Zhang Shikun, Lin Xianfeng, Gu Zepeng, Kang Lixin, Zhang Yingming, Sun Jun, Duan Nuo, Wang Zhouping, Deng Ruijie, Wu Shijia
State Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
iScience. 2025 Jul 28;28(9):113212. doi: 10.1016/j.isci.2025.113212. eCollection 2025 Sep 19.
Acrylamide and advanced glycation end products are two typical hazards produced simultaneously through the Maillard reaction of food. Traditional instrumental detection methods require specialized instrument operation, resulting in low detection efficiency. Here, a red-green dual fluorescence detection system combined with Nt.BbvCI-assisted cyclic rolling circle amplification (CRCA) technology was designed to reduce the limit of detection to the pg/mL level. Furthermore, CRCA is encapsulated in emulsion droplets for the reaction. Especially when the target concentration is low, the fluorescence is concentrated in a small number of droplets, making the fluorescence signal easier to detect and greatly improving the detection sensitivity. The fluorescence imaging pictures were further analyzed by machine learning, and the analysis time was reduced from 3 min/picture to 1.4 s/picture, improving the analysis efficiency and accuracy, and the detection limit was lowered to 10 fg/mL, which ultimately achieved the simultaneous detection of Maillard hazards in food with high sensitivity.
丙烯酰胺和晚期糖基化终产物是通过食物美拉德反应同时产生的两种典型危害物质。传统的仪器检测方法需要专门的仪器操作，导致检测效率较低。在此，设计了一种结合Nt.BbvCI辅助循环滚环扩增（CRCA）技术的红绿双荧光检测系统，将检测限降低到pg/mL水平。此外，CRCA被封装在乳液滴中进行反应。特别是当目标浓度较低时，荧光集中在少数液滴中，使荧光信号更易于检测，大大提高了检测灵敏度。通过机器学习对荧光成像图片进行进一步分析，分析时间从每张图片3分钟缩短至1.4秒，提高了分析效率和准确性，检测限降低到10 fg/mL，最终实现了对食品中美拉德危害物质的高灵敏度同时检测。
