Ye Haichao, Liu Liqin, Shen Dagang, Song Chang, Wang Huanhuan
College of Chemistry and Chemical Engineering, Xinjiang Agricultural University, Urumqi 830002, China.
Sensors (Basel). 2024 Nov 21;24(23):7415. doi: 10.3390/s24237415.
BF, volatile amines (VOAs), and biogenic amines (BAs) are the key indicators in chemical reaction catalysis and food quality monitoring. In this study, we present two types of fluorescent sensors, a hydrazone ligand (HL)-based fluorescent sensor for BF detection and a novel sensor array using six boron difluoride (BF) hydrazone complexes (BFHs) for monitoring VOAs and BAs. Spectral research indicates that the interaction mechanism between the HLs and BF is based on intramolecular charge transfer (ICT). The HLs for the monitoring of BF showed good sensitivity, selectivity, and anti-interference and have the characteristics of a visible color change. Additionally, the HL probe demonstrates reversibility in the presence of triethylamine, making it a candidate for "ON-OFF-ON" mode sensing. BF detection can also be efficiently performed using test strips for convenient, air-based applications. The BFH sensor array successfully differentiates histamine from the other typical non-volatile BAs in solution; in comparison, the VOAs are analyzed through recognition patterns and statistical analysis. The array's color changes enable the practical, on-site detection of shrimp spoilage, with principal component analysis distinguishing various ageing intervals. In summary, this sensor array demonstrates high selectivity for VOAs and BAs, with significant potential for application in real-world sample analysis.
硼氟化物(BF)、挥发性胺(VOA)和生物胺(BA)是化学反应催化和食品质量监测中的关键指标。在本研究中,我们展示了两种类型的荧光传感器,一种用于检测BF的基于腙配体(HL)的荧光传感器,以及一种使用六种二氟化硼(BF)腙配合物(BFH)监测VOA和BA的新型传感器阵列。光谱研究表明,HL与BF之间的相互作用机制基于分子内电荷转移(ICT)。用于监测BF的HL表现出良好的灵敏度、选择性和抗干扰性,并具有可见颜色变化的特点。此外,HL探针在三乙胺存在下表现出可逆性,使其成为“开-关-开”模式传感的候选者。使用测试条也可以高效地进行BF检测,便于基于空气的应用。BFH传感器阵列成功地区分了溶液中组胺与其他典型的非挥发性BA;相比之下,通过识别模式和统计分析对VOA进行分析。阵列的颜色变化能够对虾的腐败进行实际的现场检测,主成分分析可区分不同的老化阶段。总之,该传感器阵列对VOA和BA表现出高选择性,在实际样品分析中具有巨大的应用潜力。