Li Xin, Zhu Kai, Yan Bing
School of Chemical Science and Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.
Inorg Chem. 2025 Jul 14;64(27):13918-13927. doi: 10.1021/acs.inorgchem.5c01956. Epub 2025 Jun 27.
As crucial biomarkers of food spoilage, portable and real-time monitoring of the biogenic amines (BAs) is essential to ensuring food safety. In light of this, a ratiometric fluorescent probe (Tb@HOF-BPTC) is developed, which exhibits distinct fluorescence response upon exposure to BAs. It demonstrates exceptional analytical performance with a low limit of detection ranging from 3.1 to 14.3 μM and remarkably rapid response times (<7.02 s). Notably, the differential responses of three BAs to the Tb@HOF-BPTC construct individualized "portrait" that enhance identification accuracy. Moreover, a machine learning-intelligent sensing platform is constructed by integrating back-propagation neural networks (BPNN) with smartphone-based RGB recognition. This platform utilizes a smartphone camera to capture fluorescence images, which are then processed by a BPNN model to classify the concentrations and the species of the BAs. Furthermore, for the purpose of overcoming the inconvenience of detecting BAs vapors in conventional fluorescence sensing, poly(vinyl alcohol) (PVA) hydrogel microneedles combined with Tb@HOF-BPTC are designed for real-time detection of BAs. Overall, our work shows the potential of real-time food freshness assessment by constructing an intelligent sensing platform based on PVA hydrogel microneedles.
作为食品腐败的关键生物标志物，对生物胺（BAs）进行便携式实时监测对于确保食品安全至关重要。鉴于此，开发了一种比率荧光探针（Tb@HOF-BPTC），其在暴露于BAs时表现出明显的荧光响应。它具有出色的分析性能，检测限低至3.1至14.3 μM，响应时间极快（<7.02 s）。值得注意的是，三种BAs对Tb@HOF-BPTC构建体的差异响应形成了个性化的“画像”，提高了识别准确性。此外，通过将反向传播神经网络（BPNN）与基于智能手机的RGB识别相结合，构建了一个机器学习智能传感平台。该平台利用智能手机摄像头捕获荧光图像，然后由BPNN模型对BAs的浓度和种类进行分类。此外，为了克服传统荧光传感中检测BAs蒸汽的不便，设计了与Tb@HOF-BPTC相结合的聚乙烯醇（PVA）水凝胶微针用于BAs的实时检测。总体而言，我们的工作展示了通过构建基于PVA水凝胶微针的智能传感平台进行实时食品新鲜度评估的潜力。
