficient (r), corrected Root-mean-square deviation (RMSEC) and predicted Root-mean-square deviation (RMSEP) were used as the evaluation indicators of the quantitative analysis model. The experimental results show a significant difference in the three-dimensional fluorescence spectra between beef and pork. Beef has fluorescence peaks at Ex/Em values of 270/320, 330/400, 350/500, 430/515 and 410/570 nm, while pork has three fluorescence peaks at Ex/Em values of 270/320, 330/400 and 430/515 nm. By setting the synchronous fluorescence wavelength difference to 160 nm, three fluorescence peaks of beef can be collected, with two of them located at the peak. The correction set accuracy of the SVM qualitative Discriminative model for beef, pork, and adulterated meat was 97.56%, and the prediction accuracy was 92.31%. The PLS prediction model for pork addition in beef without treatment, MSC treatment, and SNV treatment was compared. The PLS model without treatment was the best, with rc, rp, RMSEC, and REMSP reaching 0.978 6, 0.959 0, 0.059 7, and 0.092 7, respectively. Therefore, the qualitative discrimination and quantitative analysis detection model for beef adulterated pork based on synchronous fluorescence technology combined with SVM and PLS has a high recognition rate and detection accuracy, which can accurately and quickly detect whether pork is adulterated in beef.
李 月，林义利，周云云，杨信廷，王增利，刘 欢. 基于同步荧光技术的牛肉中掺杂猪肉鉴别方法研究[J]. 光谱学与光谱分析, 2024, 44(10): 2968-2972.
LI Yue, LIN Yi-li, ZHOU Yun-yun, YANG Xin-ting, WANG Zeng-li, LIU Huan. Potentiality of Synchronous Fluorescence Technology for Identification of Pork Adulteration in Beef. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2968-2972.
[1] ZHANG Ying-ying, ZHAO Wen-tao, LI Hui-chen, et al(张颖颖，赵文涛，李慧晨，等). Modern Food Science and Technology(现代食品科技), 2017, 33(2): 230.
[2] SHAO Bo-yu, XU Ning, CHI Kai-yue, et al(邵博宇，徐 宁，迟凯月，等). Food and Fermentation Industry(食品与发酵工业), 2022, 48(24): 281.
[3] Jiang Xingyi, Rao Qinchun, Kristen Mittl, et al. Food Control, 2020, 110: 107045.
[4] XU Wen-juan, HAN Fang, ZHAO Han, et al(许文娟，韩 芳，赵 晗，等). Meat Industry(肉类工业), 2021，(10): 33.
[5] SHI Zi-he, Josef VOGLMEIR, LIU Li, et al(施姿鹤，Josef VOGLMEIR，刘 丽，等). Food Science(食品科学), 2019, 40(23): 319.
[6] Petter Vejle Andersen, Jens Petter Wold, Eli Gjerlaug-Enger, et al. Meat Science, 2018, 145: 94.
[7] Zhuang Qibin, Peng Yankun, Yang Deyong, et al. Journal of Food Composition and Analysis, 2023, 118: 105175.
[8] Liu Huan, Ji Zengtao, Liu Xinliang, et al. Food Chemistry, 2020, 321: 126628.
[9] Patra D, Mishra A K. Talanta, 2001, 53(4): 783.
[10] ZHANG Li-hua, XIANG Qi-sen, LI Shun-feng, et al(张丽华，相启森，李顺峰，等). Journal of Northwest A&F University (Natural Science Edition)[西北农林科技大学学报（自然科学版）], 2016, 44(12): 201.
[11] Chiang L H, Russell E L, Braatz R D. Chemometrics and Intelligent Laboratory Systems, 2000, 50: 243.
[12] HE Li-fang, LIN Dan-li, LI Yao-qun, et al(何立芳, 林丹丽, 李耀群，等). Progress in Chemistry(化学进展), 2004, 16(6): 879.
[13] XU Jin-gou, WANG Zun-ben(徐金钩，王尊本). Fluorescence Analysis Method (3rd Edition)[荧光分析法(第3版)]. Beijing：Science Press(北京：科学出版社), 2006. 155.
[14] Liu Huan, Zhu Wenying, Zhang Ning, et al. Food Control, 2023, 152: 109881.
