Ultrafast Ratiometric Fluorescent Probe and Deep Learning-Assisted On-Site Detection Platform for BAs and Meat Freshness Based on Molecular Engineering
Xin Miao, Yilin Jiang, Wenjing Liu, Chen Lu, Wenjia Tan, Feng Li, Ming Zhang
Abstract
As metabolic byproducts and representative indicators of food spoilage, the monitoring and detection for biogenic amines (BAs) are crucial but challenging for food quality assessment. Here, a strategy is proposed by combining fluorescent probe molecular engineering with a portable detection platform integrating a smartphone and a deep convolutional neural network (DCNN). Four ratiometric fluorescent probes with tunable intramolecular charge transfer (ICT) properties are designed by introducing different electron-withdrawing substituents (−F, −OCH 3, −Py, and −CN) to the carbazole. Notably, CNCz exhibits the strongest ICT property and superior sensing performance, with a satisfying detection limit (11 ppb), rapid response (<5 s), and discriminative bathochromic shift (110 nm). Then, a smartphone-based detection platform is fabricated, which enables rapid, visual, and on-site quantitative evaluation of BAs. Furthermore, by integrating DCNN, this platform achieves an impressive 98.5% accuracy in predicting meat freshness. Hereby, this study not only provides a molecular engineering strategy to fine-tune the intrinsic ICT properties to gain high-performance ratiometric fluorescent probes but also presents an intelligent detection platform for BAs and meat freshness with high practical applicability.